wanyu/IteraTeR_v2 huggingface dataset:
Original:  Here , we consider the dispersal of biological species in a fluid environment, where mixing is achieved by a combination of advection and diffusion.
Revised:  Here we consider the dispersal of biological species in a fluid environment, where mixing is achieved by a combination of advection and diffusion.
Edit intent: fluency
Original:  In particular, we perform a detailed numerical analysis of a model composed of turbulent advection, diffusive transport, and cyclic interactions among biological species in two spatial dimensions , and discuss the circumstances under which biodiversity is maintained when external environmental conditions, such as resource supply, are uniform in space.
Revised:  In particular, we perform a detailed numerical analysis of a model composed of turbulent advection, diffusive transport, and cyclic interactions among biological species in two spatial dimensions and discuss the circumstances under which biodiversity is maintained when external environmental conditions, such as resource supply, are uniform in space.
Edit intent: fluency
Original:  It is shown that the global ( space-averaged ) dynamics undergoes bifurcations as the relative strengths of advection and diffusion compared to biological interactions are varied.
Revised:  It is shown that the space-averaged ) dynamics undergoes bifurcations as the relative strengths of advection and diffusion compared to biological interactions are varied.
Edit intent: clarity
Original:  It is shown that the global ( space-averaged ) dynamics undergoes bifurcations as the relative strengths of advection and diffusion compared to biological interactions are varied.
Revised:  It is shown that the global ( space-averaged dynamics undergoes bifurcations as the relative strengths of advection and diffusion compared to biological interactions are varied.
Edit intent: fluency
Original: Neural correlations  are central to study brain information processing and computation , and yet they have been barely analyzed due to the limited availability of simultaneous recordings .
Revised: Neural correlations during a cognitive task are central to study brain information processing and computation , and yet they have been barely analyzed due to the limited availability of simultaneous recordings .
Edit intent: clarity
Original: Neural correlations  are central to study brain information processing and computation , and yet they have been barely analyzed due to the limited availability of simultaneous recordings .
Revised: Neural correlations are central to study brain information processing and computation . However, they have been barely analyzed due to the limited availability of simultaneous recordings .
Edit intent: coherence
Original: Neural correlations  are central to study brain information processing and computation , and yet they have been barely analyzed due to the limited availability of simultaneous recordings .
Revised: Neural correlations are central to study brain information processing and computation , and yet they have been poorly analyzed due to the limited availability of simultaneous recordings .
Edit intent: clarity
Original: Neural correlations  are central to study brain information processing and computation , and yet they have been barely analyzed due to the limited availability of simultaneous recordings .
Revised: Neural correlations are central to study brain information processing and computation , and yet they have been barely analyzed due to the difficulty of recording simultaneous single neurons during task performance .
Edit intent: clarity
Original:  In the present work we analyzed neural correlations using spike-train recordings that were simultaneously recorded in sensory, premotor, and motor areas of a monkey during a somatosensory discrimination task.
Revised:  In the present work , we quantified neural directional correlations using spike trains that were simultaneously recorded in sensory, premotor, and motor areas of a monkey during a somatosensory discrimination task.
Edit intent: clarity
Original:  In the present work we analyzed neural correlations using spike-train recordings that were simultaneously recorded in sensory, premotor, and motor areas of a monkey during a somatosensory discrimination task.
Revised:  In the present work we analyzed neural correlations using spike-train recordings that were simultaneously recorded in sensory, premotor, and motor cortical areas of two monkeys during a somatosensory discrimination task.
Edit intent: clarity
Original:  Upon modeling spike-trains as binary time series  we used a non-parametric Bayesian method to estimate pairwise directional correlations between every neuron pair during the different stages of the task, namely, perception, working memory, decision making and motor action .
Revised:  Upon modeling spike trains as binary time series we used a non-parametric Bayesian method to estimate pairwise directional correlations between every neuron pair during the different stages of the task, namely, perception, working memory, decision making and motor action .
Edit intent: fluency
Original:  Upon modeling spike-trains as binary time series  we used a non-parametric Bayesian method to estimate pairwise directional correlations between every neuron pair during the different stages of the task, namely, perception, working memory, decision making and motor action .
Revised:  Upon modeling spike-trains as binary time series , we used a non-parametric Bayesian method to estimate pairwise directional correlations between every neuron pair during the different stages of the task, namely, perception, working memory, decision making and motor action .
Edit intent: fluency
Original:  Upon modeling spike-trains as binary time series  we used a non-parametric Bayesian method to estimate pairwise directional correlations between every neuron pair during the different stages of the task, namely, perception, working memory, decision making and motor action .
