wanyu/IteraTeR_full_sent huggingface dataset:
Original: We develop an axiomatic theory of costly information acquisition . Our axioms capture the idea of constant marginal costs in information production: the cost of generating two independent signals is the sum of their costs, and the cost of generating a signal with probability half equals half the costof generating it deterministically .
Revised: We develop an axiomatic theory of information acquisition that captures the idea of constant marginal costs in information production: the cost of generating two independent signals is the sum of their costs, and the cost of generating a signal with probability half equals half the costof generating it deterministically .
Edit intent: clarity
Original:  Our axioms capture the idea of constant marginal costs in information production: the cost of generating two independent signals is the sum of their costs, and the cost of generating a signal with probability half equals half the costof generating it deterministically .
Revised:  Our axioms capture the idea of constant marginal costs in information production: the cost of generating two independent signals is the sum of their costs, and generating a signal with probability half equals half the costof generating it deterministically .
Edit intent: fluency
Original:  Our axioms capture the idea of constant marginal costs in information production: the cost of generating two independent signals is the sum of their costs, and the cost of generating a signal with probability half equals half the costof generating it deterministically .
Revised:  Our axioms capture the idea of constant marginal costs in information production: the cost of generating two independent signals is the sum of their costs, and the cost of generating a signal with probability half costs half its original cost .
Edit intent: clarity
Original:  These results make use of the connection between bundled crossings and graph genus . In the second setting, we bundle a given drawing to minimize the number of bundled crossings. We investigate storyline visualizations (for which the bundled drawing problem has been well studied). We show that testing if a given visualization with m characters can be bundled into k crossings is FPT in~m with singly-exponential runtime .
Revised:  These results make use of the connection between bundled crossings and graph genus .
Edit intent: coherence
Original:  By varying the Calcium concentration and the viscosity of the Endolymph solution , we are able to modulate the degree of chaos  in the hair cell dynamics . We find that the hair cell is most sensitive to a stimulus of small amplitude when it is poised in the weakly chaotic regime.
Revised:  By varying the Calcium concentration and the viscosity of the Endolymph solution , we are able to modulate the degree of chaos in the hair bundle dynamics in vitro. We consistently find that the hair cell is most sensitive to a stimulus of small amplitude when it is poised in the weakly chaotic regime.
Edit intent: clarity
Original:  The graphical simulation tool is implemented using the React library for the fancy UI elements of the Node.js framework and is available as a  web application at URL  The goals of this paper are to showcase the demonstrative capabilities of the tool for instruction, share student experiences in developing the engine underlying the simulation, and to inspire its use by other educators.
Revised:  The graphical simulation tool is implemented using the React library for the fancy ui elements of the Node.js framework and is available as a web application at URL The goals of this paper are to showcase the demonstrative capabilities of the tool for instruction, share student experiences in developing the engine underlying the simulation, and to inspire its use by other educators.
Edit intent: fluency
Original:  The graphical simulation tool is implemented using the React library for the fancy UI elements of the Node.js framework and is available as a  web application at URL  The goals of this paper are to showcase the demonstrative capabilities of the tool for instruction, share student experiences in developing the engine underlying the simulation, and to inspire its use by other educators.
Revised:  The graphical simulation tool is implemented using the React library for the fancy UI elements of the Node.js framework and is available as a single page web application at URL The goals of this paper are to showcase the demonstrative capabilities of the tool for instruction, share student experiences in developing the engine underlying the simulation, and to inspire its use by other educators.
Edit intent: meaning-changed
Original:  The graphical simulation tool is implemented using the React library for the fancy UI elements of the Node.js framework and is available as a  web application at URL  The goals of this paper are to showcase the demonstrative capabilities of the tool for instruction, share student experiences in developing the engine underlying the simulation, and to inspire its use by other educators.
Revised:  The graphical simulation tool is implemented using the React library for the fancy UI elements of the Node.js framework and is available as a web application at URL Assigning the development of the underling text-based simulation engine, on which the graphical simulator runs, to students as a course project is also an effective approach to teach students the concepts. The goals of this paper are to showcase the demonstrative capabilities of the tool for instruction, share student experiences in developing the engine underlying the simulation, and to inspire its use by other educators.
