wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper we aim to show that the concept of ensemble of trajectories can be applied to a variety of stochastic models of gene expression and hence , apart from asymptotic behavior of remote tails of probability distributions of the dynamical observables, generating functions for the cumulants of these observables can easily be obtained.
Revised:  In this paper we aim to show that the concept of ensemble of trajectories can be applied to a variety of stochastic models of gene expression and hence , apart from asymptotic behavior of remote tails of probability distribution, generating function for the cumulants of these observables can easily be obtained.
Edit intent: clarity
Original:  In this paper we aim to show that the concept of ensemble of trajectories can be applied to a variety of stochastic models of gene expression and hence , apart from asymptotic behavior of remote tails of probability distributions of the dynamical observables, generating functions for the cumulants of these observables can easily be obtained.
Revised:  In this paper we aim to show that the concept of ensemble of trajectories can be applied to a variety of stochastic models of gene expression and hence , apart from asymptotic behavior of remote tails of probability distributions of the dynamical observables, generating functions for the cumulants of this observable can also be obtained.
Edit intent: clarity
Original:  For instance, mixtures are common in unsupervised image segmentation, and typically rely on averaging the posterior mixing probabilities of spatially adjacent data points (i. e. smoothing). However, this approach has had limited success with natural images. Herewe make three contributions. First, we show that a Dirichlet prior with an appropriate choice of parameters allows -- using the  Expectation-Maximization approach -- to define any linear update rule for the mixing probabilities, including many smoothing regularizations as special cases.
Revised:  For instance, in order to classify spatially adjacent data points similarly, it is common to introduce a Laplacian constraint on the Expectation-Maximization approach -- to define any linear update rule for the mixing probabilities, including many smoothing regularizations as special cases.
Edit intent: meaning-changed
Original:  First, we show that a Dirichlet prior with an appropriate choice of parameters allows -- using the  Expectation-Maximization approach -- to define any linear update rule for the mixing probabilities, including many smoothing regularizations as special cases.
Revised:  First, we show that a Dirichlet prior with an appropriate choice of parameters allows -- using the posterior probability that each data point belongs to a class. Alternatively, the mixing probabilities can be treated as free parameters, while assuming Gauss-Markov or more complex priors to regularize those mixing probabilities. However, these approaches are constrained by the shape of the prior and often lead to complicated or intractable inference. Here, we propose a new parametrization of the Dirichlet distribution to flexibly regularize the mixing probabilities of over-parametrized mixture distributions. Using the Expectation-Maximization approach -- to define any linear update rule for the mixing probabilities, including many smoothing regularizations as special cases.
Edit intent: meaning-changed
Original:  First, we show that a Dirichlet prior with an appropriate choice of parameters allows -- using the  Expectation-Maximization approach -- to define any linear update rule for the mixing probabilities, including many smoothing regularizations as special cases.
Revised:  First, we show that a Dirichlet prior with an appropriate choice of parameters allows -- using the Expectation-Maximization algorithm, we show that our approach allows us to define any linear update rule for the mixing probabilities, including many smoothing regularizations as special cases.
Edit intent: meaning-changed
Original:  First, we show that a Dirichlet prior with an appropriate choice of parameters allows -- using the  Expectation-Maximization approach -- to define any linear update rule for the mixing probabilities, including many smoothing regularizations as special cases. Second, we demonstrate how to use this flexible design of the update rule to propagate segmentation information across layers of a deep network, and to train mixtures jointly across layers.
Revised:  First, we show that a Dirichlet prior with an appropriate choice of parameters allows -- using the Expectation-Maximization approach -- to define any linear update rule for the mixing probabilities, including spatial smoothing regularization as a special case. We then show that this flexible design of the update rule to propagate segmentation information across layers of a deep network, and to train mixtures jointly across layers.
Edit intent: clarity
Original:  Second, we demonstrate how to use this flexible design of the update rule to propagate segmentation information across layers of a deep network, and to train mixtures jointly across layers. Third, we compare the standard Gaussian mixture and the Student-t mixture, which is known to better capture the statistics of low-level visual features. We show that our models achieve competitive performance in natural image segmentation , with the Student-t mixtures reaching state-of-the art on boundaries scores .
Revised:  Second, we demonstrate how to use this flexible design can be extended to share class information between multiple mixture models. We apply our algorithm to artificial and natural image segmentation , with the Student-t mixtures reaching state-of-the art on boundaries scores .
Edit intent: clarity
Original: The rise of increasingly more powerful chatbots offers a new way to collect information through conversational surveys, where a chatbot asks open-ended questions, interprets a user's free-text responses, and probes answers when needed.
