wanyu/IteraTeR_full_sent huggingface dataset:
Original: Individual level and exhaustive income data for Romania (Cluj county ) is analysed for several consecutive years. The income distributions collapse on a master-curve when a properly normalised income is considered.
Revised: We provide an analytically treatable model that describes in a unified manner income distribution for all income categories. The approach is based on a master equation with growth and reset terms. The model assumptions on the growth and reset rates are tested on an exhaustive database with incomes on individual level spanning a nine year period in the Cluj county (Romania). In agreement with our theoretical predictions we find that income distributions computed for several years collapse on a master-curve when a properly normalised income is considered.
Edit intent: meaning-changed
Original:  The Beta Prime distribution is appropriate to fit the collapsed data . A dynamical model based on a master equation with growth and reset terms is successful in explaining the observed distribution in a self-consistent manner, i.e. the growth and reset rates are evaluated from the same individual level data. Income distribution derived for other countries are following similar trends .
Revised:  The Beta Prime distribution is appropriate to fit the collapsed data and it is shown that distributions derived for other countries are following similar trends .
Edit intent: coherence
Original:  Income distribution derived for other countries are following similar trends . The collapse on the master-curve is not perfect however, suggesting that for a more realistic modelling specific socio-economic characteristics have to be taken also into account .
Revised:  Income distribution derived for other countries are following similar trends with different fit parameters. The non-universal feature of the fit parameters suggests that for a more realistic modelling specific socio-economic characteristics have to be taken also into account .
Edit intent: clarity
Original:   Driven by more than URLanizations as well as more than 200 ML engineers and practitioners, MLPerf implements a set of rules and  practices to ensure comparability across systems with wildly differing architectures.
Revised:  In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than URLanizations as well as more than 200 ML engineers and practitioners, MLPerf implements a set of rules and practices to ensure comparability across systems with wildly differing architectures.
Edit intent: meaning-changed
Original:   Driven by more than URLanizations as well as more than 200 ML engineers and practitioners, MLPerf implements a set of rules and  practices to ensure comparability across systems with wildly differing architectures.
Revised:  Driven by more than URLanizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and practices to ensure comparability across systems with wildly differing architectures.
Edit intent: clarity
Original:   Driven by more than URLanizations as well as more than 200 ML engineers and practitioners, MLPerf implements a set of rules and  practices to ensure comparability across systems with wildly differing architectures.
Revised:  Driven by more than URLanizations as well as more than 200 ML engineers and practitioners, MLPerf implements a set of rules and best practices to ensure comparability across systems with wildly differing architectures.
Edit intent: meaning-changed
Original: In the case where beliefs are ambiguous and represented by multiple priors, a decision maker (DM)'s updating behavior may also include a revision and refinement of her initial belief, a process which is absent from the Bayesian updating.
Revised: When ambiguous beliefs are represented by multiple priors, a decision maker (DM)'s updating behavior may also include a revision and refinement of her initial belief, a process which is absent from the Bayesian updating.
Edit intent: clarity
Original: In the case where beliefs are ambiguous and represented by multiple priors, a decision maker (DM)'s updating behavior may also include a revision and refinement of her initial belief, a process which is absent from the Bayesian updating.
Revised: In the case where beliefs are ambiguous and represented by multiple priors, a decision maker (DM)'s updating rule may also include a revision and refinement of her initial belief, a process which is absent from the Bayesian updating.
Edit intent: clarity
Original: In the case where beliefs are ambiguous and represented by multiple priors, a decision maker (DM)'s updating behavior may also include a revision and refinement of her initial belief, a process which is absent from the Bayesian updating. As known in the literature, Maximum Likelihood (ML) updating provides one method of such refinement.
Revised: In the case where beliefs are ambiguous and represented by multiple priors, a decision maker (DM)'s updating behavior may also include a step of refining the initial belief. Maximum Likelihood (ML) updating provides one method of such refinement.
Edit intent: clarity
Original:  As known in the literature, Maximum Likelihood (ML) updating provides one method of such refinement. The present paper provides an axiomatization of ML for preferences admit Maxmin Expected Utility (MEU) representation. ML and Full Bayesian (FB ) are two updating rules with polar opposite methods of refining ambiguous beliefs . The present paper proposes and axiomatizes a new updating rule, Relative Maximum Likelihood (RML), for when a DM's conditional preference is not as extreme as either case.
