wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show that a modeling framework allowing for those interactions  can easily tackle puzzling problems such as the phenology of phytoplankton blooms, or vertical variability in mixed layers . Our aquacosm framework allows for a mechanistic description of planktonic ecosystems at the microscales, promising a more effective integration of observational and modeling efforts .
Revised:  We show that a modeling framework allowing for those interactions can easily tackle puzzling problems such as the phenology of phytoplankton blooms, or vertical variability in mixed layers .
Edit intent: coherence
Original:  Specifically, we use the mechanism of GBM with a reallocation parameter that quantifies the extent and direction of reallocation in the distribution, as proposed by Berman et al.
Revised:  Specifically, we use a stochastic model of Geometric Brownian Motion with a reallocation parameter that quantifies the extent and direction of reallocation in the distribution, as proposed by Berman et al.
Edit intent: clarity
Original:  Specifically, we use the mechanism of GBM with a reallocation parameter that quantifies the extent and direction of reallocation in the distribution, as proposed by Berman et al. We find that since the early 2000s reallocation is negative, meaning that incomes are exponentially diverging and that there is a perverse redistribution of resources from the poor to the rich.
Revised:  Specifically, we use the mechanism of GBM with a reallocation parameter that quantifies the extent and direction of composite redistribution implied in the income distribution.
Edit intent: clarity
Original:  Our work refutes this as we find that India has moved from a regime of progressive to regressive redistribution , where continued impoverishment of the poor is directly spurring multiplicative income growth of the rich.
Revised:  Findings from our model refute this, as we find that India has moved from a regime of progressive to regressive redistribution , where continued impoverishment of the poor is directly spurring multiplicative income growth of the rich.
Edit intent: clarity
Original:  Our work refutes this as we find that India has moved from a regime of progressive to regressive redistribution , where continued impoverishment of the poor is directly spurring multiplicative income growth of the rich.
Revised:  Our work refutes this as we find that since the early 2000s reallocation has consistently been negative, and that the Indian income distribution has entered a regime of progressive to regressive redistribution , where continued impoverishment of the poor is directly spurring multiplicative income growth of the rich.
Edit intent: meaning-changed
Original:  Outcomes from the model suggest that income shares of the bottom decile (~1\%) and bottom percentile (~0.03\%) are at historic lows  .
Revised:  Outcomes from the model suggest that income shares of the bottom decile (~1\%) and bottom percentile (~0.03\%) are at historic lows , but also that real incomes of the bottom decile (-2.5\%) and percentile (-6\%) have declined in the 2000s. We validate these findings using income distribution data and find support for our contention of persistent negative reallocation in the 2000s .
Edit intent: meaning-changed
Original:  The first to describe what is being reused, the second to describe the size of what is being reused, and the last to describe the process of seeking past experience from present input . Based on the abnormalities of two psychiatric disorders, autism and schizophrenia, we hypothesize that the structure of the pyramidal neurons of the Prefrontal Cortex determines the dimension and we make two demonstrations with artificial intelligence -
Revised:  The first to describe what the abstraction is and the second to numerically quantify the first . Based on the abnormalities of two psychiatric disorders, autism and schizophrenia, we hypothesize that the structure of the pyramidal neurons of the Prefrontal Cortex determines the dimension and we make two demonstrations with artificial intelligence -
Edit intent: clarity
Original:  The first to describe what is being reused, the second to describe the size of what is being reused, and the last to describe the process of seeking past experience from present input . Based on the abnormalities of two psychiatric disorders, autism and schizophrenia, we hypothesize that the structure of the pyramidal neurons of the Prefrontal Cortex determines the dimension and we make two demonstrations with artificial intelligence -
Revised:  The first to describe what is being reused, the second to describe the size of what is being reused, and the last to describe the process of seeking past experience from present input . Based on the common abnormalities in both biological structure and cognitive functioning of two psychiatric disorders, autism and schizophrenia, we hypothesize that the structure of the pyramidal neurons of the Prefrontal Cortex determines the dimension and we make two demonstrations with artificial intelligence -
Edit intent: meaning-changed
Original:  The first to describe what is being reused, the second to describe the size of what is being reused, and the last to describe the process of seeking past experience from present input . Based on the abnormalities of two psychiatric disorders, autism and schizophrenia, we hypothesize that the structure of the pyramidal neurons of the Prefrontal Cortex determines the dimension and we make two demonstrations with artificial intelligence -
Revised:  The first to describe what is being reused, the second to describe the size of what is being reused, and the last to describe the process of seeking past experience from present input . Based on the abnormalities of two psychiatric disorders, autism and schizophrenia, we hypothesize that the density of the dendritic spines in the pyramidal neurons of the Prefrontal Cortex determines the dimension and we make two demonstrations with artificial intelligence -