Edit intent: meaning-changed
Original:  Here we calculate the fitness dependence of the fixation-time distribution for the Moran Birth-death process in populations modeled by two extreme networks: the complete graph and the one-dimensional ring lattice, each of which admits an exact solution in the limit of large network size.
Revised:  Here we calculate the fitness dependence of the fixation-time distribution for the Moran Birth-death process in populations modeled by two extreme networks: the complete graph and the one-dimensional ring lattice, obtaining exact solutions in the limit of large network size.
Edit intent: clarity
Original: When using risk or dependence measures based on a given underlying model, it is essential to be able to quantify the sensitivity or robustness of these measures with respect to the model parameters . In this paper, we consider an underlying model which is popular in spatial extremes , the Smith max-stable random field.
Revised: When using risk or dependence measures based on a given underlying model, it is essential to be able to quantify the sensitivity or robustness of these measures with respect to the spatial dependence parameters of those fields. Max-stable fields, such as the Brown--Resnick and Smith fields, are very popular in spatial extremes , the Smith max-stable random field.
Edit intent: meaning-changed
Original:  In this paper, we consider an underlying model which is popular in spatial extremes , the Smith max-stable random field.
Revised:  In this paper, we consider an underlying model which is popular in spatial extremes . We focus on the two most popular unbiased stochastic derivative estimation approaches: the likelihood ratio method (LRM) and the infinitesimal perturbation analysis (IPA). LRM requires the multivariate density of the max-stable random field.
Edit intent: meaning-changed
Original:  In this paper, we consider an underlying model which is popular in spatial extremes , the Smith max-stable random field. We study the sensitivity properties of risk or dependence measures based on the values of this field at a finite number of locations. Max-stable fields play a key role, e.g., in the modelling of natural disasters. As their multivariate density is generally not available for more than three locations, the likelihood ratio method cannot be used to estimate the derivatives of the risk measures with respect to the model parameters .
Revised:  In this paper, we consider an underlying model which is popular in spatial extremes , the Smith max-stable field to be explicit, and IPA necessitates the computation of the derivative with respect to the model parameters .
Edit intent: coherence
Original:  As their multivariate density is generally not available for more than three locations, the likelihood ratio method cannot be used to estimate the derivatives of the risk measures with respect to the model parameters . Thus, we focus on a pathwise method, the infinitesimal perturbation analysis (IPA). We provide a convenient and tractable sufficient condition for performing IPA , which is intricate to obtain because of the very structure of max-stable fields involving pointwise maxima over an infinite number of random functions.
Revised:  As their multivariate density is generally not available for more than three locations, the likelihood ratio method cannot be used to estimate the derivatives of the risk measures with respect to the parameters for each simulated value. We propose convenient and tractable sufficient condition for performing IPA , which is intricate to obtain because of the very structure of max-stable fields involving pointwise maxima over an infinite number of random functions.
Edit intent: clarity
Original:  We provide a convenient and tractable sufficient condition for performing IPA , which is intricate to obtain because of the very structure of max-stable fields involving pointwise maxima over an infinite number of random functions.
Revised:  We provide a convenient and tractable conditions ensuring the validity of LRM and IPA in the cases of the Brown--Resnick and Smith field, respectively. Obtaining such conditions is intricate owing to the very structure of max-stable fields involving pointwise maxima over an infinite number of random functions.
Edit intent: meaning-changed
Original:  We provide a convenient and tractable sufficient condition for performing IPA , which is intricate to obtain because of the very structure of max-stable fields involving pointwise maxima over an infinite number of random functions. IPA enables the consistent estimation of the considered measures' derivatives with respect to the parameters characterizing the spatial dependence. We carry out a simulation study which shows that the approach performs well in various configurations  .
Revised:  We provide a convenient and tractable sufficient condition for performing IPA , which is intricate to obtain because of the very structure of max-stable fields . Then we focus on risk and dependence measures, which constitute one of the several frameworks where our theoretical results can be useful. We perform a simulation study which shows that the approach performs well in various configurations .