Revised:  As known in the literature, Maximum Likelihood (ML) updating indicates one example of such a refinement, whereas Full Bayesian (FB) updating does not allow for any refinement. In fact, ML and FB are the two extremes of refining beliefs with likelihood in updating. To capture behaviors between these two extremes, the present paper proposes and axiomatizes a new updating rule, Relative Maximum Likelihood (RML), for when a DM's conditional preference is not as extreme as either case.
Edit intent: clarity
Original:  The linear contraction parameter captures the relative attitudes of the DM towards ML with respect to FB . In particular, when the parameter takes either one of the two extreme values (0 or 1), RML reduces to FB or ML respectively. This paper also characterizes the Extended RML updating rule for ex-ante preferences that in addition satisfy comonotonic independence. This rule, which is related to RML, further preserves comonotonic independence for the updated preferences
Revised:  The linear contraction parameter captures the relative attitudes of the DM towards ML with respect to FB , it is also the threshold of a relative likelihood ratio test. Moreover, RML includes both FB and ML as the two extreme special cases. Additionally, the present paper provides an axiomatization of ML for preferences admit Maxmin Expected Utility (MEU) representation as a preliminary result for the exposition of RML.
Edit intent: meaning-changed
Original: When ambiguous beliefs are represented by multiple priors, a decision maker (DM)'s updating rule may also include a step of refining the initial belief  . Maximum Likelihood (ML) updating indicates one example of such a refinement, whereas Full Bayesian (FB) updating does not allow for any refinement.
Revised: When ambiguous beliefs are represented by multiple priors, updating such a belief may also include a step of refining the initial belief . Maximum Likelihood (ML) updating indicates one example of such a refinement, whereas Full Bayesian (FB) updating does not allow for any refinement.
Edit intent: clarity
Original: When ambiguous beliefs are represented by multiple priors, a decision maker (DM)'s updating rule may also include a step of refining the initial belief  . Maximum Likelihood (ML) updating indicates one example of such a refinement, whereas Full Bayesian (FB) updating does not allow for any refinement.
Revised: When ambiguous beliefs are represented by multiple priors, a decision maker (DM)'s updating rule may also include refining of the initial belief . Maximum Likelihood (ML) updating indicates one example of such a refinement, whereas Full Bayesian (FB) updating does not allow for any refinement.
Edit intent: clarity
Original: When ambiguous beliefs are represented by multiple priors, a decision maker (DM)'s updating rule may also include a step of refining the initial belief  . Maximum Likelihood (ML) updating indicates one example of such a refinement, whereas Full Bayesian (FB) updating does not allow for any refinement.
Revised: When ambiguous beliefs are represented by multiple priors, a decision maker (DM)'s updating rule may also include a step of refining the initial belief by discarding some of the priors . Maximum Likelihood (ML) updating indicates one example of such a refinement, whereas Full Bayesian (FB) updating does not allow for any refinement.
Edit intent: meaning-changed
Original: When ambiguous beliefs are represented by multiple priors, a decision maker (DM)'s updating rule may also include a step of refining the initial belief  . Maximum Likelihood (ML) updating indicates one example of such a refinement, whereas Full Bayesian (FB) updating does not allow for any refinement.
Revised: When ambiguous beliefs are represented by multiple priors, a decision maker (DM)'s updating rule may also include a step of refining the initial belief . Maximum Likelihood (ML) and Full Bayesian (FB) updating does not allow for any refinement.
Edit intent: clarity
Original: When ambiguous beliefs are represented by multiple priors, a decision maker (DM)'s updating rule may also include a step of refining the initial belief  . Maximum Likelihood (ML) updating indicates one example of such a refinement, whereas Full Bayesian (FB) updating does not allow for any refinement. In fact, ML and FB are the two extremes of refining beliefs with likelihoodin updating .
Revised: When ambiguous beliefs are represented by multiple priors, a decision maker (DM)'s updating rule may also include a step of refining the initial belief . Maximum Likelihood (ML) updating indicates one example of such a refinement, whereas Full Bayesian (FB) are the two extremes of refining beliefs with likelihoodin updating .
Edit intent: coherence
Original:  To capture behaviors between these two extremes, the present paper proposes and axiomatizes a new updating rule , Relative Maximum Likelihood (RML) , to represent conditional preferences that are not as extreme as either case.
Revised:  To capture behaviors between these two extremes, the present paper proposes and axiomatizes a new updating rule : Relative Maximum Likelihood (RML) , to represent conditional preferences that are not as extreme as either case.
