wanyu/IteraTeR_full_sent huggingface dataset:
Original:  A recent advancement in network science, multilayer network analysis, the study of network structures of multiple interconnected `layers' , offers a novel way to represent and analyze the structure of animal behavior, and help strengthen links to broader ecological and evolutionary contexts . We outline the potential uses of these new methods at individual-, group-, population-, and evolutionary-levels, and we highlight their potential to advance behavioral ecology research .
Revised:  A recent advancement in network science, multilayer network analysis, the study of network structures of multiple interconnected `layers' , offers a novel way to represent and analyze the structure of animal behavior, and help strengthen links to broader ecological and evolutionary contexts and processes. In this article, we describe how to encode animal behavior data as different types of multilayer networks, and we link recently-developed multilayer methods to individual-, group-, population-, and evolutionary-levels, and we highlight their potential to advance behavioral ecology research .
Edit intent: clarity
Original:  We outline the potential uses of these new methods at individual-, group-, population-, and evolutionary-levels, and we highlight their potential to advance behavioral ecology research . This novel quantitative approach makes it possible to address classic research questions from a new perspective and opens a diversity of new questions that previously have been out of reach.
Revised:  We outline the potential uses of these new methods at individual-, group-, population-, and evolutionary-levels, and we highlight their potential to advance behavioral ecology research , including examples that demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. These new insights have consequences for ecological processes such as disease transmission. We also discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we outline methodological challenges for the application of these approaches. Multilayer network analysis offers a promising approach for tackling classical research questions from a new perspective and opens a diversity of new questions that previously have been out of reach.
Edit intent: meaning-changed
Original:  A recent formalism, known as multilayer network analysis , has advanced the study of multifaceted networked systems and offers a novel way to represent and analyse animal behaviour as connected ` layers' of interactions.
Revised:  A recent formalism, known as multilayer network analysis , has advanced the study of multifaceted networked systems and offers a novel way to represent and analyse animal behaviour as connected ` layers' of interactions.
Edit intent: fluency
Original:  A recent formalism, known as multilayer network analysis , has advanced the study of multifaceted networked systems and offers a novel way to represent and analyse animal behaviour as connected ` layers' of interactions.
Revised:  A recent formalism, known as multilayer network analysis , has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour as connected ` layers' of interactions.
Edit intent: meaning-changed
Original:  A recent formalism, known as multilayer network analysis , has advanced the study of multifaceted networked systems and offers a novel way to represent and analyse animal behaviour as connected ` layers' of interactions.
Revised:  A recent formalism, known as multilayer network analysis , has advanced the study of multifaceted networked systems and offers a novel way to represent and analyse animal behaviour as connected ' layers' of interactions.
Edit intent: fluency
Original:  Many complex interacting systems in nature , such as earthquakes , are described by power laws. In statistical physics, fluctuations for systems near the critical point follow a power law distribution. The exponents associated with the power law for fluctuations can be used to categorize the systems into specific universality classes. In economics, growth rates for firms depend on different factors for different industries .
Revised:  Many complex interacting systems in nature , such as earthquakes .
Edit intent: clarity
Original:   Here, we show that fluctuations in growth for companies follow a power law with very similar scaling exponents, irrespective of the economic sector.
Revised:  Remarkably, work in econophysics has provided evidence that the statistical properties of the growth of business firms follow the same sorts of power laws that characterize physical systems near their critical points. Given how economies change over time, whether these statistical properties are persistent, robust, and universal like those of physical systems remains an open question. Here, we show that fluctuations in growth for companies follow a power law with very similar scaling exponents, irrespective of the economic sector.
Edit intent: meaning-changed
Original:   Here, we show that fluctuations in growth for companies follow a power law with very similar scaling exponents, irrespective of the economic sector. Our findings hold for many industries across all available data .
Revised:  Here, we show that the scaling properties of firm growth previously demonstrated for publicly-traded U.S. manufacturing firms from 1974 to 1993 apply to the same sorts of firms from 1993 to 2015, to firms in other broad sectors (such as materials), and to firms in new sectors (such as Internet services). We measure virtually the same scaling exponent for manufacturing for the 1993 to 2015 period as for the 1974 to 1993 period and virtually the same scaling exponent for other sectors as for manufacturing .
