wanyu/IteraTeR_full_sent huggingface dataset:
Original:  amounts to the effective size of its event space, and can be quantified using the R\'enyi family of indices (also known as Hill numbers in ecology or Hannah-Kay indices in economics), which are indexed by an elasticity parameter q \geq 0. Importantly, under these indices, the heterogeneity of a composite system (the \gamma-heterogeneity) is decomposable into heterogeneity arising from variation within and between component subsystems (the \alpha- and \beta -heterogeneities , respectively).
Revised:  amounts to the effective size of its event space, and can be quantified using the R\'enyi family of indices (also known as Hill numbers in ecology or Hannah-Kay indices in economics), which are indexed by an elasticity parameter q \geq 0. Importantly, under these indices, the heterogeneity of a composite system (the \gamma-heterogeneity) is decomposable into heterogeneity arising from variation within and between component subsystems (the \alpha- and \beta -heterogeneity , respectively).
Edit intent: clarity
Original:  We show that under non-parametric pooling (where \gamma-heterogeneity must typically be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Revised:  Under non-parametric pooling (where \gamma-heterogeneity must typically be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Edit intent: coherence
Original:  We show that under non-parametric pooling (where \gamma-heterogeneity must typically be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Revised:  We show that under non-parametric pooling , the \gamma-heterogeneity must typically be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Edit intent: clarity
Original:  We show that under non-parametric pooling (where \gamma-heterogeneity must typically be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Revised:  We show that under non-parametric pooling (where \gamma-heterogeneity must often be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Edit intent: clarity
Original:  We show that under non-parametric pooling (where \gamma-heterogeneity must typically be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Revised:  We show that under non-parametric pooling (where \gamma-heterogeneity must typically be estimated numerically , but the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Edit intent: coherence
Original:  We show that under non-parametric pooling (where \gamma-heterogeneity must typically be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Revised:  We show that under non-parametric pooling (where \gamma-heterogeneity must typically be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Edit intent: clarity
Original:  We show that under non-parametric pooling (where \gamma-heterogeneity must typically be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0, and \beta-heterogeneity amounts to the discrete number of distinct mixture components in the system.
Revised:  We show that under non-parametric pooling (where \gamma-heterogeneity must typically be estimated numerically ), the multiplicative decomposition holds such that \gamma \geq \alpha for all values of the elasticity parameter q > 0.
Edit intent: clarity
Original:  Conversely, under parametric pooling (as in a Gaussian mixed-effects model), which facilitates efficient analytical computation of \gamma-heterogeneity , we show that the \gamma \geq \alpha condition holds  only at q=1.
Revised:  Conversely, under parametric pooling , \gamma-heterogeneity , we show that the \gamma \geq \alpha condition holds only at q=1.
Edit intent: clarity
Original:  Conversely, under parametric pooling (as in a Gaussian mixed-effects model), which facilitates efficient analytical computation of \gamma-heterogeneity , we show that the \gamma \geq \alpha condition holds  only at q=1.
Revised:  Conversely, under parametric pooling (as in a Gaussian mixed-effects model), which facilitates efficient analytical computation of \gamma-heterogeneity can be computed efficiently in closed-form, but the \gamma \geq \alpha condition holds only at q=1.
Edit intent: meaning-changed
Original:  Conversely, under parametric pooling (as in a Gaussian mixed-effects model), which facilitates efficient analytical computation of \gamma-heterogeneity , we show that the \gamma \geq \alpha condition holds  only at q=1.
Revised:  Conversely, under parametric pooling (as in a Gaussian mixed-effects model), which facilitates efficient analytical computation of \gamma-heterogeneity , we show that the \gamma \geq \alpha condition holds reliably only at q=1.
Edit intent: clarity
Original: We aim to improve question answering (QA) by decomposing hard questions into easier sub-questions that existing QA systems can answer. Since collecting labeled decompositions is cumbersome, we propose an unsupervised approach to produce sub-questions . Specifically, by leveraging >10M questions from Common Crawl, we learn to map from the distribution of multi-hop questions to the distribution of single-hop sub-questions.
Revised: We aim to improve question answering (QA) by decomposing hard questions into easier sub-questions that existing QA systems are capable of answering. Since labeling questions with decompositions is cumbersome, we propose an unsupervised approach to produce sub-questions . Specifically, by leveraging >10M questions from Common Crawl, we learn to map from the distribution of multi-hop questions to the distribution of single-hop sub-questions.
