wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We consider using these learned dictionaries as the front-end that can effectively imposing priors on the constituent components. We found that models with  complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Revised:  Extensive experimental results also indicate that these interpretable kernels provide important information on the component features of a neuron's receptive field. In addition, we find that models with complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Edit intent: clarity
Original:  We found that models with  complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Revised:  We found that models with a complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Edit intent: fluency
Original:  We found that models with  complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Revised:  We found that models with complex sparse code front-end are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Edit intent: meaning-changed
Original:  We found that models with  complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Revised:  We found that models with complex sparse code prior are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Edit intent: clarity
Original:  We found that models with  complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Revised:  We found that models with complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been found to exhibit complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Edit intent: clarity
Original:  We found that models with  complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity but not better for modeling neurons that preferred orientation bars or grating (OT) .
Revised:  We found that models with complex sparse code prior are significantly better than models with classical Gabor and/or Laplacian of Gaussian filter priors for modeling and predicting the activities of neurons that have been earlier identified to exhibit higher-order (HO) complex pattern selectivity .
Edit intent: meaning-changed
Original:  Its implementation, an automated momentum equity trading system  , proved to be successful in extensive historical and real-time experiments , discussed in the paper. Risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning  .
Revised:  Its implementation, an automated momentum equity trading system , proved to be successful in extensive historical and real-time experiments . Momentum risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning .
Edit intent: clarity
Original:  We begin with a relatively simple new algebraic-type theory of news impact on share-prices, which describes well their power growth, periodicity, logarithmic periodicity, and the market phenomena like price targets and profit-taking ;
Revised:  We begin with a new algebraic-type theory of news impact on share-prices, which describes well their power growth, periodicity, logarithmic periodicity, and the market phenomena like price targets and profit-taking ;
Edit intent: clarity
Original:  We begin with a relatively simple new algebraic-type theory of news impact on share-prices, which describes well their power growth, periodicity, logarithmic periodicity, and the market phenomena like price targets and profit-taking ;
Revised:  We begin with a relatively simple new algebraic-type theory of news impact on share-prices, which describes well their power growth, periodicity, and the market phenomena like price targets and profit-taking ;
Edit intent: meaning-changed
Original:  We begin with a relatively simple new algebraic-type theory of news impact on share-prices, which describes well their power growth, periodicity, logarithmic periodicity, and the market phenomena like price targets and profit-taking ;
Revised:  We begin with a relatively simple new algebraic-type theory of news impact on share-prices, which describes well their power growth, periodicity, logarithmic periodicity, and the market phenomena like price targets and profit-taking . This theory generally requires
Edit intent: meaning-changed
Original:  Its discretization results in some tables of bids,  expected returns for different investment horizons. This is a new approach; its preimage is a new contract card game presented at the end, a combination of bridge and poker.
Revised:  Its discretization results in some tables of bids, expected returns for main investment horizons, the key in our trading system. The ML procedures we use are similar to those in neural networking. A preimage of our approach is the new contract card game presented at the end, a combination of bridge and poker.
Edit intent: meaning-changed
Original:  In this work , we study the effectiveness of active learning, which optimizes the orderof experiments, and meta learning, which transfers knowledge from one context to another ,  to reduce the number of experiments necessary to build a predictive model.
Revised:  Here , we study the effectiveness of active learning, which optimizes the orderof experiments, and meta learning, which transfers knowledge from one context to another , to reduce the number of experiments necessary to build a predictive model.
Edit intent: clarity
Original:  In this work , we study the effectiveness of active learning, which optimizes the orderof experiments, and meta learning, which transfers knowledge from one context to another ,  to reduce the number of experiments necessary to build a predictive model.
Revised:  In this work , we study the effectiveness of active learning, which optimizes the orderof experiments, and meta learning, which transfers knowledge from one context to another , and meta-learning, transferring knowledge between contexts, to reduce the number of experiments necessary to build a predictive model.
Edit intent: meaning-changed
Original: Divide and Choose among two agents , and the Diminishing Share (DS) and Moving Knife (MK) algorithms among many, elicit parsimonious information to guarantee to each a Fair Share, worth at least 1/ n-th of the whole manna.
