wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show that strategies in which the trader is observing the signal a number of times during the trading period, can dramatically reduce the transaction costs and improve the performance of the optimal static strategy . These results answer a question which was raised by Brigo and Piat 6%DIFDELCMD < ]%%% , by analyzing two cases where adaptive strategies can improve the performance of the execution .
Revised:  We show that strategies in which the trader is observing the signal a number of times during the trading period, can dramatically reduce the transaction costs and improve the performance of the optimal static strategy 6%DIFDELCMD < ]%%% , by analyzing two cases where adaptive strategies can improve the performance of the execution .
Edit intent: coherence
Original:  We uncover positive sorting for single men and negative sorting for married women that accounts for a substantial fraction of the gender wage gap at the top of the distribution.
Revised:  After controlling for endogenous employment selection, we still find substantial gender wage gap -- ranging from 21\% to 40\% throughout the (latent) offered wage distribution that is not explained by observable labor market characteristics. We also uncover positive sorting for single men and negative sorting for married women that accounts for a substantial fraction of the gender wage gap at the top of the distribution.
Edit intent: meaning-changed
Original:  We solve for the unique subgame perfect equilibrium of this game, and establish properties of the equilibrium strategies and payoffs in the limit. 
Revised:  We solve for the unique subgame perfect equilibrium of this game, and establish properties of the equilibrium strategies and payoffs in the limit. The solution and comparative statics thereof provide insight about intertemporal choice with status concerns. In particular we find that patient players take fewer risks.
Edit intent: meaning-changed
Original:  In each periodeach player chooses any probability distribution with support on 0, 1%DIFDELCMD < ] %%% with a given mean, where the mean is the realized value of the draw from the previous period.
Revised:  In each periodeach player chooses any probability distribution with support on %DIFDELCMD < ] %%% with a given mean, where the mean is the realized value of the draw from the previous period.
Edit intent: meaning-changed
Original:  In each periodeach player chooses any probability distribution with support on 0, 1%DIFDELCMD < ] %%% with a given mean, where the mean is the realized value of the draw from the previous period. The player with the highest realization in the period achieves a payoff of 1, and the other player, 0; and each player seeks to maximize the discounted sum of his per-period payoffs over the whole time horizon .
Revised:  In each periodeach player chooses any probability distribution with support on 0, 1%DIFDELCMD < ] %%% Each period, each seller provides information via a Blackwell experiment to a single short-lived buyer, who buys from the seller whose product has the highest expected quality .
Edit intent: meaning-changed
Original:  We solve for the unique subgame perfect equilibrium of this game, and establish properties of the equilibrium strategies and payoffs in the limit. The solution and comparative staticsthereof provide insight about intertemporal choice with status concerns. In particular we find that patient players take fewer risks .
Revised:  We solve for the unique subgame perfect equilibrium of this game, and conduct comparative statics: in particular we find that patient players take fewer risks .
Edit intent: clarity
Original:  We developed "de novo" a versatile analysis pipeline to  quantify the spatio-temporal propagation of the SWs in the frequency band 0.5, 4%DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Revised:  We developed a versatile analysis pipeline to quantify the spatio-temporal propagation of the SWs in the frequency band 0.5, 4%DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Edit intent: clarity
Original:  We developed "de novo" a versatile analysis pipeline to  quantify the spatio-temporal propagation of the SWs in the frequency band 0.5, 4%DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Revised:  We developed "de novo" a versatile analysis pipeline to identify and quantify the spatio-temporal propagation of the SWs in the frequency band 0.5, 4%DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Edit intent: meaning-changed
Original:  We developed "de novo" a versatile analysis pipeline to  quantify the spatio-temporal propagation of the SWs in the frequency band 0.5, 4%DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Revised:  We developed "de novo" a versatile analysis pipeline to quantify the spatio-temporal propagation of the SWs 0.5, 4%DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Edit intent: clarity
Original:  We developed "de novo" a versatile analysis pipeline to  quantify the spatio-temporal propagation of the SWs in the frequency band 0.5, 4%DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Revised:  We developed "de novo" a versatile analysis pipeline to quantify the spatio-temporal propagation of the SWs in the frequency band %DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Edit intent: meaning-changed
Original:  We developed "de novo" a versatile analysis pipeline to  quantify the spatio-temporal propagation of the SWs in the frequency band 0.5, 4%DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Revised:  We developed "de novo" a versatile analysis pipeline to quantify the spatio-temporal propagation of the SWs in the frequency band 0.5, 4%DIFDELCMD < ] %%% . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Edit intent: clarity
Original:  We developed "de novo" a versatile analysis pipeline to  quantify the spatio-temporal propagation of the SWs in the frequency band 0.5, 4%DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neural activity, the response of fluorescence proteins and the slow waves dynamics.
Revised:  We developed "de novo" a versatile analysis pipeline to quantify the spatio-temporal propagation of the SWs in the frequency band 0.5, 4%DIFDELCMD < ] %%% Hz . Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neuronal activity, the response of fluorescence proteins and the slow waves dynamics.
Edit intent: fluency
Original:  The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo  . Comparison of preliminary experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA).