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Original:  Furthermore, we show that fluctuations of the growth rate for new industries URLanize into a power law distribution over relatively short time scales . Our results provide the first strong evidence of statistical mechanical universality in economic systems, and can be used as an empirical test for theories of microeconomic growth .
Revised:  Furthermore, we show that fluctuations of the growth rate for new industries URLanize into a power law distribution over relatively short time scales .
Edit intent: coherence
Original:  In this paper we  show that there are exactly four non-circularizable arrangements of 5 pseudocircles , exactly one of them is intersecting. For n=6, we show that there are exactly three non-circularizable digon-free intersecting arrangements.
Revised:  In this paper we show that there are exactly four non-circularizable arrangements of 5 pseudocircles , exactly one of them was known before). We also show that exactly three out of the 2131 digon-free intersecting arrangements.
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Original:  We also have some additional examples of non-circularizable arrangements of 6 pseudocircles . The proofs of non-circularizability use various techniques, most depend on incidence theorems , others use arguments involving metric properties of arrangements of planes, or angles in planar figures .
Revised:  We also have some additional examples of non-circularizable arrangements of 6 pseudocircles are non-circularizable. Most of our non-circularizability use various techniques, most depend on incidence theorems , others use arguments involving metric properties of arrangements of planes, or angles in planar figures .
Edit intent: clarity
Original:  The proofs of non-circularizability use various techniques, most depend on incidence theorems , others use arguments involving metric properties of arrangements of planes, or angles in planar figures .
Revised:  The proofs of non-circularizability use various techniques, most depend on incidence theorems like Miquel's. In other cases we contradict circularizability by considering a continuous deformation where the circles of an assumed circle representation grow or shrink in a controlled way .
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Original:  The claims that we have all non-circularizable arrangements with the given properties are based on a program that generated all connected arrangements of n\leq 6 pseudocircles and all intersecting arrangements of n\leq 7 pseudocircles . Given the complete lists of arrangements, we used heuristics to find circle representations.
Revised:  The claims that we have all non-circularizable arrangements with the given properties are based on a program that generated all arrangements up to a certain size . Given the complete lists of arrangements, we used heuristics to find circle representations.
Edit intent: meaning-changed
Original:  Examples where the heuristics failed had to be examined by hand . One of the digon-free intersecting arrangements of pseudocircles with n=6 is particularly interesting. It only has 8 triangles and occurs as a subarrangement of every digon-free intersecting arrangement with less than 2n-4 triangles for n=7,8,9. Hence it may be true that every digon-free intersecting arrangement of circles contains at least 2n-4 triangles .
Revised:  Examples where the heuristics failed had to be examined by hand .
Edit intent: coherence
Original:  Here, we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise , in the Langevin framework, on the states and parameters  .
Revised:  Here, we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise on the states and parameters .
Edit intent: clarity
Original:  Here, we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise , in the Langevin framework, on the states and parameters  .
Revised:  Here, we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise , in the Langevin framework, on the states and parameters , as given in the Langevin framework .
Edit intent: meaning-changed
Original:  We compare the performance of this framework with different fixed values of process noise covariance in biomolecular system models, including an oscillator model, as well as in experimentally measured growth rate of E. coli, finding that it has a similar performance with the advantage that the noise covariance does not have to be guessed .
Revised:  We compare the performance of this framework with different fixed values of process noise covariance in biomolecular system models, including an oscillator model, as well as in experimentally measured data for a negative transcriptional feedback circuit .
Edit intent: meaning-changed
Original:  The indicators  are derived from  Census 2011 [The Ministry of Home Affairs, Government of India, 2011] and  NFHS-4 [The Ministry of Health and Family Welfare, Government of India, 2016] survey data.
Revised:  The indicators that we use are derived from Census 2011 [The Ministry of Home Affairs, Government of India, 2011] and NFHS-4 [The Ministry of Health and Family Welfare, Government of India, 2016] survey data.