Revised:  Upon modeling spike-trains as binary time series we used a nonparametric Bayesian method to estimate pairwise directional correlations between every neuron pair during the different stages of the task, namely, perception, working memory, decision making and motor action .
Edit intent: fluency
Original:  Upon modeling spike-trains as binary time series  we used a non-parametric Bayesian method to estimate pairwise directional correlations between every neuron pair during the different stages of the task, namely, perception, working memory, decision making and motor action .
Revised:  Upon modeling spike-trains as binary time series we used a non-parametric Bayesian method to estimate pairwise directional correlations between many pairs of neurons throughout different stages of the task, namely, perception, working memory, decision making and motor action .
Edit intent: clarity
Original:  Upon modeling spike-trains as binary time series  we used a non-parametric Bayesian method to estimate pairwise directional correlations between every neuron pair during the different stages of the task, namely, perception, working memory, decision making and motor action .
Revised:  Upon modeling spike-trains as binary time series we used a non-parametric Bayesian method to estimate pairwise directional correlations between every neuron pair during the different stages of the task, namely, perception, working memory, decision making , and motor report .
Edit intent: clarity
Original:  We find that solving the task involves feed-forward and feedback correlation paths linking sensory and motor areas during certain task intervals.
Revised:  We find that solving the task involves feedforward and feedback correlation paths linking sensory and motor areas during certain task intervals.
Edit intent: fluency
Original:  Specifically, task information is communicated by decision-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor  and motor areas when decision making takes place.
Revised:  Specifically, information is communicated by decision-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor and motor areas when decision making takes place.
Edit intent: clarity
Original:  Specifically, task information is communicated by decision-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor  and motor areas when decision making takes place.
Revised:  Specifically, task information is communicated by task-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor and motor areas when decision making takes place.
Edit intent: fluency
Original:  Specifically, task information is communicated by decision-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor  and motor areas when decision making takes place.
Revised:  Specifically, task information is communicated by decision-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor , and motor areas when decision making takes place.
Edit intent: fluency
Original:  Crucially, when the monkey is no longer requested to perform the task , the majority of these correlations consistently vanish across all  areas.
Revised:  Crucially, when the monkey is no longer requested for task performance, a major proportion of directional correlations consistently vanish across all areas.
Edit intent: clarity
Original:  (3) estimating weights for triadic features that map directly to theoretical models such as structural balance.
Revised:  (3) estimating weights for triadic features that map to theoretical models such as structural balance.
Edit intent: clarity
Original:  To avoid the intractable sum over possible labelings of each network, we employ noise-contrastive estimation, thus obtaining the structural parameters efficiently.
Edit intent: coherence
Original:  We apply this model to a dataset of movie scripts, with the goal of learning the  social function of address terms  such as 'Madame', 'comrade', and 'dude'.
Revised:  We apply this model to the social function of address terms such as 'Madame', 'comrade', and 'dude'.
Edit intent: clarity
Original:  We apply this model to a dataset of movie scripts, with the goal of learning the  social function of address terms  such as 'Madame', 'comrade', and 'dude'.
Revised:  We apply this model to a dataset of movie scripts, with the goal of learning the problem of inducing the social function of address terms such as 'Madame', 'comrade', and 'dude'.
Edit intent: clarity
Original:  We apply this model to a dataset of movie scripts, with the goal of learning the  social function of address terms  such as 'Madame', 'comrade', and 'dude'.
Revised:  We apply this model to a dataset of movie scripts, with the goal of learning the social function of address terms , such as 'Madame', 'comrade', and 'dude'.
Edit intent: fluency
Original:  Working without any labeled data , our system offers a coherent clustering of address terms, while at the same time making intuitively plausible judgments of the formality of social relations in each film  .
Revised:  On a dataset of movie scripts , our system offers a coherent clustering of address terms, while at the same time making intuitively plausible judgments of the formality of social relations in each film .
Edit intent: clarity
Original:  Working without any labeled data , our system offers a coherent clustering of address terms, while at the same time making intuitively plausible judgments of the formality of social relations in each film  .
Revised:  Working without any labeled data , our system obtains a coherent clustering of address terms, while at the same time making intuitively plausible judgments of the formality of social relations in each film .
Edit intent: clarity
Original:  Yet, different Wikipedia language editions offer different descriptions of cultural phenomena .
Revised:  Yet, different Wikipedia language editions offer different descriptions of cultural practices .
Edit intent: clarity
Original:  Unveiling diverging representations of cultures is an important problem since they may foster the formation of cross-cultural stereotypes, misunderstandings and potentially even conflict.
Revised:  Unveiling diverging representations of cultures provides an important insight, since they may foster the formation of cross-cultural stereotypes, misunderstandings and potentially even conflict.
