wanyu/IteraTeR_full_sent huggingface dataset:
Original: This paper solves the optimal investment and consumption strategies for an ambiguity-averse agent in an incomplete financial market . The agent seeks her best and robust strategies via optimizing her robust forward investment and consumption preferences.
Revised: This paper solves the optimal investment and consumption strategies for an ambiguity-averse agent in an incomplete financial market with model uncertainty. The market incompleteness arises from investment constraints of the agent, while the model uncertainty stems from drift and volatility processes for risky stocks in the financial market. The agent seeks her best and robust strategies via optimizing her robust forward investment and consumption preferences.
Edit intent: meaning-changed
Original:  The results are based on the study of the combinatorial properties of the B-, T-, and W-configurations in a 3-connected 1-plane graph . An implication of the upper bound is the existence of an O(n^10{7})-time drawing algorithm that computes an OPVR of an n-vertex 3-connected 1-plane graph on an integer grid of size O(n) \times O(n) and with vertex complexity at most five.
Revised:  These results are based on the study of the combinatorial properties of the B-, T-, and W-configurations in a 3-connected 1-plane graph . An implication of the upper bound is the existence of an O(n^10{7})-time drawing algorithm that computes an OPVR of an n-vertex 3-connected 1-plane graph on an integer grid of size O(n) \times O(n) and with vertex complexity at most five.
Edit intent: fluency
Original:  The results are based on the study of the combinatorial properties of the B-, T-, and W-configurations in a 3-connected 1-plane graph . An implication of the upper bound is the existence of an O(n^10{7})-time drawing algorithm that computes an OPVR of an n-vertex 3-connected 1-plane graph on an integer grid of size O(n) \times O(n) and with vertex complexity at most five.
Revised:  The results are based on the study of the combinatorial properties of the B-, T-, and W-configurations in 3-connected 1-plane graph . An implication of the upper bound is the existence of an O(n^10{7})-time drawing algorithm that computes an OPVR of an n-vertex 3-connected 1-plane graph on an integer grid of size O(n) \times O(n) and with vertex complexity at most five.
Edit intent: fluency
Original:  The results are based on the study of the combinatorial properties of the B-, T-, and W-configurations in a 3-connected 1-plane graph . An implication of the upper bound is the existence of an O(n^10{7})-time drawing algorithm that computes an OPVR of an n-vertex 3-connected 1-plane graph on an integer grid of size O(n) \times O(n) and with vertex complexity at most five.
Revised:  The results are based on the study of the combinatorial properties of the B-, T-, and W-configurations in a 3-connected 1-plane graphs . An implication of the upper bound is the existence of an O(n^10{7})-time drawing algorithm that computes an OPVR of an n-vertex 3-connected 1-plane graph on an integer grid of size O(n) \times O(n) and with vertex complexity at most five.
Edit intent: fluency
Original:  The results are based on the study of the combinatorial properties of the B-, T-, and W-configurations in a 3-connected 1-plane graph . An implication of the upper bound is the existence of an O(n^10{7})-time drawing algorithm that computes an OPVR of an n-vertex 3-connected 1-plane graph on an integer grid of size O(n) \times O(n) and with vertex complexity at most five.
Revised:  The results are based on the study of the combinatorial properties of the B-, T-, and W-configurations in a 3-connected 1-plane graph . An implication of the upper bound is the existence of a O(n^10{7})-time drawing algorithm that computes an OPVR of an n-vertex 3-connected 1-plane graph on an integer grid of size O(n) \times O(n) and with vertex complexity at most five.
Edit intent: fluency
Original: Predicting how proteins interact with one another - that is, which surfaces of one protein bind to which surfaces of another protein - is a central problem in biology.
Revised: While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question. In this work we demonstrate that representations learned from raw atomic coordinates can outperform hand-engineered structural features while displaying a much higher degree of transferrability. To do so, we focus on a central problem in biology: predicting how proteins interact with one another - that is, which surfaces of one protein bind to which surfaces of another protein - is a central problem in biology.
Edit intent: meaning-changed
Original: Predicting how proteins interact with one another - that is, which surfaces of one protein bind to which surfaces of another protein - is a central problem in biology. Here we present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction.
Revised: Predicting how proteins interact with one another - that is, which surfaces of one protein bind to which surfaces of another protein . We present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction.
Edit intent: clarity
Original: While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question.
Revised: While there has been an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited. Whether performance in such tasks can be improved by using large repositories of tangentially related structural data remains an open research question.