Edit intent: meaning-changed
Original:  Learning hierarchical distributed representations of the raw data in an automatic manner (e.g. risk taking behavior), it uncovers generative features that determine the target (e.g., trader's profitability), avoids manual feature engineering, and is more robust toward change (e.g. dynamic market conditions).
Revised:  Learning hierarchical distributed representations of the data in an automatic manner (e.g. risk taking behavior), it uncovers generative features that determine the target (e.g., trader's profitability), avoids manual feature engineering, and is more robust toward change (e.g. dynamic market conditions).
Edit intent: clarity
Original:  The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over powerful machine learning benchmarks. Empirical results suggest that the financial institution which provided the data can increase annual profits by 16\% through implementing a deep learning based risk management policy. The findings demonstrate the potential of applying deep learning methods for management science problems in finance, marketing, and accounting .
Revised:  The results of employing a deep network for operational risk forecasting confirm the feature learning capability of deep learning, provide guidance on designing a suitable network architecture and demonstrate the superiority of deep learning over machine learning and rule-based benchmarks .
Edit intent: clarity
Original:  Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances using machine learning for discovering bioactive ligands . Machine learning , which is centered around the development and applications of algorithms that can learn from data automatically , offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies.
Revised:  Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances for automating the discovery of bioactive ligands using machine learning . Machine learning , which is centered around the development and applications of algorithms that can learn from data automatically , offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies.
Edit intent: clarity
Original:  Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances using machine learning for discovering bioactive ligands . Machine learning , which is centered around the development and applications of algorithms that can learn from data automatically , offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies.
Revised:  Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances using machine learning for discovering bioactive ligands . Machine learning is centered around the development and applications of algorithms that can learn from data automatically , offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies.
Edit intent: coherence
Original:  Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances using machine learning for discovering bioactive ligands . Machine learning , which is centered around the development and applications of algorithms that can learn from data automatically , offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies.
Revised:  Along with highlighting the recent advances in structure-based ligand discovery, including docking and molecular dynamics, this article focuses on the latest advances using machine learning for discovering bioactive ligands . Machine learning , which is centered around the development and applications of algorithms that can learn from data automatically . Such an approach offers immense opportunities for bioactivity prediction as well as quantitative structure-activity relationship studies.
Edit intent: clarity
Original:  After the most recent and successful applications of machine learning for bioactive ligand discovery are reviewed, this article concludes with an outlook on deep learning methods that are capable of  extracting salient information from structural data as a promising future direction for  bioactive ligand discovery.
Revised:  This review describes the most recent and successful applications of machine learning for bioactive ligand discovery are reviewed, this article concludes with an outlook on deep learning methods that are capable of extracting salient information from structural data as a promising future direction for bioactive ligand discovery.
Edit intent: meaning-changed
Original:  After the most recent and successful applications of machine learning for bioactive ligand discovery are reviewed, this article concludes with an outlook on deep learning methods that are capable of  extracting salient information from structural data as a promising future direction for  bioactive ligand discovery.
Revised:  After the most recent and successful applications of machine learning for bioactive ligand discovery , concluding with an outlook on deep learning methods that are capable of extracting salient information from structural data as a promising future direction for bioactive ligand discovery.
Edit intent: clarity
Original:  After the most recent and successful applications of machine learning for bioactive ligand discovery are reviewed, this article concludes with an outlook on deep learning methods that are capable of  extracting salient information from structural data as a promising future direction for  bioactive ligand discovery.
Revised:  After the most recent and successful applications of machine learning for bioactive ligand discovery are reviewed, this article concludes with an outlook on deep learning methods that are capable of automatically extracting salient information from structural data as a promising future direction for bioactive ligand discovery.
Edit intent: clarity
Original:  After the most recent and successful applications of machine learning for bioactive ligand discovery are reviewed, this article concludes with an outlook on deep learning methods that are capable of  extracting salient information from structural data as a promising future direction for  bioactive ligand discovery.
Revised:  After the most recent and successful applications of machine learning for bioactive ligand discovery are reviewed, this article concludes with an outlook on deep learning methods that are capable of extracting salient information from structural data as a promising future direction for rapid and efficient bioactive ligand discovery.