Revised: The rise of increasingly more powerful chatbots offers a new way to collect information through conversational surveys, where a chatbot asks open-ended questions, interprets a user's free-text responses, and probes answers whenever needed.
Edit intent: fluency
Original:  Our detailed analysis of over 5200 free-text responses revealed that the chatbot drove a significantly higher level of participant engagement and elicited significantly better quality responses  in terms of relevance, depth, and readability .
Revised:  Our detailed analysis of over 5200 free-text responses revealed that the chatbot drove a significantly higher level of participant engagement and elicited significantly better quality responses measured by Gricean Maxims in terms of relevance, depth, and readability .
Edit intent: meaning-changed
Original:  Our detailed analysis of over 5200 free-text responses revealed that the chatbot drove a significantly higher level of participant engagement and elicited significantly better quality responses  in terms of relevance, depth, and readability .
Revised:  Our detailed analysis of over 5200 free-text responses revealed that the chatbot drove a significantly higher level of participant engagement and elicited significantly better quality responses in terms of their informativeness, relevance, specificity, and clarity .
Edit intent: clarity
Original:  Moreover, we establish a new link between percolation models and game theory to shed light on various aspects of Nash equilibria. Through this connection , we describe in detail the geometry of Nash equilibria and show that, when the probability of ties is small, a best-response dynamics reaches a Nash equilibrium with a probability that quickly approaches one as the number of players grows.
Revised:  Moreover, we establish a new link between percolation models and game theory , we describe in detail the geometry of Nash equilibria and show that, when the probability of ties is small, a best-response dynamics reaches a Nash equilibrium with a probability that quickly approaches one as the number of players grows.
Edit intent: clarity
Original: While cooperativity in ligand-induced receptor dimerization (e.g., EGF receptors) have been linked with receptor-receptor couplings via state-vector representations of physical observables, effects arising from higher-order oligomer  formations of unobserved receptors have received less attention.
Revised: While cooperativity in ligand-induced receptor dimerization has been linked with receptor-receptor couplings via state-vector representations of physical observables, effects arising from higher-order oligomer formations of unobserved receptors have received less attention.
Edit intent: clarity
Original: While cooperativity in ligand-induced receptor dimerization (e.g., EGF receptors) have been linked with receptor-receptor couplings via state-vector representations of physical observables, effects arising from higher-order oligomer  formations of unobserved receptors have received less attention.
Revised: While cooperativity in ligand-induced receptor dimerization (e.g., EGF receptors) have been linked with receptor-receptor couplings via minimal representations of physical observables, effects arising from higher-order oligomer formations of unobserved receptors have received less attention.
Edit intent: meaning-changed
Original: While cooperativity in ligand-induced receptor dimerization (e.g., EGF receptors) have been linked with receptor-receptor couplings via state-vector representations of physical observables, effects arising from higher-order oligomer  formations of unobserved receptors have received less attention.
Revised: While cooperativity in ligand-induced receptor dimerization (e.g., EGF receptors) have been linked with receptor-receptor couplings via state-vector representations of physical observables, effects arising from higher-order oligomer (e.g., trimer and tetramer) formations of unobserved receptors have received less attention.
Edit intent: meaning-changed
Original:  First, we extend existing BSDE-based xVA frameworks to include initial margin by following the approach of Cr\'epey (2015a) and Cr\'epey (2015b).
Revised:  First, we enhance current BSDE-based xVA frameworks to include initial margin by following the approach of Cr\'epey (2015a) and Cr\'epey (2015b).
Edit intent: clarity
Original:  First, we extend existing BSDE-based xVA frameworks to include initial margin by following the approach of Cr\'epey (2015a) and Cr\'epey (2015b).
Revised:  First, we extend existing BSDE-based xVA frameworks to include initial margin by following the approach o Cr\'epey (2015a) and Cr\'epey (2015b).
Edit intent: fluency
Original: Deep neural networks (DNNs) have revolutionized AI due to their remarkable performance in pattern recognition, comprising of both memorizing complex training sets and demonstrating intelligence by generalizing to previously unseen data (test sets).
Revised: A longstanding problem for Deep Neural Networks (DNNs) have revolutionized AI due to their remarkable performance in pattern recognition, comprising of both memorizing complex training sets and demonstrating intelligence by generalizing to previously unseen data (test sets).
Edit intent: meaning-changed
Original: Deep neural networks (DNNs) have revolutionized AI due to their remarkable performance in pattern recognition, comprising of both memorizing complex training sets and demonstrating intelligence by generalizing to previously unseen data (test sets). The high generalization performance in DNNs has been explained by several mathematical tools, including optimization, information theory, and resilience analysis. In humans, it is the ability to abstract concepts from examples that facilitates generalization; this paper thus researches DNN generalization from that perspective. A recent computational neuroscience study revealed a correlation between abstraction and particular neural firing patterns. We express these brain patterns in a closed-form mathematical expression, termed the ` Cognitive Neural Activation metric ' (CNA) and apply it to DNNs.