Edit intent: meaning-changed
Original:  The indicators  are derived from  Census 2011 [The Ministry of Home Affairs, Government of India, 2011] and  NFHS-4 [The Ministry of Health and Family Welfare, Government of India, 2016] survey data.
Revised:  The indicators are derived from the Census 2011 [The Ministry of Home Affairs, Government of India, 2011] and NFHS-4 [The Ministry of Health and Family Welfare, Government of India, 2016] survey data.
Edit intent: fluency
Original:  The indicators  are derived from  Census 2011 [The Ministry of Home Affairs, Government of India, 2011] and  NFHS-4 [The Ministry of Health and Family Welfare, Government of India, 2016] survey data.
Revised:  The indicators are derived from Census 2011 [The Ministry of Home Affairs, Government of India, 2011] and the NFHS-4 [The Ministry of Health and Family Welfare, Government of India, 2016] survey data.
Edit intent: fluency
Original:  The indicators that we use are derived from the Census 2011 The Ministry of Home Affairs, Government of India, 2011%DIFDELCMD < ] %%% and the NFHS-4 The Ministry of Health and Family Welfare, Government of India, 2016%DIFDELCMD < ] %%% survey data.
Revised:  The The Ministry of Home Affairs, Government of India, 2011%DIFDELCMD < ] %%% and the NFHS-4 The Ministry of Health and Family Welfare, Government of India, 2016%DIFDELCMD < ] %%% survey data.
Edit intent: coherence
Original:  The indicators that we use are derived from the Census 2011 The Ministry of Home Affairs, Government of India, 2011%DIFDELCMD < ] %%% and the NFHS-4 The Ministry of Health and Family Welfare, Government of India, 2016%DIFDELCMD < ] %%% survey data.
Revised:  The indicators that we use are derived from the Census 2011 %DIFDELCMD < ] %%% and the NFHS-4 The Ministry of Health and Family Welfare, Government of India, 2016%DIFDELCMD < ] %%% survey data.
Edit intent: clarity
Original:  The indicators that we use are derived from the Census 2011 The Ministry of Home Affairs, Government of India, 2011%DIFDELCMD < ] %%% and the NFHS-4 The Ministry of Health and Family Welfare, Government of India, 2016%DIFDELCMD < ] %%% survey data.
Revised:  The indicators that we use are derived from the Census 2011 The Ministry of Home Affairs, Government of India, 2011%DIFDELCMD < ] %%% The Ministry of Health and Family Welfare, Government of India, 2016%DIFDELCMD < ] %%% survey data.
Edit intent: coherence
Original:  The indicators that we use are derived from the Census 2011 The Ministry of Home Affairs, Government of India, 2011%DIFDELCMD < ] %%% and the NFHS-4 The Ministry of Health and Family Welfare, Government of India, 2016%DIFDELCMD < ] %%% survey data.
Revised:  The indicators that we use are derived from the Census 2011 The Ministry of Home Affairs, Government of India, 2011%DIFDELCMD < ] %%% and the NFHS-4 %DIFDELCMD < ] %%% survey data.
Edit intent: coherence
Original:  The indicators that we use are derived from the Census 2011 The Ministry of Home Affairs, Government of India, 2011%DIFDELCMD < ] %%% and the NFHS-4 The Ministry of Health and Family Welfare, Government of India, 2016%DIFDELCMD < ] %%% survey data. We use a deep convolutional neural network to build a model for regression of asset indicators from satellite images. We show that the direct regression of asset indicators gives superior R2 scores compared to that of transfer learning through night light data, which is a popular proxy for economic development used world wide. We also use the asset prediction model for accurate transfer learning of other socio-economic and health indicators which are not intuitively related to observable features in satellite images, or are not always well correlated with each other.
Revised:  The indicators that we use are derived from the Census 2011 The Ministry of Home Affairs, Government of India, 2011%DIFDELCMD < ] %%% and the NFHS-4 The Ministry of Health and Family Welfare, Government of India, 2016%DIFDELCMD < ] %%% diverse set of indicators are often not intuitively related to observable features in satellite images, or are not always well correlated with each other.
Edit intent: clarity
Original:  We also use the asset prediction model for accurate transfer learning of other socio-economic and health indicators which are not intuitively related to observable features in satellite images, or are not always well correlated with each other.