Edit intent: clarity
Original: We propose a general null model for persistent homology barcodes from a point cloud, to test for example acyclicity in simplicial complexes generated from point clouds. One advantage of the null model we propose is efficiency in generating a null model that applies to a broad set of hypothesis testing procedures.
Revised: Multiple hypothesis testing requires a control procedure. Simply increasing simulations or permutations to meet a Bonferroni-style threshold is prohibitively expensive. In this paper we propose a null model that applies to a broad set of hypothesis testing procedures.
Edit intent: meaning-changed
Original:  One advantage of the null model we propose is efficiency in generating a null model that applies to a broad set of hypothesis testing procedures. The second key idea in this paper is using the null model to address multiple hypothesis testing via control of family-wise error rates and false discovery rates .
Revised:  One advantage of the null model we propose is efficiency in generating a null model based approach to testing for acyclicity, coupled with a Family-Wise Error Rate (FWER) control method that does not suffer from these computational costs. We adapt an False Discovery Rate (FDR) control approach to the topological setting, and show it to be compatible both with our null model approach and with previous approaches to hypothesis testing in persistent homology. By extending a limit theorem for persistent homology on samples from point processes, we provide theoretical validation for our FWER and FDR control methods .
Edit intent: meaning-changed
Original: Multiple hypothesis testing requires a control procedure . Simply increasing simulations or permutations to meet a Bonferroni-style threshold is prohibitively expensive.
Revised: In this paper we propose a computationally efficient multiple hypothesis testing procedure for persistence homology. Most current approaches to hypothesis testing using persistence homology is based on resampling or permutation procedures, this is because there exists little distribution theory that provides a natural null model for persistence diagrams . Simply increasing simulations or permutations to meet a Bonferroni-style threshold is prohibitively expensive.
Edit intent: meaning-changed
Original:  In this paper we propose a null model based  approach to testing for acyclicity, coupled with a Family-Wise Error Rate (FWER) control method that does not suffer from these computational costs .
Revised:  In this paper we propose a null model based based approach to testing for acyclicity, coupled with a Family-Wise Error Rate (FWER) control method that does not suffer from these computational costs .
Edit intent: fluency
Original:  In this paper we propose a null model based  approach to testing for acyclicity, coupled with a Family-Wise Error Rate (FWER) control method that does not suffer from these computational costs .
Revised:  In this paper we propose a null model based approach to testing for acyclicity, coupled with a Family-Wise Error Rate (FWER) control method that does not suffer from the computational costs associated to resampling and permutation based approaches .
Edit intent: meaning-changed
Original:  We adapt an False Discovery Rate (FDR) control approach to the topological setting, and show it to be compatible both with our null model approach and with previous approaches to hypothesis testing in persistent homology.
Revised:  We adapt standard False Discovery Rate (FDR) control approach to the topological setting, and show it to be compatible both with our null model approach and with previous approaches to hypothesis testing in persistent homology.
Edit intent: meaning-changed
Original:  By extending a limit theorem for persistent homology on samples from point processes, we provide theoretical validation for our FWER and FDR control methods .
Revised:  A key idea in our paper is that one can compute an empirical distribution under a null hypothesis model that can be used for hypothesis testing in the same way that a normal or t-distribution is used in classical statistics. A argument for the use of this empirical null is based on simulations and limit theorems for persistent homology on samples from point processes, we provide theoretical validation for our FWER and FDR control methods .
Edit intent: meaning-changed
Original:  For this application, Bitler, Gelbach, and Hoynes (2017) show that standard CATE estimators fail to provide evidence for theoretical labour supply predictions. Therefore, this is an interesting benchmark to showcase the value added by using CML methods .
Revised:  This experiment entails a mix of positive and negative work incentives. Previous studies show that it is hard to tackle the effect heterogeneity of Jobs First by means of CATEs .
Edit intent: meaning-changed
Original:  The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in artificial neural networks (ANNs), while the latter either be considered biologically implausible or exhibit poor performance.
Revised:  The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in artificial neural networks (ANNs), whereas the latter are either considered to be biologically implausible or exhibit poor performance.
Edit intent: fluency
Original:  By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST  ).
Revised:  By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset ).
Edit intent: clarity