Revised: Deep neural networks (DNNs) is understanding their puzzling ability to generalize well. We approach this problem through the unconventional angle of Cognitive Neural Activation metric ' (CNA) and apply it to DNNs.
Edit intent: coherence
Original:  We express these brain patterns in a closed-form mathematical expression, termed the ` Cognitive Neural Activation metric ' (CNA) and apply it to DNNs.
Revised:  We express these brain patterns in a closed-form mathematical expression, termed the `, drawing inspiration from recent neuroscience work, allowing us to define the Cognitive Neural Activation metric ' (CNA) and apply it to DNNs.
Edit intent: meaning-changed
Original:  We express these brain patterns in a closed-form mathematical expression, termed the ` Cognitive Neural Activation metric ' (CNA) and apply it to DNNs. Our findings reveal parallels in the mechanism underlying abstraction in DNNs and those in the human brain. Beyond simply measuring similarity to human abstraction, the CNA is able to predict and rate how well a DNN will perform on test sets, and determines the best network architectures for a given task in a manner not possible with extant tools. These results were validated on a broad range of datasets (including ImageNet and random labeled datasets) and neural architectures .
Revised:  We express these brain patterns in a closed-form mathematical expression, termed the ` Cognitive Neural Activation metric ' (CNA) for DNNs, which is the correlation between information complexity (entropy) of given input and the concentration of higher activation values in deeper layers of the network. The CNA is highly predictive of generalization ability, outperforming norm-and-margin-based generalization metrics on an extensive evaluation of over 100 dataset-and-network-architecture combinations, especially in cases where additive noise is present and/or training labels are corrupted. These strong empirical results show the usefulness of CNA as a generalization metric, and encourage further research on the connection between information complexity and representations in the deeper layers of networks in order to better understand the generalization capabilities of DNNs .
Edit intent: meaning-changed
Original:  To address this issue , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable  graph pooling method that takes into account the node features and graph structure to improve molecular understanding.
Revised:  To address these issues , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable graph pooling method that takes into account the node features and graph structure to improve molecular understanding.
Edit intent: fluency
Original:  To address this issue , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable  graph pooling method that takes into account the node features and graph structure to improve molecular understanding.
Revised:  To address this issue , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable hierarchical graph pooling method that takes into account the node features and graph structure to improve molecular understanding.
Edit intent: meaning-changed
Original:  To address this issue , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable  graph pooling method that takes into account the node features and graph structure to improve molecular understanding.
Revised:  To address this issue , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable graph pooling method that takes into account both node features and graph structure to improve molecular understanding.
Edit intent: clarity
Original:  To address this issue , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable  graph pooling method that takes into account the node features and graph structure to improve molecular understanding. Inspired by theories in graph signal processing, LaPool performs a feature-driven hierarchical segmentation of molecules by selecting a set of centroid nodes from a graph as cluster representatives. It then learns a sparse assignment of remaining nodes into these clusters using an attention mechanism. We benchmark our model by showing that it outperforms recent graph pooling layers on molecular graph understanding and prediction tasks.
Revised:  To address this issue , we propose LaPool (Laplacian Pooling), a novel, data-driven, and interpretable graph pooling method that takes into account the node features and graph structure to improve molecular representation. We benchmark LaPool on molecular graph understanding and prediction tasks.
Edit intent: meaning-changed
Original:  We benchmark our model by showing that it outperforms recent graph pooling layers on molecular graph understanding and prediction tasks.
Revised:  We benchmark our model by showing that it outperforms recent graph pooling layers on molecular graph prediction and understanding tasks and show that it outperforms recent GNNs. Interestingly, LaPool also remains competitive on non-molecular tasks. Both quantitative and qualitative assessments are done to demonstrate LaPool's improved interpretability and prediction tasks.
Edit intent: meaning-changed
Original:  We benchmark our model by showing that it outperforms recent graph pooling layers on molecular graph understanding and prediction tasks. We then demonstrate improved interpretability by identifying important molecular substructures and generating novel and valid molecules, with important applications in drug discovery and pharmacology .
Revised:  We benchmark our model by showing that it outperforms recent graph pooling layers on molecular graph understanding and highlight its potential benefits in drug design. Finally, we demonstrate LaPool's utility for the generation of valid and novel molecules by incorporating it into an adversarial autoencoder .
