wanyu/IteraTeR_v2 huggingface dataset:
Original:  Allowing also for targeted deletion and ` parametrizing' the space of fitnesses with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: one where the ` fit-get-richer' (FGR) and one where the ` winner-takes-all' (WTA).
Revised:  Allowing also for targeted deletion and ' parametrizing' the space of fitnesses with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: one where the ` fit-get-richer' (FGR) and one where the ` winner-takes-all' (WTA).
Edit intent: fluency
Original:  Allowing also for targeted deletion and ` parametrizing' the space of fitnesses with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: one where the ` fit-get-richer' (FGR) and one where the ` winner-takes-all' (WTA).
Revised:  Allowing also for targeted deletion and ` parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: one where the ` fit-get-richer' (FGR) and one where the ` winner-takes-all' (WTA).
Edit intent: fluency
Original:  Allowing also for targeted deletion and ` parametrizing' the space of fitnesses with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: one where the ` fit-get-richer' (FGR) and one where the ` winner-takes-all' (WTA).
Revised:  Allowing also for targeted deletion and ` parametrizing' the space of fitnesses with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: the ' fit-get-richer' (FGR) and one where the ` winner-takes-all' (WTA).
Edit intent: fluency
Original:  Allowing also for targeted deletion and ` parametrizing' the space of fitnesses with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: one where the ` fit-get-richer' (FGR) and one where the ` winner-takes-all' (WTA).
Revised:  Allowing also for targeted deletion and ` parametrizing' the space of fitnesses with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: one where the ` fit-get-richer' (FGR) and the ' winner-takes-all' (WTA).
Edit intent: fluency
Original:  By tracking the system's four parameters through maximum likelihood, we can measure in real-time its distance from the  WTA regime.
Revised:  By calibrating the system's four parameters through maximum likelihood, we can measure in real-time its distance from the WTA regime.
Edit intent: clarity
Original:  By tracking the system's four parameters through maximum likelihood, we can measure in real-time its distance from the  WTA regime.
Revised:  By tracking the system's four parameters with maximum likelihood, we can measure in real-time its distance from the WTA regime.
Edit intent: clarity
Original:  This is demonstrated by applying the theory to the eToro  trading platform, where traders can mimic each other, thus giving rise to systematic instabilities if one or a few traders become too popular (WTA regime).
Revised:  This is demonstrated by applying the theory to the eToro trading platform, where traders can imitate each other, thus giving rise to systematic instabilities if one or a few traders become too popular (WTA regime).
Edit intent: fluency
Original:  Finally, we show how to effectively and cost-efficiently prevent the formation of WTA agents through transformation of the fitness distribution along a geodesic path  .
Revised:  Finally, we show how to effectively and cost-efficiently prevent the formation of WTA agents through an optimal transformation along a geodesic path .
Edit intent: clarity
Original:  Instead, it is the weakest agents that need to be supported, to generate an overall more balanced fitness landscape and thus a healthier, more stable system .
Revised:  Instead, we demonstrate that interventions that jump-start low-fitness individuals to transform them into high fitness agents is the best way to avoid swelling inequality and escalating fragility .
Edit intent: coherence
Original: We develop an early-warning system for the formation of disproportional dominance in complex systems.
Revised: We develop an optimal, cost-minimizing intervention policy to avoid the formation of disproportional dominance in complex systems.
Edit intent: clarity
Original:  Assuming that the growth dynamics is captured by proportional growth , we describe the system with fitness-adjusted preferential attachment , where the idiosyncratic system dynamicsis conveniently absorbed into the fitness term .
Revised:  Accounting for generic proportional growth dynamics , we describe the system with fitness-adjusted preferential attachment , where the idiosyncratic system dynamicsis conveniently absorbed into the fitness term .
Edit intent: clarity
Original:  Assuming that the growth dynamics is captured by proportional growth , we describe the system with fitness-adjusted preferential attachment , where the idiosyncratic system dynamicsis conveniently absorbed into the fitness term .
Revised:  Assuming that the growth dynamics is captured by proportional growth , we describe the system with fitness-adjusted preferential attachment . The fitness term allows us to represent a large variety of specific system dynamics, making the framework broadly applicable .
Edit intent: clarity
Original:  Allowing also for targeted deletion and ' parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: the ' fit-get-richer' (FGR) and the ' winner-takes-all' (WTA).
Revised:  Allowing also for targeted deletion and ` parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: the ' fit-get-richer' (FGR) and the ' winner-takes-all' (WTA).
Edit intent: fluency
Original:  Allowing also for targeted deletion and ' parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: the ' fit-get-richer' (FGR) and the ' winner-takes-all' (WTA).