Edit intent: clarity
Original:  We demonstrate the utility of our approach via a case study that focuses on food cultures and validate our results using 1) various external reference data sources (i.e., the European Social Value Survey, migration statistics), 2) crowdsourcing methods and 3) simulations.
Revised:  We demonstrate the utility of our approach via a case study that focuses on food cultures and validate our results using 1) various external reference data sources (i.e., the European Social Survey, migration statistics), 2) crowdsourcing methods and 3) simulations.
Edit intent: clarity
Original: The Hopfield recurrent neural network is an auto-associative distributed model of memory .
Revised: The Hopfield recurrent neural network is an auto-associative model of memory .
Edit intent: clarity
Original:  Here, we design sparsely-connected Hopfield networks on n-nodes having%DIFDELCMD < \[2^{\sqrt{2n + 1{4}}}{n^{1/4} \pi}\] %%% graph cliques as robust memories by analytically minimizing the probability flowobjective function over these patterns.
Revised:  Here, we %DIFDELCMD < \[2^{\sqrt{2n + 1{4}}}{n^{1/4} \pi}\] %%% graph cliques as robust memories by analytically minimizing the probability flowobjective function over these patterns.
Edit intent: clarity
Original: The cross-media retrieval problem has received much attention in recent years due to the rapidly increasing multimedia data on the Internet.
Revised: The cross-media retrieval problem has received much attention in recent years due to the rapid increasing of multimedia data on the Internet.
Edit intent: fluency
Original:  A new approach to the problem was raised which intends to match the different modal features directly.
Revised:  A new approach to the problem has been raised which intends to match the different modal features directly.
Edit intent: fluency
Original:  Thus, how to get rid of the heterogeneity between the different modalities and match the different modal features with different dimensionsbecome the key points in this research.
Revised:  Thus, how to get rid of the heterogeneity between different modalities and match the different modal features with different dimensionsbecome the key points in this research.
Edit intent: fluency
Original:  However, the traditional metric learning algorithms are only focusing on one modality features, which suffers difficulties to handle the heterogeneous features with different dimensions.
Revised:  However, the traditional metric learning algorithms only focus on single-modal features, which suffers difficulties to handle the heterogeneous features with different dimensions.
Edit intent: clarity
Original:  However, the traditional metric learning algorithms are only focusing on one modality features, which suffers difficulties to handle the heterogeneous features with different dimensions.
Revised:  However, the traditional metric learning algorithms are only focusing on one modality features, which suffer difficulties in addressing the cross-modal features of different dimensions.
Edit intent: clarity
Original:  Thus, in this paper, we proposed a heterogeneous similarity learning algorithm based on the metric learning for the cross-modal feature matching.
Revised:  In this paper, we proposed a heterogeneous similarity learning algorithm based on the metric learning for the cross-modal feature matching.
Edit intent: coherence
Original:  Thus, in this paper, we proposed a heterogeneous similarity learning algorithm based on the metric learning for the cross-modal feature matching.
Revised:  Thus, in this paper, we propose a cross-modal similarity learning algorithm based on the metric learning for the cross-modal feature matching.
Edit intent: clarity
Original:  Thus, in this paper, we proposed a heterogeneous similarity learning algorithm based on the metric learning for the cross-modal feature matching.
Revised:  Thus, in this paper, we proposed a heterogeneous similarity learning algorithm for the cross-modal feature matching.
Edit intent: clarity
Original:  With the nuclear penalization, an accelerated proximal gradient algorithm is successfully imported to find the optimal solution with the fast convergence rate of O(1/t^2).
Revised:  With the nuclear penalization, an accelerated proximal gradient algorithm is successfully imported to find the optimal solution with a fast convergence rate of O(1/t^2).
Edit intent: fluency
Original:  With the nuclear penalization, an accelerated proximal gradient algorithm is successfully imported to find the optimal solution with the fast convergence rate of O(1/t^2).
Revised:  With the nuclear penalization, an accelerated proximal gradient algorithm is successfully imported to find the optimal solution with the fast convergence rate O(1/t^2).
Edit intent: coherence
Original: We identify a new natural coalescent structure, the seed-bank coalescent, which describes the gene genealogy of populations under the influence of a strong seed-bank effect, where `dormant forms' of individuals (such as seeds or spores) may jump a significant number of generations before joining the `active' population.
Revised: We identify a new natural coalescent structure, the seed-bank coalescent, whichseed-bank coalescent describes the gene genealogy of populations under the influence of a strong seed-bank effect, where `dormant forms' of individuals (such as seeds or spores) may jump a significant number of generations before joining the `active' population.