Revised:  The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo , thus enabling a calibration tool for the analysis pipeline . Comparison of preliminary experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA).
Edit intent: meaning-changed
Original:  The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo  . Comparison of preliminary experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA).
Revised:  The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo . Comparison of experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA).
Edit intent: clarity
Original:  Results showed that the regional coreness - i.e., the probability of a region to be in the multiplex core - was mainly driven by DWI structural connectivity with significant contributions of fMRI and MEG functional connectivity, respectively at 0.1-0.3 Hz and 8-10 Hz.
Revised:  Globally, the regional coreness - i.e., the probability of a region to be in the multiplex core - was mainly driven by DWI structural connectivity with significant contributions of fMRI and MEG functional connectivity, respectively at 0.1-0.3 Hz and 8-10 Hz.
Edit intent: clarity
Original:  Results showed that the regional coreness - i.e., the probability of a region to be in the multiplex core - was mainly driven by DWI structural connectivity with significant contributions of fMRI and MEG functional connectivity, respectively at 0.1-0.3 Hz and 8-10 Hz. At the global scale, we showed that the coreness significantly decreased in AD patients as a result of the randomization process initiated by the neurodegeneration.
Revised:  Results showed that the regional coreness - i.e., the probability of a region to be in the multiplex core - significantly decreased in AD patients as a result of the randomization process initiated by the neurodegeneration.
Edit intent: coherence
Original:  At the local scale , the most impacted areas were in the core of the network - including temporal, parietal and occipital areas - while we reported compensatory increments for the peripheral regions in the sensorimotor system.
Revised:  Locally , the most impacted areas were in the core of the network - including temporal, parietal and occipital areas - while we reported compensatory increments for the peripheral regions in the sensorimotor system.
Edit intent: clarity
Original:  This dynamic method enables tracking the evolution of all initial stocks, external inputs, and arbitrary intercompartmental flows as well as the associated storages derived from these inputs, flows and stocks individually and separately within the system.
Revised:  The dynamic method enables tracking the evolution of all initial stocks, external inputs, and arbitrary intercompartmental flows as well as the associated storages derived from these inputs, flows and stocks individually and separately within the system.
Edit intent: fluency
Original:  This dynamic method enables tracking the evolution of all initial stocks, external inputs, and arbitrary intercompartmental flows as well as the associated storages derived from these inputs, flows and stocks individually and separately within the system.
Revised:  This dynamic method enables tracking the evolution of all initial stocks, external inputs, and arbitrary intercompartmental flows as well as the associated storages derived from these stocks, inputs, and flows individually and separately within the system.
Edit intent: fluency
Original:  Multiple system flows and associated storages transmitted  from one compartment directly or indirectly to any other or along a given flow path are then analytically characterized, systematically classified, and mathematically formulated.
Revised:  The transient and the dynamic direct, indirect, acyclic, cycling, and transfer (diact) flows and associated storages transmitted from one compartment directly or indirectly to any other or along a given flow path are then analytically characterized, systematically classified, and mathematically formulated.
Edit intent: meaning-changed
Original:  Multiple system flows and associated storages transmitted  from one compartment directly or indirectly to any other or along a given flow path are then analytically characterized, systematically classified, and mathematically formulated.
Revised:  Multiple system flows and associated storages transmitted along a given flow path or from one compartment directly or indirectly to any other or along a given flow path are then analytically characterized, systematically classified, and mathematically formulated.
Edit intent: fluency
Original:  The transient and the dynamic direct, indirect, acyclic, cycling, and transfer (diact) flows and associated storages transmitted along a given flow path or from one compartment directly or indirectly to any other are then analytically characterized, systematically classified, and mathematically formulated.
Revised:  The transient and the dynamic direct, indirect, acyclic, cycling, and transfer (diact) flows and associated storages transmitted along a given flow path or from one compartment--directly or indirectly--to any other are then analytically characterized, systematically classified, and mathematically formulated.
Edit intent: fluency
Original:  Here, we propose a biologically realistic and unifying approach to emergent pattern formation. Our biphasic model of multicellular tissues incorporates turnover and transport of morphogens controlling cell differentiation and tissue mechanics in a single framework , where one tissue phase consists of a poroelastic network made of cells and the other is the extracellular fluidpermeating between cells .
Revised:  Here, we propose a minimal model combining tissue mechanics to morphogen turnover and transport of morphogens controlling cell differentiation and tissue mechanics in a single framework , where one tissue phase consists of a poroelastic network made of cells and the other is the extracellular fluidpermeating between cells .
Edit intent: clarity
Original:  Our biphasic model of multicellular tissues incorporates turnover and transport of morphogens controlling cell differentiation and tissue mechanics in a single framework , where one tissue phase consists of a poroelastic network made of cells and the other is the extracellular fluidpermeating between cells .
Revised:  Our biphasic model of multicellular tissues incorporates turnover and transport in order to explore new routes to patterning. Our active description couples morphogen reaction-diffusion, which impact on cell differentiation and tissue mechanics in a single framework , where one tissue phase consists of a poroelastic network made of cells and the other is the extracellular fluidpermeating between cells .