Edit intent: meaning-changed
Original:  We finally use the insights from the attention experiments to alter BERT : we remove the learned attention at shallow layers, and show that this manipulation improves performance on a wide range of syntactic tasks . Cognitive neuroscientists have already begun using NLP networks to study the brain, and this work closes the loop to allow the interaction between NLP and cognitive neuroscience to be a true cross-pollination.
Revised:  We finally hypothesize that altering BERT to better align with brain recordings would enable it to also better understand language. Probing the altered BERT using syntactic NLP tasks reveals that the model with increased brain-alignment outperforms the original model . Cognitive neuroscientists have already begun using NLP networks to study the brain, and this work closes the loop to allow the interaction between NLP and cognitive neuroscience to be a true cross-pollination.
Edit intent: meaning-changed
Original:  These models are typically requested to reproduce the one- and two-point statistics of the amino-acid usage in these protein families,{\em i.e. ~ the so-called residue conservation and covariation statistics  . Pairwise Potts models are the maximum-entropy models achieving this.
Revised:  These models are typically requested to reproduce the one- and two-point statistics of the amino-acid usage in a protein family,{\em i.e. ~ the so-called residue conservation and covariation statistics . Pairwise Potts models are the maximum-entropy models achieving this.
Edit intent: fluency
Original:  These models are typically requested to reproduce the one- and two-point statistics of the amino-acid usage in these protein families,{\em i.e. ~ the so-called residue conservation and covariation statistics  . Pairwise Potts models are the maximum-entropy models achieving this.
Revised:  These models are typically requested to reproduce the one- and two-point statistics of the amino-acid usage in these protein families,{\em i.e. ~to capture the so-called residue conservation and covariation statistics . Pairwise Potts models are the maximum-entropy models achieving this.
Edit intent: clarity
Original:  These models are typically requested to reproduce the one- and two-point statistics of the amino-acid usage in these protein families,{\em i.e. ~ the so-called residue conservation and covariation statistics  . Pairwise Potts models are the maximum-entropy models achieving this.
Revised:  These models are typically requested to reproduce the one- and two-point statistics of the amino-acid usage in these protein families,{\em i.e. ~ the so-called residue conservation and covariation statistics of proteins of common evolutionary origin . Pairwise Potts models are the maximum-entropy models achieving this.
Edit intent: clarity
Original:  In particular, several attempts at defining and/or quantifying complexity have  , at some point , run into intrinsic difficulties. This didactic text provides a brief review of some of the approaches that have been used to characterize complexity, and also suggests a possible definition of complexity based on the cost assigned to mapping the entity of interest, as well as on the  cost of the error implied by its respective reconstruction .
Revised:  In particular, several attempts at defining and/or quantifying complexity have , at some point , run into intrinsic limitations. Two are the main objectives of the present work: (i) to review some of the approaches that have been used to characterize complexity, and also suggests a possible definition of complexity based on the cost assigned to mapping the entity of interest, as well as on the cost of the error implied by its respective reconstruction .
Edit intent: clarity
Original:  This didactic text provides a brief review of some of the approaches that have been used to characterize complexity, and also suggests a possible definition of complexity based on the cost assigned to mapping the entity of interest, as well as on the  cost of the error implied by its respective reconstruction .
Revised:  This didactic text provides a brief review of some of the main approaches to complexity; and (ii) to suggest a cost-based approach that, to a great extent, can be understood as an integration of the cost of the error implied by its respective reconstruction .
Edit intent: clarity
Original:  This didactic text provides a brief review of some of the approaches that have been used to characterize complexity, and also suggests a possible definition of complexity based on the cost assigned to mapping the entity of interest, as well as on the  cost of the error implied by its respective reconstruction .
Revised:  This didactic text provides a brief review of some of the approaches that have been used to characterize complexity, and also suggests a possible definition of complexity based on the cost assigned to mapping the entity of interest, as well as on the several facets of complexity. More specifically, it is poised that complexity, an inherently relative and subjective concept, can be summarized as the cost of the error implied by its respective reconstruction .
Edit intent: meaning-changed
Original:  This didactic text provides a brief review of some of the approaches that have been used to characterize complexity, and also suggests a possible definition of complexity based on the cost assigned to mapping the entity of interest, as well as on the  cost of the error implied by its respective reconstruction .
Revised:  This didactic text provides a brief review of some of the approaches that have been used to characterize complexity, and also suggests a possible definition of complexity based on the cost assigned to mapping the entity of interest, as well as on the cost of developing a model, plus the cost of its respective operation. The proposal is illustrated respectively to several applications examples, including a real-data base situation .
Edit intent: meaning-changed