Revised:  Allowing also for targeted deletion and ' parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: the ` fit-get-richer' (FGR) and the ' winner-takes-all' (WTA).
Edit intent: fluency
Original:  Allowing also for targeted deletion and ' parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: the ' fit-get-richer' (FGR) and the ' winner-takes-all' (WTA).
Revised:  Allowing also for targeted deletion and ' parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: the ' fit-get-richer' (FGR) and the ` winner-takes-all' (WTA).
Edit intent: fluency
Original:  By calibrating the system's four parameters with maximum likelihood, we can measure in real-time its distance from the unfavorable WTA regime.
Revised:  By calibrating the system's four parameters with maximum likelihood, we can measure in real-time the system's distance from the unfavorable WTA regime.
Edit intent: fluency
Original:  The common counter-measure of stopping the most dominant agents from growing further does not solve sustainably the problem of drastic inequality .
Revised:  The common measure of penalising the most dominant agents from growing further does not solve sustainably the problem of drastic inequality .
Edit intent: clarity
Original:  The common counter-measure of stopping the most dominant agents from growing further does not solve sustainably the problem of drastic inequality .
Revised:  The common counter-measure of stopping the most dominant agents from growing further does not solve sustainably the problem of drastic inequity .
Edit intent: fluency
Original:  Instead, we demonstrate that interventions that jump-start low-fitness individuals to transform them into high fitness agents is the best way to avoid swelling inequality and escalating fragility.
Revised:  Instead, we demonstrate that interventions that jump-start low-fitness individuals upward is the best way to avoid swelling inequality and escalating fragility.
Edit intent: clarity
Original:  Instead, we demonstrate that interventions that jump-start low-fitness individuals to transform them into high fitness agents is the best way to avoid swelling inequality and escalating fragility.
Revised:  Instead, we demonstrate that interventions that jump-start low-fitness individuals to transform them into high fitness agents is the best way to avoid swelling inequity and escalating fragility.
Edit intent: fluency
Original: We develop an optimal, cost-minimizing intervention policy to avoid the formation of disproportional dominance (`winner-takes-all') in growing complex systems.
Revised: We develop an early warning system and subsequent optimal intervention policy to avoid the formation of disproportional dominance (`winner-takes-all') in growing complex systems.
Edit intent: clarity
Original:  Allowing also for targeted deletion and `parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the  system into two regimes:  the `fit-get-richer' (FGR) and  the `winner-takes-all' (WTA).
Revised:  Allowing also for targeted deletion and `parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the growing system into two regimes: the `fit-get-richer' (FGR) and the `winner-takes-all' (WTA).
Edit intent: clarity
Original:  Allowing also for targeted deletion and `parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the  system into two regimes:  the `fit-get-richer' (FGR) and  the `winner-takes-all' (WTA).
Revised:  Allowing also for targeted deletion and `parametrizing' the space of fitness distributions with the Beta-distribution, we derive an exact 4-dimensional phase diagram that separates the system into two regimes: one where the `fit-get-richer' (FGR) and the `winner-takes-all' (WTA).
Edit intent: clarity
Original:  By calibrating the system's four parameters with maximum likelihood, we can measure in real-time the system's distance from the unfavorable WTA regime  .
Revised:  By calibrating the system's parameters with maximum likelihood, we can measure in real-time the system's distance from the unfavorable WTA regime .
Edit intent: clarity
Original:  By calibrating the system's four parameters with maximum likelihood, we can measure in real-time the system's distance from the unfavorable WTA regime  .
Revised:  By calibrating the system's four parameters with maximum likelihood, its distance from the unfavorable WTA regime .
Edit intent: clarity
Original:  We show how to effectively and cost-efficiently prevent the formation of WTA agents through an optimal transformation along a geodesic path in the space of the fitness distributions.
Revised:  We show how to efficiently control the system back into a more stable state along a geodesic path in the space of the fitness distributions.
Edit intent: clarity
Original:  We show how to effectively and cost-efficiently prevent the formation of WTA agents through an optimal transformation along a geodesic path in the space of the fitness distributions.
Revised:  We show how to effectively and cost-efficiently prevent the formation of WTA agents through an optimal transformation along a geodesic path in the space of fitness distributions.
Edit intent: clarity
Original:  The common measure of penalising the most dominant agents and/or helping the lowest fit agents does not solve sustainably the problem of drastic inequity.
Revised:  It turns out that the common measure of penalising the most dominant agents and/or helping the lowest fit agents does not solve sustainably the problem of drastic inequity.
Edit intent: coherence
Original:  The common measure of penalising the most dominant agents and/or helping the lowest fit agents does not solve sustainably the problem of drastic inequity.
Revised:  The common measure of penalizing the most dominant agents and/or helping the lowest fit agents does not solve sustainably the problem of drastic inequity.