Revised: A basic test of fairness when we divide a manna of private items between n agents is the lowest welfare the rule guarantees to each agent, irrespective of others preferences. Two familiar examples are: the Equal Split Guarantee when the manna is divisible and preferences are convex; and 1/ n-th of the whole manna.
Edit intent: meaning-changed
Original: Divide and Choose among two agents , and the Diminishing Share (DS) and Moving Knife (MK) algorithms among many, elicit parsimonious information to guarantee to each a Fair Share, worth at least 1/ n-th of the whole manna. Our n-person Divide and Choose (D C) rule, unlike DS and MK, works if the manna has subjectively good and bad parts.
Revised: Divide and Choose among two agents , and the Diminishing Share (DS) and Moving Knife (MK) algorithms among many, elicit parsimonious information to guarantee to each a Fair Share, worth at least 1/ C) rule, unlike DS and MK, works if the manna has subjectively good and bad parts.
Edit intent: clarity
Original:  Our n-person Divide and Choose (D C) rule, unlike DS and MK, works if the manna has subjectively good and bad parts. If utilities are additive over indivisible items, it implements the canonical "Fair Share up to one item" approximation. The D%DIFDELCMD < &%%% C rule also offers one interpretation of the Fair Share when utilities are neither additivenor monotonic .
Revised:  Our n-person Divide and Choose (D %DIFDELCMD < &%%% C rule also offers one interpretation of the Fair Share when utilities are neither additivenor monotonic .
Edit intent: coherence
Original:  The D%DIFDELCMD < &%%% C rule also offers one interpretation of the Fair Share when utilities are neither additivenor monotonic . Under a mild continuity assumption, it guarantees to each agent her minMax utility : that of her best share in the worst possible partition.
Revised:  The D%DIFDELCMD < &%%% n of the utility of a heterogenous non atomic cake, if utilities are additive. The minMax utility of an agent is that of her best share in the worst possible partition.
Edit intent: meaning-changed
Original:  Under a mild continuity assumption, it guarantees to each agent her minMax utility : that of her best share in the worst possible partition. This is lower than her Maxmin utility : that of her worst share in the best possible partition.
Revised:  Under a mild continuity assumption, it guarantees to each agent her minMax utility : that of her best share in the worst possible n-partition of. It is weakly below her Maxmin utility : that of her worst share in the best possible partition.
Edit intent: meaning-changed
Original:  This is lower than her Maxmin utility : that of her worst share in the best possible partition. When the manna is unanimously good, or unanimously bad, better guarantees than minMax are feasible.
Revised:  This is lower than her Maxmin utility : that of her worst share in the best possible n-partition. The Maxmin guarantee is not feasible, even with two agents, if non convex preferences are allowed. The minMax guarantee is feasible in the very general class of problems where the manna is non atomic and utilities are continuous, but not necessarily additive or monotonic. The proof uses advanced algebraic topology techniques. And the minMax guarantee is implemented by the n-person version of Divide and Choose due to Kuhn (1967). When utilities are co-monotone (a larger part of the manna is weakly better for everyone, or weakly worse for everyone) better guarantees than minMax are feasible.
Edit intent: meaning-changed
Original: Financial market in oil-dependent countries has been always influenced by any changes in international energy market, In particular, oil price.It is therefore of considerable interest to investigate the impact of oil price on financial markets. The aim of this paperis to model the impact of oil price volatility on stock and industry indices by considering gas and gold price,exchange rate and trading volume as explanatory variables.
Revised: In this paper, we model the impact of oil price volatility on stock and industry indices by considering gas and gold price,exchange rate and trading volume as explanatory variables.
Edit intent: clarity
Original:  The aim of this paperis to model the impact of oil price volatility on stock and industry indices by considering gas and gold price,exchange rate and trading volume as explanatory variables. We also propose Feed-forward networks as an accurate method to model non-linearity. we use data from 2009 to 2018 that is split in two periods during international energy sanction and post-sanction.