Revised:  We also use the asset prediction model for accurate transfer learning of other socio-economic and health indicators which are not intuitively related to observable features in satellite images, and are not even always well correlated with each other.
Edit intent: clarity
Original:  The tool can be extended to monitor the progress of development of a regionover time, and to flag potential anomalies because of dissimilar outcomes due to different policy interventions in a geographic region by detecting sharp spatial discontinuities in the regression output .
Revised:  Our predictive tool is more accurate than using night light as a proxy, and can be used to predict missing data, smooth out noise in surveys, monitor development progress of a region, and flag potential anomalies because of dissimilar outcomes due to different policy interventions in a geographic region by detecting sharp spatial discontinuities in the regression output .
Edit intent: meaning-changed
Original:  The tool can be extended to monitor the progress of development of a regionover time, and to flag potential anomalies because of dissimilar outcomes due to different policy interventions in a geographic region by detecting sharp spatial discontinuities in the regression output .
Revised:  The tool can be extended to monitor the progress of development of a regionover time, and to flag potential anomalies . Finally, we use predicted variables to do robustness analysis of a regression study of high rate of stunting in India .
Edit intent: meaning-changed
Original: The general problem in topology optimization is correctly formulated as a double-\min mixed integer nonlinear programming (MINLP) problembased on the minimum total potential energy principle. It is proved that for linear elastic structures, the alternative iteration leads to a Knapsack problem, which is considered to be NP-hard in  computer science.
Revised: The general problem in topology optimization is correctly formulated as a bi-level knapsack problem, which is considered to be NP-hard in computer science.
Edit intent: meaning-changed
Original:  We expand this work by providing algorithms to combine and aggregate Trends data and show how time series clustering can be used to analyze competition .
Revised:  We expand this work by providing algorithms to combine and aggregate search volume data, so that the resulting data is both consistent over time and consistent between data series. We give a brand equity example, where Google Trends is used to analyze shopping data for 100 top ranked brands and these data are used to nowcast economic variables. We describe the importance of out of sample prediction and show how principal component analysis (PCA) can be used to analyze competition .
Edit intent: meaning-changed
Original:  We expand this work by providing algorithms to combine and aggregate Trends data and show how time series clustering can be used to analyze competition .
Revised:  We expand this work by providing algorithms to combine and aggregate Trends data and show how time series clustering can be used to improve the signal to noise ratio and prevent overfitting in nowcasting models. We give a finance example, where exploratory data analysis and classification is used to analyze the relationship between Google Trends searches and stock prices .
Edit intent: meaning-changed
Original:  Encoder-decoder networks, which are special class of RNNs suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes .
Revised:  Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes .
Edit intent: meaning-changed
Original:  Encoder-decoder networks, which are special class of RNNs suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes . The proposed method does not rely on pretreatment covariates and encoder-decoder networks are capable of learning nonconvex combinations of control unit outcomes to construct a counterfactual.
Revised:  Encoder-decoder networks, which are special class of RNNs suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs. Unlike the synthetic control method, the proposed method does not rely on pretreatment covariates and encoder-decoder networks are capable of learning nonconvex combinations of control unit outcomes to construct a counterfactual.
Edit intent: meaning-changed
Original:  The proposed method does not rely on pretreatment covariates and encoder-decoder networks are capable of learning nonconvex combinations of control unit outcomes to construct a counterfactual. To demonstrate the proposed method , I extend a field experiment studying the effect of radio advertisements on electoral competition to observational time-series .
Revised:  The proposed method does not rely on pretreatment covariates and encoder-decoder networks are capable of learning nonconvex combinations of control units, and can handle multiple treated units. Encoder-decoder networks outperform the synthetic control method in simulated and empirical data applications .
Edit intent: meaning-changed
Original: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Revised: This paper proposes an alternative to the synthetic control method (SCM) for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Edit intent: meaning-changed
Original: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Revised: This paper proposes a method for estimating the effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Edit intent: clarity
Original: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Revised: This paper proposes a method for estimating the causal effect of a policy intervention on an outcome over time . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
Edit intent: clarity