Edit intent: fluency
Original:  The common measure of penalising the most dominant agents and/or helping the lowest fit agents does not solve sustainably the problem of drastic inequity.
Revised:  The common measure of penalising the most dominant agents does not solve sustainably the problem of drastic inequity.
Edit intent: coherence
Original:  Instead, we demonstrate that interventions that first create a critical mass of high-fitness individuals followed by pushing low-fitness individuals upward is the best way to avoid swelling inequity and escalating fragility.
Revised:  Instead, interventions that first create a critical mass of high-fitness individuals followed by pushing low-fitness individuals upward is the best way to avoid swelling inequity and escalating fragility.
Edit intent: coherence
Original: We develop an early warning system and subsequent optimal intervention policy to avoid the formation of disproportional dominance (`winner-takes-all') in growing complex systems .
Revised: We develop an early warning system and subsequent optimal intervention policy to avoid the formation of disproportional dominance (`winner-takes-all') in growing complex networks .
Edit intent: clarity
Original:  By calibrating the system's parameters with maximum likelihood, its distance from the unfavorable WTA regime can be monitored in real time.
Revised:  By calibrating the system's parameters with maximum likelihood, its distance from the WTA regime can be monitored in real time.
Edit intent: clarity
Original:  Instead, interventions that first create a critical mass of high-fitness individuals followed by pushing  low-fitness individuals upward is the best way to avoid swelling inequity and escalating fragility.
Revised:  Instead, interventions that first create a critical mass of high-fitness individuals followed by pushing the relatively low-fitness individuals upward is the best way to avoid swelling inequity and escalating fragility.
Edit intent: clarity
Original:  The state of links inform about the probability of spreading the disease from infected to susceptible individuals.
Revised:  The state of links inform about the probability of carrying the contagion of the disease from infected to susceptible individuals.
Edit intent: clarity
Original:  These states depend on the full structure of the network, and its determination is not straightforward from the knowledge of nodes' states.
Revised:  Note that these states depend on the full structure of the network, and its determination is not straightforward from the knowledge of nodes' states.
Edit intent: coherence
Original:  In particular, we wonder about its computation in discrete-time epidemics, which is the approach used in basically all large-scale simulation of epidemics at the world-scale.
Edit intent: coherence
Original:  Moreover, our approach allows new schemes for the contention of epidemics based on link deactivation that outperform previous approaches .
Revised:  Epidemic containment based on links' deactivation promises to be an efficient tool to maintain functionality on networks while controlling the spread of diseases, as for example in air transportation networks .
Edit intent: clarity
Original:  Here, we confront this challenge and propose a set of discrete-time governing equations that can be closed and analyzed, assessing the contribution of links to spreading processes in complex networks.
Revised:  Here, we confront this challenge and propose a set of discrete-time governing equations can be closed and analyzed, assessing the contribution of links to spreading processes in complex networks.
Edit intent: fluency
Original:  Our approach allows an scheme for the contention of epidemics, based on deactivating the most important links in transmitting the disease , which outperforms previous proposals .
Revised:  Our approach allows a scheme for the contention of epidemics, based on deactivating the most important links in transmitting the disease , which outperforms previous proposals .
Edit intent: fluency
Original:  Our approach allows an scheme for the contention of epidemics, based on deactivating the most important links in transmitting the disease , which outperforms previous proposals .
Revised:  Our approach allows an scheme for the of epidemics, based on deactivating the most important links in transmitting the disease , which outperforms previous proposals .
Edit intent: clarity
Original:  Our approach allows an scheme for the contention of epidemics, based on deactivating the most important links in transmitting the disease , which outperforms previous proposals .
Revised:  Our approach allows an scheme for the contention of epidemics, based on deactivating the most important links in transmitting the disease .
Edit intent: clarity
Original:  Epidemic containment based on links' deactivation promises to be an efficient tool to maintain functionality on networks while controlling the spread of diseases, as for example in air transportation networks.
Revised:  Epidemic containment based on links' deactivation promises to be an effective tool to maintain functionality on networks while controlling the spread of diseases, as for example in air transportation networks.
Edit intent: clarity
Original:  A specific combination of skip connection and sequence-to-sequence autoencoder was able to generate most close prediction from the ground truth image.
Revised:  A specific combination of skip connection and sequence-to-sequence autoencoder was able to generate closest prediction from the ground truth image.
Edit intent: clarity
Original:  However, since these latent factors are largely uninterpreted,  predictions made using them are opaque.
Revised:  Since these latent factors are largely uninterpreted, predictions made using them are opaque.
Edit intent: coherence
Original:  However, since these latent factors are largely uninterpreted,  predictions made using them are opaque.