Edit intent: fluency
Original:  To capture behaviors between these two extremes, the present paper proposes and axiomatizes a new updating rule , Relative Maximum Likelihood (RML) , to represent conditional preferences that are not as extreme as either case. RML updates an intermediate set of priors , which can be expressed as a linear contraction of the initial set with respect to the set of maximum likelihood priors .
Revised:  To capture behaviors between these two extremes, the present paper proposes and axiomatizes a new updating rule , Relative Maximum Likelihood (RML) updating. This rule updates an intermediate set of priors , which can be expressed as a linear contraction of the initial set with respect to the set of maximum likelihood priors .
Edit intent: clarity
Original:  Moreover, RML includes both FB and ML as the two extreme special cases . Additionally, the present paper provides an axiomatization of ML for preferences admit Maxmin Expected Utility (MEU) representation as a preliminary result for the exposition of RML .
Revised:  Moreover, RML includes both FB and ML as the two extreme special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility (MEU) representation as a preliminary result for the exposition of RML .
Edit intent: meaning-changed
Original:  Additionally, the present paper provides an axiomatization of ML for preferences admit Maxmin Expected Utility (MEU) representation as a preliminary result for the exposition of RML .
Revised:  Additionally, the present paper provides an axiomatization of ML for preferences admit Maxmin Expected Utility . The axiomatization relies on weakening the axioms that characterize FB and ML. In particular, the axiom that characterizes ML is identified in this paper, fixing an existing problem in the literature .
Edit intent: meaning-changed
Original: When ambiguous beliefs are represented by multiple priors, updating such a belief may also include refining of the initial belief by discarding some of the priors. Maximum Likelihood (ML) and Full Bayesian (FB) are the two extremes of refining beliefs based on likelihood. To capture behaviors between these two extremes, this paper proposes a new updating rule: Relative Maximum Likelihood (RML) updating .
Revised: This paper proposes and axiomatizes a new updating rule: Relative Maximum Likelihood (RML) updating .
Edit intent: clarity
Original:  This rule updates an intermediate set of priors that is a linear contraction of the initial set with respect to the set of priors attaining maximum likelihood. The linear contraction parameter captures the DM's relative inclination towards ML with respect to FB, hence ML and FBare the two extreme special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility  .
Revised:  This rule updates an intermediate set of priors that is a linear contraction of C towards its subset assigning a maximal probability to the observed event. The degree of contraction captures the extent of willingness to discard priors based on likelihood when updating. Two well-known updating rules of multiple priors, Full Bayesian (FB) and Maximum Likelihood (ML), are included as special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility .
Edit intent: clarity
Original:  The linear contraction parameter captures the DM's relative inclination towards ML with respect to FB, hence ML and FBare the two extreme special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility  .
Revised:  The linear contraction parameter captures the DM's relative inclination towards ML with respect to FB, hence ML and FBare the two extreme special cases of RML. An axiomatic characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility .
Edit intent: clarity
Original:  The linear contraction parameter captures the DM's relative inclination towards ML with respect to FB, hence ML and FBare the two extreme special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility  .
Revised:  The linear contraction parameter captures the DM's relative inclination towards ML with respect to FB, hence ML and FBare the two extreme special cases of RML. More importantly, characterization of conditional preferences generated by RML updating is provided when the preferences are represented by the Maxmin Expected Utility .
Edit intent: clarity
Original:  The linear contraction parameter captures the DM's relative inclination towards ML with respect to FB, hence ML and FBare the two extreme special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility  .
Revised:  The linear contraction parameter captures the DM's relative inclination towards ML with respect to FB, hence ML and FBare the two extreme special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences admit Maxmin Expected Utility .
Edit intent: clarity
Original:  The linear contraction parameter captures the DM's relative inclination towards ML with respect to FB, hence ML and FBare the two extreme special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility  .
Revised:  The linear contraction parameter captures the DM's relative inclination towards ML with respect to FB, hence ML and FBare the two extreme special cases of RML. More importantly, characterization of conditional preferences given by RML updating is provided when the preferences are represented by the Maxmin Expected Utility representations .
Edit intent: clarity
Original:  The axiomatization relies on weakening the axioms that characterize FB and ML. In particular, the axiom that characterizes ML is identified  in this paper, fixing an existing problem in the literature.
Revised:  The axiomatization relies on weakening the axioms that characterize FB and ML. In particular, the axiom that characterizes ML is identified for the first time in this paper, fixing an existing problem in the literature.