Edit intent: fluency
Original: We identify a new natural coalescent structure, the seed-bank coalescent, which describes the gene genealogy of populations under the influence of a strong seed-bank effect, where `dormant forms' of individuals (such as seeds or spores) may jump a significant number of generations before joining the `active' population.
Revised: We identify a new natural coalescent structure, the seed-bank coalescent, which, that describes the gene genealogy of populations under the influence of a strong seed-bank effect, where `dormant forms' of individuals (such as seeds or spores) may jump a significant number of generations before joining the `active' population.
Edit intent: coherence
Original:  In particular, the seed-bank coalescent `does not come down from infinity', and the time to the most recent common ancestor of a sample of size n grows like \log\log n , which is the order also observed for the Bolthausen-Sznitman coalescent .
Revised:  In particular, the seed-bank coalescent `does not come down from infinity', and the time to the most recent common ancestor of a sample of size n grows like \log\log n .
Edit intent: clarity
Original: We identify a new natural coalescent structure, which we call the seed-bank coalescent , that describes the gene genealogy of populations under the influence of a strong seed-bank effect, where `dormant forms' of individuals (such as seeds or spores) may jump a significant number of generations before joining the `active' population.
Revised: We identify a new natural coalescent structure, which we call the seed-bank coalescent , that describes the gene genealogy of populations under the influence of a strong seed-bank effect, where `dormant forms' of individuals (such as seeds or spores) may jump a significant number of generations before joining the `active' population.
Edit intent: fluency
Original: We identify a new natural coalescent structure, which we call the seed-bank coalescent , that describes the gene genealogy of populations under the influence of a strong seed-bank effect, where `dormant forms' of individuals (such as seeds or spores) may jump a significant number of generations before joining the `active' population.
Revised: We identify a new natural coalescent structure, which we call the seed-bank coalescent , that describes the gene genealogy of populations under the influence of a strong seed-bank effect, where "dormant forms" of individuals (such as seeds or spores) may jump a significant number of generations before joining the `active' population.
Edit intent: fluency
Original: We identify a new natural coalescent structure, which we call the seed-bank coalescent , that describes the gene genealogy of populations under the influence of a strong seed-bank effect, where `dormant forms' of individuals (such as seeds or spores) may jump a significant number of generations before joining the `active' population.
Revised: We identify a new natural coalescent structure, which we call the seed-bank coalescent , that describes the gene genealogy of populations under the influence of a strong seed-bank effect, where `dormant forms' of individuals (such as seeds or spores) may jump a significant number of generations before joining the "active" population.
Edit intent: fluency
Original:  This extends earlier results of Kaj, Krone and Lascoux (2001) who show that the genealogy of a Wright-Fisher model in the presence of a `weak' seed-bank effect is given by a suitably time-changed Kingman coalescent.
Revised:  This extends earlier results of Kaj, Krone and Lascoux (2001) who show that the genealogy of a Wright-Fisher model in the presence of a "weak" seed-bank effect is given by a suitably time-changed Kingman coalescent.
Edit intent: fluency
Original:  In particular, the seed-bank coalescent ` does not come down from infinity ', and the time to the most recent common ancestor of a sample of size n grows like \log\log n.
Revised:  In particular, the seed-bank coalescent " does not come down from infinity ', and the time to the most recent common ancestor of a sample of size n grows like \log\log n.
Edit intent: fluency
Original:  In particular, the seed-bank coalescent ` does not come down from infinity ', and the time to the most recent common ancestor of a sample of size n grows like \log\log n.
Revised:  In particular, the seed-bank coalescent ` does not come down from infinity ," and the time to the most recent common ancestor of a sample of size n grows like \log\log n.
Edit intent: fluency
Original:  While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics is conserved, here we show that this is generally impossible already for second-order statistics .
Revised:  While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics are conserved, here we show that this is generally impossible already for second-order statistics .
Edit intent: fluency
Original:  While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics is conserved, here we show that this is generally impossible already for second-order statistics .
Revised:  While reliable methods have long existed to adjust parameters such that the first-order statistics of network dynamics is conserved, here we show that limitations already arise if also second-order statistics .
Edit intent: clarity
Original:  When changing in-degrees, effective connectivity and hence correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a limited range of in-degrees determined by the extrinsic variance .
Revised:  When changing in-degrees, effective connectivity and hence correlation structure can be maintained by an appropriate scaling of the synaptic weights, but only over a range of numbers of synapses that is limited by the variance of external inputs to the network .
Edit intent: clarity
Original:  Our results  show that the reducibility of asynchronous networks is fundamentally limited.
Revised:  Our results therefore show that the reducibility of asynchronous networks is fundamentally limited.
Edit intent: coherence