Edit intent: meaning-changed
Original:  Our biphasic model of multicellular tissues incorporates turnover and transport of morphogens controlling cell differentiation and tissue mechanics in a single framework , where one tissue phase consists of a poroelastic network made of cells and the other is the extracellular fluidpermeating between cells .
Revised:  Our biphasic model of multicellular tissues incorporates turnover and transport of morphogens controlling cell differentiation and tissue mechanics , to a two-phase poroelastic rheology , where one tissue phase consists of a poroelastic network made of cells and the other is the extracellular fluidpermeating between cells .
Edit intent: meaning-changed
Original:  Our biphasic model of multicellular tissues incorporates turnover and transport of morphogens controlling cell differentiation and tissue mechanics in a single framework , where one tissue phase consists of a poroelastic network made of cells and the other is the extracellular fluidpermeating between cells .
Revised:  Our biphasic model of multicellular tissues incorporates turnover and transport of morphogens controlling cell differentiation and tissue mechanics in a single framework , where one tissue phase consists of a poroelastic cell network and the other is the extracellular fluidpermeating between cells .
Edit intent: clarity
Original:  Our biphasic model of multicellular tissues incorporates turnover and transport of morphogens controlling cell differentiation and tissue mechanics in a single framework , where one tissue phase consists of a poroelastic network made of cells and the other is the extracellular fluidpermeating between cells .
Revised:  Our biphasic model of multicellular tissues incorporates turnover and transport of morphogens controlling cell differentiation and tissue mechanics in a single framework , where one tissue phase consists of a poroelastic network made of cells and the other of a permeating extracellular fluid, which provides a feedback by actively transporting morphogens .
Edit intent: meaning-changed
Original:  Moreover, we unravel a qualitatively different advection-driven  instability which allows for the formation of patterns with a single morphogen and which single mode pattern scales with tissue size.
Revised:  Moreover, we unravel a qualitatively different advection-driven Keller-Segel instability which allows for the formation of patterns with a single morphogen and which single mode pattern scales with tissue size.
Edit intent: meaning-changed
Original: The use of deep learning for protein residue-residue contact prediction has not extended to template-free model generation, with current methods using predicted contacts to enhance existing approaches.
Revised: New algorithms for exploiting amino acid covariation have had a large impact on de novo protein modelling. The limited applicability of these methods to smaller protein families has limited their use for structural annotation of whole genomes, however. Most recently, deep learning techniques have shown promise in allowing residue-residue contact prediction has not extended to template-free model generation, with current methods using predicted contacts to enhance existing approaches.
Edit intent: meaning-changed
Original:  Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, that predicts inter-atomic distances and uses these to build models . Unsatisfied constraints are removed in an iterative process . DMPfold produces more accurate models than two popular methods for the CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, which uses deep learning throughout to predict inter-atomic distances and uses these to build models . Unsatisfied constraints are removed in an iterative process . DMPfold produces more accurate models than two popular methods for the CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Edit intent: meaning-changed
Original:  Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, that predicts inter-atomic distances and uses these to build models . Unsatisfied constraints are removed in an iterative process . DMPfold produces more accurate models than two popular methods for the CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, that predicts inter-atomic distance bounds, the main chain hydrogen bond network, and main chain torsion angles and uses these to build models . Unsatisfied constraints are removed in an iterative process . DMPfold produces more accurate models than two popular methods for the CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Edit intent: meaning-changed
Original:  Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, that predicts inter-atomic distances and uses these to build models . Unsatisfied constraints are removed in an iterative process . DMPfold produces more accurate models than two popular methods for the CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, that predicts inter-atomic distances and uses these to build models in an iterative process . DMPfold produces more accurate models than two popular methods for the CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Edit intent: clarity
Original:  Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, that predicts inter-atomic distances and uses these to build models . Unsatisfied constraints are removed in an iterative process . DMPfold produces more accurate models than two popular methods for the CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, that predicts inter-atomic distances and uses these to build models . Unsatisfied constraints are removed in an iterative procedure . DMPfold produces more accurate models than two popular methods for the CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Edit intent: clarity
Original:  Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, that predicts inter-atomic distances and uses these to build models . Unsatisfied constraints are removed in an iterative process . DMPfold produces more accurate models than two popular methods for the CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Revised:  Here we introduce DMPfold, a development of the DeepMetaPSICOV contact predictor, that predicts inter-atomic distances and uses these to build models . Unsatisfied constraints are removed in an iterative process . DMPfold produces more accurate models than two popular methods for a test set of CASP12 free modelling domains, and is able to generate high quality models without any modifications for a set of transmembrane proteins.
Edit intent: clarity
Original:  It shows particularly good performance when generating a single model, making it a computationally tractable way of generating models from availablesequence data .
Revised:  We apply it to all Pfam domains without a known structure and provide high confidence models for 25\% of these so-called "dark" families, a calculation that took less than a week on a small cluster with 200 available cores. DMPfold provides models for 16\% of human proteome UniProt entries without structures, can generate accurate models with alignments of fewer than 100 sequences in some cases, and is freely available .
Edit intent: meaning-changed