Revised:  The aim of this paperis to model the impact of oil price volatility on stock and industry indices in two periods of international sanctions and post-sanction. To analyse the purpose of study, we use Feed-forward networks as an accurate method to model non-linearity. we use data from 2009 to 2018 that is split in two periods during international energy sanction and post-sanction.
Edit intent: clarity
Original:  We also propose Feed-forward networks as an accurate method to model non-linearity. we use data from 2009 to 2018 that is split in two periods during international energy sanction and post-sanction.
Revised:  We also propose Feed-forward neural net-works. The period of study is from 2008 to 2018 that is split in two periods during international energy sanction and post-sanction.
Edit intent: clarity
Original:  The result is more robust in the post-sanction period and global financial crisis in 2014. Herein, it is important for financial market analysts and policy makers to note which factors and when influence the financial market, especially in an oil-dependent country such as Iran with uncertainty in the international politics.
Revised:  The result is more robust in the post-sanction period and global financial crisis , the model performs better in predicting industry index. Additionally, oil price-stock market index prediction performs better in the period of international sanctions. Herein, these results are, up to some extent, important for financial market analysts and policy makers to note which factors and when influence the financial market, especially in an oil-dependent country such as Iran with uncertainty in the international politics.
Edit intent: meaning-changed
Original: A discrete system's heterogeneity is measured by the R\'enyi heterogeneity family of indices (also known as Hill numbers or Hannah-Kay indices), whose units are known as the numbers equivalent , and whose scaling properties are consistent and intuitive .
Revised: A discrete system's heterogeneity is measured by the R\'enyi heterogeneity family of indices (also known as Hill numbers or Hannah--Kay indices), whose units are known as the numbers equivalent , and whose scaling properties are consistent and intuitive .
Edit intent: fluency
Original: A discrete system's heterogeneity is measured by the R\'enyi heterogeneity family of indices (also known as Hill numbers or Hannah-Kay indices), whose units are known as the numbers equivalent , and whose scaling properties are consistent and intuitive .
Revised: A discrete system's heterogeneity is measured by the R\'enyi heterogeneity family of indices (also known as Hill numbers or Hannah-Kay indices), whose units are the numbers equivalent , and whose scaling properties are consistent and intuitive .
Edit intent: fluency
Original: A discrete system's heterogeneity is measured by the R\'enyi heterogeneity family of indices (also known as Hill numbers or Hannah-Kay indices), whose units are known as the numbers equivalent , and whose scaling properties are consistent and intuitive .
Revised: A discrete system's heterogeneity is measured by the R\'enyi heterogeneity family of indices (also known as Hill numbers or Hannah-Kay indices), whose units are known as the numbers equivalent .
Edit intent: coherence
Original:  Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require a priori (A) categorical partitioning and (B) pairwise distance measurement on the space of observable data . This precludes their application to problems in disciplines where categories are ill-defined  or where semantically relevant features must be learned as abstractions from some data.
Revised:  Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require a priori (A) categorical partitioning and (B) pairwise distance measurement on the observable data space, thereby precluding application to problems in disciplines where categories are ill-defined or where semantically relevant features must be learned as abstractions from some data.
Edit intent: coherence
Original:   Compared with existing state-of-the-art indices on a beta-mixture distribution, we show that RRH more accurately detects the number of distinct mixture components.
Revised:  We show that RRH can generalize existing biodiversity and economic equality indices. Compared with existing state-of-the-art indices on a beta-mixture distribution, we show that RRH more accurately detects the number of distinct mixture components.
Edit intent: meaning-changed
Original:   Compared with existing state-of-the-art indices on a beta-mixture distribution, we show that RRH more accurately detects the number of distinct mixture components. We also show that RRH can measure heterogeneity in natural images whose semantically relevant features must be abstracted using deep generative models. We further show that RRH can uniquely capture heterogeneity caused by distinct components in mixture distributions. Our novel approach will enable measurement of heterogeneity in disciplines where a priori categorical partitions of observable data are not possible, or where semantically relevant features must be inferred using latent variable models .
Revised:  Compared with existing state-of-the-art indices on a beta-mixture distribution, we show that RRH responds more appropriately to changes in mixture component separation and weighting. Finally, we demonstrate the measurement of RRH in disciplines where a priori categorical partitions of observable data are not possible, or where semantically relevant features must be inferred using latent variable models .