Revised:  However, since these latent factors are largely uninterpreted, however, predictions made using them are opaque.
Edit intent: coherence
Original:  In this paper , we develop LFI for collaborative filtering based recommender systems , which are particularly challenging from an interpretation perspective .
Revised:  In this paper we develop LFI for collaborative filtering based recommender systems , which are particularly challenging from an interpretation perspective .
Edit intent: fluency
Original:  In this paper , we develop LFI for collaborative filtering based recommender systems , which are particularly challenging from an interpretation perspective .
Revised:  In this paper , we develop LFI for collaborative filtering based recommender systems .
Edit intent: coherence
Original:  We illustrate the use of LFI interpretations on the MovieLens dataset demonstrating that latent factors can be predicted with enough accuracy for accurately replicating the predictions of the true model .
Revised:  We illustrate the use of LFI interpretations on the MovieLens dataset demonstrating that latent factors can be predicted with sufficient accuracy for replicating the predictions of the true model .
Edit intent: clarity
Original: There is a cognitive limit in Humans .
Revised: There is a cognitive limit in Human Mind .
Edit intent: clarity
Original:  This cognitive limit has played a decisive role in almost all fields including Computer Sciences .
Revised:  This cognitive limit has played a decisive role in almost all fields including computer sciences .
Edit intent: fluency
Original:  If certain conditions are met then computational complexity starts decreasing , even sometime it does not appears at all.
Revised:  The complexity starts decreasing if certain conditions are met then computational complexity starts decreasing , even sometime it does not appears at all.
Edit intent: clarity
Original:  If certain conditions are met then computational complexity starts decreasing , even sometime it does not appears at all.
Revised:  If certain conditions are met , even sometime it does not appears at all.
Edit intent: clarity
Original:  Very simple mechanical computing systems are designed and implemented to demonstrate this idea and it is further supported by Electrical Systems .
Revised:  Very simple Mechanical computing systems are designed and implemented to demonstrate this idea and it is further supported by Electrical Systems .
Edit intent: fluency
Original:  Very simple mechanical computing systems are designed and implemented to demonstrate this idea and it is further supported by Electrical Systems .
Revised:  Very simple mechanical computing systems are designed and implemented to demonstrate this idea and it is further supported by Electrical systems .
Edit intent: fluency
Original:  Some important predictions are made based on this new insight  .
Revised:  Some important predictions are made based on this new insight some important predictions are made .
Edit intent: clarity
Original: This paper is gives a short introduction into HomotopyContinuation.jl, a polynomial equations solver for Julia 1%DIFDELCMD < ]%%% . It present excerpts from the comprehensive documentation available at URL The aim of this project is twofold: establishing a fast numerical polynomial solver in Julia and at the same time providing a highly customizable algorithmic environment for researchers for designing and performing individual experiments.
Revised: This paper is gives a short introduction into HomotopyContinuation.jl, 1%DIFDELCMD < ]%%% . It present excerpts from the comprehensive documentation available at URL The aim of this project is twofold: establishing a fast numerical polynomial solver in Julia and at the same time providing a highly customizable algorithmic environment for researchers for designing and performing individual experiments.
Edit intent: clarity
Original: This paper is gives a short introduction into HomotopyContinuation.jl, a polynomial equations solver for Julia 1%DIFDELCMD < ]%%% . It present excerpts from the comprehensive documentation available at URL The aim of this project is twofold: establishing a fast numerical polynomial solver in Julia and at the same time providing a highly customizable algorithmic environment for researchers for designing and performing individual experiments.
Revised: This paper is gives a short introduction into HomotopyContinuation.jl, a polynomial equations solver for Julia %DIFDELCMD < ]%%% . It present excerpts from the comprehensive documentation available at URL The aim of this project is twofold: establishing a fast numerical polynomial solver in Julia and at the same time providing a highly customizable algorithmic environment for researchers for designing and performing individual experiments.
Edit intent: coherence
Original: This paper is gives a short introduction into HomotopyContinuation.jl, a polynomial equations solver for Julia 1%DIFDELCMD < ]%%% . It present excerpts from the comprehensive documentation available at URL The aim of this project is twofold: establishing a fast numerical polynomial solver in Julia and at the same time providing a highly customizable algorithmic environment for researchers for designing and performing individual experiments.
Revised: This paper is gives a short introduction into HomotopyContinuation.jl, a polynomial equations solver for Julia 1%DIFDELCMD < ]%%% 
Edit intent: coherence
Original:  1%DIFDELCMD < ] %%% J. Bezanson, A. Edelman, S.Karpinski and V.
Revised:  %DIFDELCMD < ] %%% J. Bezanson, A. Edelman, S.Karpinski and V.
Edit intent: fluency