Edit intent: meaning-changed
Original:  The axiomatization relies on weakening the axioms that characterize FB and ML. In particular, the axiom that characterizes ML is identified  in this paper, fixing an existing problem in the literature.
Revised:  The axiomatization relies on weakening the axioms that characterize FB and ML. In particular, the axiom that characterizes ML is identified in this paper, addressing a long-standing open question in the literature.
Edit intent: clarity
Original:   Observational studies are standard in policy evaluation from labour markets, educational surveys and other empirical studies.
Revised:  The groups can be understood as a broader aggregation of the conditional average treatment effect (CATE) where the number of groups is set in advance. In economics, this approach is similar to pre-analysis plans. Observational studies are standard in policy evaluation from labour markets, educational surveys and other empirical studies.
Edit intent: meaning-changed
Original: Our interest focuses on developing statistical methods for analysis of brain structural connectomes . Nodes in the brain connectome graph correspond to different regions of interest (ROIs) while edges correspond to white matter fiber connections between these ROIs.
Revised: There has been huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationship with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding to different regions of interest (ROIs) while edges correspond to white matter fiber connections between these ROIs.
Edit intent: meaning-changed
Original:  Due to the high-dimensionality and non-Euclidean nature of the data, it becomes challenging to conduct analyses of the population distribution of brain connectomes and relate connectomes to other factors, such as cognition . Current approaches focus on summarizing the graph using either pre-specified topological features or principal components analysis (PCA).
Revised:  Due to the high-dimensionality and non-Euclidean nature of networks, it is challenging to depict their population distribution and relate them to human traits . Current approaches focus on summarizing the graph using either pre-specified topological features or principal components analysis (PCA).
Edit intent: clarity
Original:  Due to the high-dimensionality and non-Euclidean nature of the data, it becomes challenging to conduct analyses of the population distribution of brain connectomes and relate connectomes to other factors, such as cognition . Current approaches focus on summarizing the graph using either pre-specified topological features or principal components analysis (PCA).
Revised:  Due to the high-dimensionality and non-Euclidean nature of the data, it becomes challenging to conduct analyses of the population distribution of brain connectomes and relate connectomes to other factors, such as cognition . Current approaches focus on summarizing the network using either pre-specified topological features or principal components analysis (PCA).
Edit intent: clarity
Original:  In this article, we instead develop a nonlinear latent factor model for summarizing the brain graph in both unsupervised and supervised settings. The proposed approach builds on methods for hierarchical modeling of replicated graph data, as well as variational auto-encoders that use neural networks for dimensionality reduction.
Revised:  In this article, we instead develop a nonlinear latent factor model to characterize the population distribution of brain graphs and infer the relationships between brain structural connectomes and human traits.
Edit intent: meaning-changed
Original:  We compare GATE with tensor PCA and other competitors through simulations and applications to data from the Human Connectome Project (HCP)  .
Revised:  We compare GATE with tensor PCA and other competitors through simulations and applications to data from the Human Connectome Project (HCP) for adults, to understand the structural brain connectome and its relationship with cognition. Numerical results demonstrate huge advantages of GATE over competitors in terms of prediction accuracy, statistical inference and computing efficiency. We found that structural connectomes have a stronger association with a wide range of human cognitive traits than was apparent using previous approaches .
Edit intent: meaning-changed
Original: Objective: Sliding and tapered sliding window methods are the most often used approaches in computing dynamic correlations in biomedical signals such as the brain resting-state fMRI  . However,  due to the discrete nature of windows, the window methods suffer spurious high-frequency fluctuations and the zig-zag pattern in dynamic correlations.
Revised: Recent studies have shown that functional brain brainwork is dynamic even during rest. A common approach to modeling the brain network in whole brain resting-state fMRI . However, due to the discrete nature of windows, the window methods suffer spurious high-frequency fluctuations and the zig-zag pattern in dynamic correlations.
Edit intent: meaning-changed
Original: Objective: Sliding and tapered sliding window methods are the most often used approaches in computing dynamic correlations in biomedical signals such as the brain resting-state fMRI  . However,  due to the discrete nature of windows, the window methods suffer spurious high-frequency fluctuations and the zig-zag pattern in dynamic correlations.
Revised: Objective: Sliding and tapered sliding window methods are the most often used approaches in computing dynamic correlations in biomedical signals such as the brain resting-state fMRI is to compute the correlation between anatomical regions via sliding windows . However, due to the discrete nature of windows, the window methods suffer spurious high-frequency fluctuations and the zig-zag pattern in dynamic correlations.
Edit intent: meaning-changed