Edit intent: clarity
Original:  Our novel approach will enable measurement of heterogeneity in disciplines where a priori categorical partitions of observable data are not possible, or where semantically relevant features must be inferred using latent variable models .
Revised:  Our novel approach will enable measurement of heterogeneity in a set of natural images, with respect to abstract representations learned by a deep neural network. The RRH approach will further enable heterogeneity measurement in disciplines whose data do not easily conform to the assumptions of existing indices .
Edit intent: meaning-changed
Original:  Empirical studies of the vegetation cover of drylands and semiarid regions have revealed the existence of vegetation patches of broadly diverse sizes , such that the probability distribution of their sizes can be fitted by a power law, i.e. vegetation patches are approximately scale free.
Revised:  Empirical studies of the vegetation cover in some areas such as drylands and semiarid regions have revealed the existence of vegetation patches of broadly diverse sizes , such that the probability distribution of their sizes can be fitted by a power law, i.e. vegetation patches are approximately scale free.
Edit intent: clarity
Original:  Empirical studies of the vegetation cover of drylands and semiarid regions have revealed the existence of vegetation patches of broadly diverse sizes , such that the probability distribution of their sizes can be fitted by a power law, i.e. vegetation patches are approximately scale free.
Revised:  Empirical studies of the vegetation cover of drylands and semiarid regions have revealed the existence of vegetation patches of broadly diverse sizes . In particular, the probability distribution of their sizes can be fitted by a power law, i.e. vegetation patches are approximately scale free.
Edit intent: coherence
Original:  Empirical studies of the vegetation cover of drylands and semiarid regions have revealed the existence of vegetation patches of broadly diverse sizes , such that the probability distribution of their sizes can be fitted by a power law, i.e. vegetation patches are approximately scale free.
Revised:  Empirical studies of the vegetation cover of drylands and semiarid regions have revealed the existence of vegetation patches of broadly diverse sizes , such that the probability distribution of patch sizes can be fitted by a power law, i.e. vegetation patches are approximately scale free.
Edit intent: clarity
Original:  Using a simple individual-based model for vegetation dynamics, we show that environmental  variability --a well-recognized characteristic feature of semiarid environments-- promotes the emergence of power-law vegetation patches in a robust way (i.e. for a broad range of parameter values)  .
Revised:  Using a simple model for vegetation dynamics, we show that environmental variability --a well-recognized characteristic feature of semiarid environments-- promotes the emergence of power-law vegetation patches in a robust way (i.e. for a broad range of parameter values) .
Edit intent: clarity
Original:  Using a simple individual-based model for vegetation dynamics, we show that environmental  variability --a well-recognized characteristic feature of semiarid environments-- promotes the emergence of power-law vegetation patches in a robust way (i.e. for a broad range of parameter values)  .
Revised:  Using a simple individual-based model for vegetation dynamics, we show that environmental temporal variability --a well-recognized characteristic feature of semiarid environments-- promotes the emergence of power-law vegetation patches in a robust way (i.e. for a broad range of parameter values) .
Edit intent: meaning-changed
Original:  Using a simple individual-based model for vegetation dynamics, we show that environmental  variability --a well-recognized characteristic feature of semiarid environments-- promotes the emergence of power-law vegetation patches in a robust way (i.e. for a broad range of parameter values)  .
Revised:  Using a simple individual-based model for vegetation dynamics, we show that environmental variability --a well-recognized feature of semiarid environments-- promotes the emergence of power-law vegetation patches in a robust way (i.e. for a broad range of parameter values) .
Edit intent: clarity
Original:  Using a simple individual-based model for vegetation dynamics, we show that environmental  variability --a well-recognized characteristic feature of semiarid environments-- promotes the emergence of power-law vegetation patches in a robust way (i.e. for a broad range of parameter values)  .
Revised:  Using a simple individual-based model for vegetation dynamics, we show that environmental variability --a well-recognized characteristic feature of semiarid environments-- promotes in a robust way (i.e. for a broad range of parameter values) .
Edit intent: clarity