Edit intent: clarity
Original:  Since collecting labeled decompositions is cumbersome, we propose an unsupervised approach to produce sub-questions . Specifically, by leveraging >10M questions from Common Crawl, we learn to map from the distribution of multi-hop questions to the distribution of single-hop sub-questions.
Revised:  Since collecting labeled decompositions is cumbersome, we take an unsupervised approach to produce sub-questions . Specifically, by leveraging >10M questions from Common Crawl, we learn to map from the distribution of multi-hop questions to the distribution of single-hop sub-questions.
Edit intent: clarity
Original:  Since collecting labeled decompositions is cumbersome, we propose an unsupervised approach to produce sub-questions . Specifically, by leveraging >10M questions from Common Crawl, we learn to map from the distribution of multi-hop questions to the distribution of single-hop sub-questions.
Revised:  Since collecting labeled decompositions is cumbersome, we propose an unsupervised approach to produce sub-questions , also enabling us to leverage millions of questions from the internet. Specifically, we propose an algorithm for One-to-N Unsupervised Sequence transduction (ONUS) that learns to map one hard, multi-hop questions to the distribution of single-hop sub-questions.
Edit intent: meaning-changed
Original:  Since collecting labeled decompositions is cumbersome, we propose an unsupervised approach to produce sub-questions . Specifically, by leveraging >10M questions from Common Crawl, we learn to map from the distribution of multi-hop questions to the distribution of single-hop sub-questions.
Revised:  Since collecting labeled decompositions is cumbersome, we propose an unsupervised approach to produce sub-questions . Specifically, by leveraging >10M questions from Common Crawl, we learn to map from the distribution of multi-hop question to many simpler, single-hop sub-questions.
Edit intent: meaning-changed
Original:  We answer sub-questions with an off-the-shelf QA model and incorporate the resulting answers in a downstream, multi-hop QA system. On a popular multi-hop QA dataset, HotpotQA, we show large improvements over a strong baseline , especially on adversarial and out-of-domain questions.
Revised:  We answer sub-questions with an off-the-shelf QA model and incorporate the resulting answers to a recomposition model that combines them into a final answer. We show large QA improvements on HotpotQA over a strong baseline , especially on adversarial and out-of-domain questions.
Edit intent: meaning-changed
Original:  On a popular multi-hop QA dataset, HotpotQA, we show large improvements over a strong baseline , especially on adversarial and out-of-domain questions. Our method is generally applicable and automatically learns to decompose questions of different classes , while matching the performance of decomposition methods that rely heavily on hand-engineering and annotation .
Revised:  On a popular multi-hop QA dataset, HotpotQA, we show large improvements over a strong baseline , especially on adversarial and out-of-domain , and multi-hop dev sets. ONUS automatically learns to decompose questions of different classes , while matching the performance of decomposition methods that rely heavily on hand-engineering and annotation .
Edit intent: meaning-changed
Original:  Our method is generally applicable and automatically learns to decompose questions of different classes , while matching the performance of decomposition methods that rely heavily on hand-engineering and annotation .
Revised:  Our method is generally applicable and automatically learns to decompose questions of different classes , while matching the utility of supervised and heuristic decomposition methods for QA and exceeding those methods in fluency. Qualitatively, we find that using sub-questions is promising for shedding light on why a QA system makes a prediction .
Edit intent: meaning-changed
Original:  Molecular dynamics calculations fully confirm the stability in the 3CL-pro binding pocket of  most potent binder identified by docking, namely a chlorophenyl-pyridyl-carboxamide derivative.
Revised:  Molecular dynamics calculations fully confirm the stability in the 3CL-pro binding pocket of the most potent binder identified by docking, namely a chlorophenyl-pyridyl-carboxamide derivative.
Edit intent: fluency
Original:  This granularity makes it difficult to produce overviews of LD content . We therefore introduce the concept of path-based summaries  which carries a higher level of semantics for data producers.
Revised:  This granularity makes it difficult for data producers to have meaningful overviews of the content of their own datasets. We report a characterisation of LD producers' needs, and introduce the concept of path-based summaries which carries a higher level of semantics for data producers.
Edit intent: meaning-changed
Original:  We therefore introduce the concept of path-based summaries  which carries a higher level of semantics for data producers. We also introduce the tool Path Outlines to support LD producers in browsing path-based summaries of their datasets.
Revised:  We therefore introduce the concept of path-based summaries which carries a higher level of semantics , to meet their needs. We present the tool Path Outlines to support LD producers in browsing path-based summaries of their datasets.