Edit intent: meaning-changed
Original: While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question. In this work we demonstrate that representations learned from raw atomic coordinates can outperform hand-engineered structural features while displaying a much higher degree of transferrability. To do so, we focus on a central problem in biology: predicting how proteins interact with one another--that is, which surfaces of one protein bind to which surfaces of another protein.
Revised: While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open question. To address this question, we focused on a central problem in biology: predicting how proteins interact with one another--that is, which surfaces of one protein bind to which surfaces of another protein.
Edit intent: clarity
Original:  We present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction .
Revised:  We built a training dataset, the Database of Interacting Protein Structures (DIPS), that contains biases but is two orders of magnitude larger than those used previously. We found that these biases significantly degrade the performance of existing methods on gold-standard data. Hypothesizing that assumptions baked into the hand-crafted features on which these methods depend were the source of the problem, we developed the first end-to-end learning method for protein interface prediction .
Edit intent: meaning-changed
Original:  We present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction . Despite using only spatial coordinates and identities of atoms as inputs , SASNet outperforms state-of-the-art methods that rely on hand-engineered , high-level features.
Revised:  We present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction , the Siamese Atomic Surfacelet Network (SASNet). Using only spatial coordinates and identities of atoms as inputs , SASNet outperforms state-of-the-art methods that rely on hand-engineered , high-level features.
Edit intent: coherence
Original:  Despite using only spatial coordinates and identities of atoms as inputs , SASNet outperforms state-of-the-art methods that rely on hand-engineered , high-level features. These results are particularly striking because we train the method entirely on a significantly biased dataset that does not account for the fact that proteins deform when binding to one another. Demonstrating the first successful application of transfer learning to atomic-level data, our network maintains high performance, without retraining, when tested on real cases in which proteins do deform.
Revised:  Despite using only spatial coordinates and identities of atoms as inputs , SASNet outperforms state-of-the-art methods that rely on hand-engineered , even when trained on only 3\% of our new dataset. Code and data available at URL
Edit intent: meaning-changed
Original:  In the context of this setup, conclusions from previous results can be generally summarized as follows: (i) imposing instrument monotonicity can misspecify the model; and (ii) when the model is not misspecified, instrument monotonicity does not have any identifying content on the marginal distributions of the potential outcomes. In this paper, I demonstrate that instrument monotonicity can however have identifying content on features of the joint distribution of the potential outcomes when the model is not misspecified.
Revised:  Specifically, I derive necessary and sufficient conditions on the distribution of the data under which the identified set for the distribution of potential outcomes when the model is not misspecified.
Edit intent: clarity
Original:  In this paper, I demonstrate that instrument monotonicity can however have identifying content on features of the joint distribution of the potential outcomes when the model is not misspecified. I illustrate how this identifying content can lead to additional informative conclusions with respect to the proportion who benefit, a specific feature of the joint distribution of the potential outcomes .
Revised:  In this paper, I demonstrate that instrument monotonicity can however have identifying content on features of the joint distribution of the potential outcomes when the instrument monotonicity assumption is imposed can be a strict subset of that when it is not imposed .
Edit intent: meaning-changed
Original: Because CNN models are compute-intensive, where billions of operations can be required just for an inference over a single input image, a variety of CNN accelerators have been proposed and developed. For the early CNN models, the research mostly focused on convolutional and fully-connected layers because the two layers consumed most of the computation cycles. For more recent CNN models, however, non-convolutional layers have become comparably important because of the popular use of newly designed non-convolutional layers and because of the reduction in the number and size of convolutional filters. Non-convolutional layers, including batch normalization (BN) , typically have relatively lower computational intensity compared to the convolutional or fully-connected layers, and hence are often constrained by main-memory bandwidth.
Revised: Batch Normalization (BN) , typically have relatively lower computational intensity compared to the convolutional or fully-connected layers, and hence are often constrained by main-memory bandwidth.
Edit intent: coherence
Original:  Non-convolutional layers, including batch normalization (BN) , typically have relatively lower computational intensity compared to the convolutional or fully-connected layers, and hence are often constrained by main-memory bandwidth. In this paper, we focus on accelerating the BN layers among the non-convolutional layers, as BN has become a core design block of modern CNNs . A typical modern CNN has a large number of BN layers  . BN requires mean and variance calculations over each mini-batch during training.