Edit intent: coherence
Original:  We show that participants prefer Path Outlines , find it easier to understand, easier to use, faster, and lowering the number of tasks that users give-up before completing them .
Revised:  We show that Path Outlines is faster, leads to better task completion and less errors, and that participants prefer it, find it easier to understand, easier to use, faster, and lowering the number of tasks that users give-up before completing them .
Edit intent: meaning-changed
Original:  We investigate the relationships between citations , quality, and impact using a survey with an embedded experiment in which 12,670 authors in 15 academic fieldsdescribe about 25K specific referencing decisions. Results suggest that citation counts, when equated with quality and impact, are biased in opposite directions. First, experimentally exposing papers' actual citation counts during the survey causes respondents to perceive all but the top 10\% cited papers as of lower quality.
Revised:  We investigate high-influence and low-influence citations and the mechanisms producing them using 17,154 ground-truth citation types provided via survey by 9,380 authors systematically sampled across academic fields. Overall, 54\% of citations denote little-to-no influence and these citations are concentrated among low status (lightly cited) papers. In contrast, high-influence citations are concentrated among high status (highly cited) papers through a number of steps that resemble a pipeline. Authors discover highly cited papers earlier in their projects, more often through social contacts, and read them more closely. Papers' status, above and beyond any quality differences, directly helps determine their pipeline: experimentally revealing or hiding citation counts during the survey causes respondents to perceive all but the top 10\% cited papers as of lower quality.
Edit intent: meaning-changed
Original:  First, experimentally exposing papers' actual citation counts during the survey causes respondents to perceive all but the top 10\% cited papers as of lower quality. Because perceptions of quality are a key factor in citing decisions, citation counts are likely to endogenously cause more citing of top papers and equating them with quality overestimates the actual quality of those papers.
Revised:  First, experimentally exposing papers' actual citation counts during the survey shows that low counts cause lowered perceptions of quality are a key factor in citing decisions, citation counts are likely to endogenously cause more citing of top papers and equating them with quality overestimates the actual quality of those papers.
Edit intent: meaning-changed
Original:  Because perceptions of quality are a key factor in citing decisions, citation counts are likely to endogenously cause more citing of top papers and equating them with quality overestimates the actual quality of those papers. Conversely, 54\% of references had either zero or minor influence on authors who cite them, but references to highly citedpapers were about 200\% more likely to denote substantial impact. Equating citations with impact thus underestimates the impact of highly cited papers . Real citation practices thus reveal that citations are biased measures of quality and impact .
Revised:  Because perceptions of quality . Accounting for citation types thus reveals a "double status effect": in addition to affecting how often a work is cited, status affects how meaningfully it is cited. Consequently, highly cited papers . Real citation practices thus reveal that citations are biased measures of quality and impact .
Edit intent: coherence
Original:  This study investigates whether a machine-learning-based system can predict the rate of cognitive-decline in mildly cognitively impaired (MCI) patients by processing only the clinical and imaging data collected at the initial visit.
Revised: Purpose: This study investigates whether a machine-learning-based system can predict the rate of cognitive-decline in mildly cognitively impaired (MCI) patients by processing only the clinical and imaging data collected at the initial visit.
Edit intent: clarity
Original:  This study investigates whether a machine-learning-based system can predict the rate of cognitive-decline in mildly cognitively impaired (MCI) patients by processing only the clinical and imaging data collected at the initial visit.
Revised:  This study investigates whether a machine-learning-based system can predict the rate of cognitive decline in mildly cognitively impaired (MCI) patients by processing only the clinical and imaging data collected at the initial visit.
Edit intent: clarity
Original:  This study investigates whether a machine-learning-based system can predict the rate of cognitive-decline in mildly cognitively impaired (MCI) patients by processing only the clinical and imaging data collected at the initial visit.
Revised:  This study investigates whether a machine-learning-based system can predict the rate of cognitive-decline in mildly cognitively impaired patients by processing only the clinical and imaging data collected at the initial visit.
Edit intent: clarity
Original:  We build a predictive model based on a supervised hybrid neural network utilizing a 3-Dimensional Convolutional Neural Network to perform volume analysis of Magnetic Resonance Imaging (MRI) and integration of non-imaging clinical data at the fully connected layer of the architecture.
Revised:  Approach: We built a predictive model based on a supervised hybrid neural network utilizing a 3-Dimensional Convolutional Neural Network to perform volume analysis of Magnetic Resonance Imaging (MRI) and integration of non-imaging clinical data at the fully connected layer of the architecture.
Edit intent: fluency
Original:  We build a predictive model based on a supervised hybrid neural network utilizing a 3-Dimensional Convolutional Neural Network to perform volume analysis of Magnetic Resonance Imaging (MRI) and integration of non-imaging clinical data at the fully connected layer of the architecture.
Revised:  We build a predictive model based on a supervised hybrid neural network utilizing a 3-Dimensional Convolutional Neural Network to perform volume analysis of Magnetic Resonance Imaging and integration of non-imaging clinical data at the fully connected layer of the architecture.
Edit intent: clarity
Original:  The system achieved an area under the receiver operator curve (AUC) of 66.6\% for cognitive decline class prediction  .
Revised:  The system achieved an area under the receiver operator curve (AUC) of 66.6\% for cognitive decline class prediction . Conclusion: To our knowledge, this is the first study that predicts slowly deteriorating/stable or rapidly deteriorating classes by processing routinely collected baseline clinical and demographic data (Baseline MRI, Baseline MMSE, Scalar Volumetric data, Age, Gender, Education, Ethnicity, and Race). The training data is built based on MMSE-rate values. Unlike the studies in the literature that focus on predicting Mild Cognitive Impairment-to-Alzheimer`s disease conversion and disease classification, we approach the problem as an early prediction of cognitive decline rate in MCI patients .
Edit intent: meaning-changed
Original:  These systems rely on user reports for accessing new contents, which has serious problems including the slow handling of violations, the loss of normal and experienced users' time, the low quality of some reports, and discouraging feedback to new users.
Revised:  These systems mainly rely on community reports for assessing contents, which has serious problems including the slow handling of violations, the loss of normal and experienced users' time, the low quality of some reports, and discouraging feedback to new users.
Edit intent: clarity
Original:  These systems rely on user reports for accessing new contents, which has serious problems including the slow handling of violations, the loss of normal and experienced users' time, the low quality of some reports, and discouraging feedback to new users.
Revised:  These systems rely on user reports for accessing new contents, which has serious problems such as the slow handling of violations, the loss of normal and experienced users' time, the low quality of some reports, and discouraging feedback to new users.
Edit intent: clarity
Original: Cyberaggression has been found in various contexts and online social platforms, and modeled on different data using state-of-the-art machine and deep learning algorithms to enable automatic detection and blocking of this behavior.
Revised: Cyberaggression has been studied in various contexts and online social platforms, and modeled on different data using state-of-the-art machine and deep learning algorithms to enable automatic detection and blocking of this behavior.
Edit intent: clarity
Original:  V-transforms describe the relationship between quantiles of the return distribution and quantiles of the distribution of a predictable volatility proxy variable constructed as a function of the return. V-transforms can be represented as copulas and permit the formulation and estimation of models that combine arbitrary marginal distributions with linear or non-linear time series models for the dynamics of the volatility proxy.
Revised:  V-transforms describe the relationship between quantiles of the return distribution and quantiles of the distribution of a predictable volatility proxy variable . They can be represented as copulas and permit the formulation and estimation of models that combine arbitrary marginal distributions with linear or non-linear time series models for the dynamics of the volatility proxy.
Edit intent: clarity
Original:  The idea is illustrated using a transformed Gaussian ARMA process for volatility, yielding the class of VT-ARMA copula models. These can replicate many of the stylized facts of financial return series and  facilitate the calculation of marginal and conditional characteristics of the model including quantile measures of risk.
Revised:  The idea is illustrated using a Gaussian ARMA copula process and the resulting model is shown to replicate many of the stylized facts of financial return series and facilitate the calculation of marginal and conditional characteristics of the model including quantile measures of risk.
Edit intent: clarity
Original:  We have carried out extensive experiments on an existing dataset of SQL andPython , and results show that our model can significantly improve the results of the code retrieval task over the-state-of-art models, as well as achieve competitive performance in terms of BLEU score for the code summarization task.
Revised:  We have carried out extensive experiments on an existing dataset of SQL and Python , and results show that our model can significantly improve the results of the code retrieval task over the-state-of-art models, as well as achieve competitive performance in terms of BLEU score for the code summarization task.
Edit intent: fluency
Original:  In this paper, we propose to replace all but one attention head of each encoder layer with  fixed -- non-learnable -- attentive patterns that are solely based on position and do not require any external knowledge.
Revised:  In this paper, we propose to replace all but one attention head of each encoder layer with simple fixed -- non-learnable -- attentive patterns that are solely based on position and do not require any external knowledge.
Edit intent: meaning-changed