Revised:  Non-convolutional layers, including batch normalization (BN) has become a core design block of modern CNNs . A typical modern CNN has a large number of BN layers . BN requires mean and variance calculations over each mini-batch during training.
Edit intent: coherence
Original:  In this paper, we focus on accelerating the BN layers among the non-convolutional layers, as BN has become a core design block of modern CNNs . A typical modern CNN has a large number of BN layers  . BN requires mean and variance calculations over each mini-batch during training.
Revised:  In this paper, we focus on accelerating the BN layers among the non-convolutional layers, as BN has become a core design block of modern Convolutional Neural Networks (CNNs) . A typical modern CNN has a large number of BN layers . BN requires mean and variance calculations over each mini-batch during training.
Edit intent: clarity
Original:  In this paper, we focus on accelerating the BN layers among the non-convolutional layers, as BN has become a core design block of modern CNNs . A typical modern CNN has a large number of BN layers  . BN requires mean and variance calculations over each mini-batch during training.
Revised:  In this paper, we focus on accelerating the BN layers among the non-convolutional layers, as BN has become a core design block of modern CNNs . A typical modern CNN has a large number of BN layers in its lean and deep architecture . BN requires mean and variance calculations over each mini-batch during training.
Edit intent: meaning-changed
Original:  To address this increasingly important problem, we propose to restructure BN layers by first splitting it into two sub-layers  and then combining the first sub-layer with its preceding convolutional layer and the second sub-layer with the following activation and convolutional layers .
Revised:  To address this increasingly important problem, we propose to restructure BN layers by first splitting a BN layer into two sub-layers and then combining the first sub-layer with its preceding convolutional layer and the second sub-layer with the following activation and convolutional layers .
Edit intent: clarity
Original:  To address this increasingly important problem, we propose to restructure BN layers by first splitting it into two sub-layers  and then combining the first sub-layer with its preceding convolutional layer and the second sub-layer with the following activation and convolutional layers .
Revised:  To address this increasingly important problem, we propose to restructure BN layers by first splitting it into two sub-layers (fission) and then combining the first sub-layer with its preceding convolutional layer and the second sub-layer with the following activation and convolutional layers .
Edit intent: meaning-changed
Original:  To address this increasingly important problem, we propose to restructure BN layers by first splitting it into two sub-layers  and then combining the first sub-layer with its preceding convolutional layer and the second sub-layer with the following activation and convolutional layers .
Revised:  To address this increasingly important problem, we propose to restructure BN layers by first splitting it into two sub-layers and then combining the first sub-layer with its preceding CONV layer and the second sub-layer with the following activation and convolutional layers .
Edit intent: meaning-changed
Original:  To address this increasingly important problem, we propose to restructure BN layers by first splitting it into two sub-layers  and then combining the first sub-layer with its preceding convolutional layer and the second sub-layer with the following activation and convolutional layers .
Revised:  To address this increasingly important problem, we propose to restructure BN layers by first splitting it into two sub-layers and then combining the first sub-layer with its preceding convolutional layer and the second sub-layer with the following activation and CONV layers (fusion) .
Edit intent: meaning-changed
Original:  The proposed solution can significantly reduce main-memory accesses while training the latest CNN models, and the experiments on a chip multiprocessor with our modified Caffe implementation show that the proposed BN restructuring can improve the performance of DenseNet with 121 convolutional layers by 28.4 \%.
Revised:  The proposed solution can significantly reduce main-memory accesses while training the latest CNN models, and the experiments on a chip multiprocessor show that the proposed BN restructuring can improve the performance of DenseNet with 121 convolutional layers by 28.4 \%.
Edit intent: clarity
Original:  The proposed solution can significantly reduce main-memory accesses while training the latest CNN models, and the experiments on a chip multiprocessor with our modified Caffe implementation show that the proposed BN restructuring can improve the performance of DenseNet with 121 convolutional layers by 28.4 \%.
Revised:  The proposed solution can significantly reduce main-memory accesses while training the latest CNN models, and the experiments on a chip multiprocessor with our modified Caffe implementation show that the proposed BN restructuring can improve the performance of DenseNet-121 by 25.7 \%.
Edit intent: meaning-changed
Original:  In addition, we develop a connection between the solution of the ergodic BSDE system and the long-term growth rate of classical utility maximization problems  .
Revised:  In addition, we develop a connection between the solution of the ergodic BSDE system and the long-term growth rate of classical utility maximization problems , and use the ergodic BSDE system to study the large time behavior of PDE systems with quadratic growth Hamiltonians .
Edit intent: meaning-changed
Original:  Differently, under public WOM, the seller may prefer an environment with less competition between informed consumers and the presence of highly connected influencers (hubs) is the main driver to make network density beneficial to profits.
Revised:  Differently, under Public WOM, the seller may prefer an environment with less competition between informed consumers and the presence of highly connected influencers (hubs) is the main driver to make network density beneficial to profits.
Edit intent: fluency
Original:  When the seller is able to discriminate between private and public WOM, the optimal strategy is to cheaply incentivize the more connected people to pass on the information publicly and then offer a high bonus for private WOM.
Revised:  When the seller is able to discriminate between Private and Public WOM, the optimal strategy is to cheaply incentivize the more connected people to pass on the information publicly and then offer a high bonus for private WOM.
Edit intent: fluency
Original:  When the seller is able to discriminate between private and public WOM, the optimal strategy is to cheaply incentivize the more connected people to pass on the information publicly and then offer a high bonus for private WOM.
Revised:  When the seller is able to discriminate between private and public WOM, the optimal strategy is to cheaply incentivize the more connected people to pass on the information publicly and then offer a high bonus for Private WOM.
Edit intent: fluency
Original:  In contrast to the previous research, we (i) consider very general driving processes  , (ii) allow for inhomogeneous connection structures, and (iii) analyze a game in which the particles determine their connections strategically.
Revised:  In contrast to the previous research, we (i) consider very general driving processes and interaction functions , (ii) allow for inhomogeneous connection structures, and (iii) analyze a game in which the particles determine their connections strategically.
Edit intent: meaning-changed
Original:  Two auxiliary mathematical results, useful in their own right, are uncovered during our investigation: a generalization of Schauder's fixed-point theorem for the Skorokhod space with the M1 topology, and the application of the max-plus algebra to the equilibrium problem for credit networks .
Revised:  Two auxiliary mathematical results, useful in their own right, are uncovered during our investigation: a generalization of Schauder's fixed-point theorem for the Skorokhod space with the M1 topology, and the application of the max-plus algebra to the equilibrium version of the network flow problem .
Edit intent: clarity
Original: We develop a new approach for estimating average treatment effects in the observational studies with unobserved cluster-level heterogeneity. The previous approach relied heavily on linear fixed effect specifications that severely limit the heterogeneity between clusters.
Revised: We develop a new approach for estimating average treatment effects in the observational studies with unobserved group-level heterogeneity. A common approach in such settings is to use linear fixed effect specifications that severely limit the heterogeneity between clusters.
Edit intent: clarity
Original:  The previous approach relied heavily on linear fixed effect specifications that severely limit the heterogeneity between clusters. These methods imply that linearly adjusting for differences between clusters in average covariate values addresses all concerns with cross-cluster comparisons.
Revised:  The previous approach relied heavily on linear fixed effect specifications that severely limit the extent of the heterogeneity between groups by making the restrictive assumption that linearly adjusting for differences between clusters in average covariate values addresses all concerns with cross-cluster comparisons.
Edit intent: clarity
Original:  These methods imply that linearly adjusting for differences between clusters in average covariate values addresses all concerns with cross-cluster comparisons. Instead, we consider an exponential family structure on the within-cluster distribution of covariates and treatments that implies that a low-dimensional sufficient statistic can summarize the empirical distribution, where this sufficient statistic may include functions of the data beyond average covariate values.
Revised:  These methods imply that linearly adjusting for differences between clusters in average covariate values addresses all concerns with cross-group comparisons. We start by making two observations. First we note that the fixed effect method in effect adjusts only for differences between groups by adjusting for the average of covariate values and average treatment. Second, we note that weighting by the inverse of the propensity score would remove biases for comparisons between treated and control units under the fixed effect set up. We then develop three generalizations of the fixed effect approach based on these two observations. First, we suggest more general, nonlinear, adjustments for the average covariate values.
Edit intent: meaning-changed
Original:  To illustrate our approach we study panel data modelswhere the distribution of individual effects may be misspecifiedand the number of time periods is small , and we revisit the structural evaluation of a conditional cash transfer program in Mexico .
Revised:  As illustrations, we study two binary choice models: a cross-sectional model where the error distribution is misspecified, and a dynamic panel data model where the distribution of individual effects may be misspecifiedand the number of time periods is small , and we revisit the structural evaluation of a conditional cash transfer program in Mexico .
Edit intent: meaning-changed