Edit intent: clarity
Original:  The first to describe what is being reused, the second to describe the size of what is being reused, and the last to describe the process of seeking past experience from present input . Based on the abnormalities of two psychiatric disorders, autism and schizophrenia, we hypothesize that the structure of the pyramidal neurons of the Prefrontal Cortex determines the dimension and we make two demonstrations with artificial intelligence -
Revised:  The first to describe what is being reused, the second to describe the size of what is being reused, and the last to describe the process of seeking past experience from present input . Based on the abnormalities of two psychiatric disorders, autism and schizophrenia, we hypothesize that the structure of the pyramidal neurons of the Prefrontal Cortex determines the dimension . We show the computational validity of our claims by making two demonstrations with artificial intelligence -
Edit intent: clarity
Original: The ability to create abstract categorizations to be reused in different contexts is one of the most critical abilities for intelligent behavior. In this letter, we present a proposal for how neurobiological structure plays a role in this ability. We introduce two terms: kernel and kernel dimension. The first to describe what the abstraction is and the second to numerically quantify the first. Based on the common abnormalities in both biological structure and cognitive functioning of two psychiatric disorders, autism and schizophrenia, we hypothesize that the density of the dendritic spines in the pyramidal neurons of the Prefrontal Cortex determines the dimension. We show the computational validity of our claims by making two demonstrations with artificial intelligence - one to show that structural density can effect kernel dimension and the other to show that cognitive functioning is effected by the dimension of the kernel .
Revised: Over the last few decades, a lot of progress has been made in understanding different aspects of the brain's ability to form abstract representations, but a specific mechanism for how they are created and used remains to emerge. Here, we review recent findings on the subject and we propose a mechanism for the dynamics of forming abstract representations, where the formation of local connectivity in neural networks of abstract representations are the transcription of individual details .
Edit intent: coherence
Original: We are introducing the new family of the Continuous Time Random Walks (CTRW) with long-term memory within consecutive waiting times.
Revised: In many physical, social or economical phenomena we observe changes of a studied quantity only in discrete, irregularly distributed points in time. The stochastic process used by physicists to describe this kind of variables is the Continuous Time Random Walks (CTRW) with long-term memory within consecutive waiting times.
Edit intent: meaning-changed
Original: We are introducing the new family of the Continuous Time Random Walks (CTRW) with long-term memory within consecutive waiting times. This memory is introduced to the model by the assumption that consecutive waiting times are the analog of CTRW themselves.
Revised: We are introducing the new family of the Continuous Time Random Walks (CTRW) with long-term memory within the sequence of time intervals between observations are missing. Here, we fill this gap by introducing a new family of CTRWs. The memory is introduced to the model by the assumption that consecutive waiting times are the analog of CTRW themselves.
Edit intent: meaning-changed
Original:  This memory is introduced to the model by the assumption that consecutive waiting times are the analog of CTRW themselves. This way, we obtain the 'Continuous' Time Random Walk in Continuous Time Random Walk. Surprisingly, this type of process , only with the long-term memory within waiting times, can successfully describe slowly decaying nonlinear autocorrelation function of the stock market return .
Revised:  This memory is introduced to the model by the assumption that many consecutive time intervals can be the same. Surprisingly, in this process we can observe a slowly decaying nonlinear autocorrelation function of the stock market return .
Edit intent: clarity
Original:  Surprisingly, this type of process , only with the long-term memory within waiting times, can successfully describe slowly decaying nonlinear autocorrelation function of the stock market return .
Revised:  Surprisingly, this type of process , only with the long-term memory within waiting times, can successfully describe slowly decaying nonlinear autocorrelation function without a fat-tailed distribution of time intervals. Our model applied to high-frequency stock market data can successfully describe the slope of decay of nonlinear autocorrelation function of stock market returns .
Edit intent: meaning-changed
Original: The inverse Potts problem to infer the Boltzmann distribution for homologous protein sequences from their single-site and pairwise  frequencies recently attracts a great deal of attention due to its capacity to accurately predict residue-residue contacts in a 3D protein complex.
Revised: The inverse Potts problem to infer a Boltzmann distribution for homologous protein sequences from their single-site and pairwise frequencies recently attracts a great deal of attention due to its capacity to accurately predict residue-residue contacts in a 3D protein complex.
Edit intent: fluency
Original: The inverse Potts problem to infer the Boltzmann distribution for homologous protein sequences from their single-site and pairwise  frequencies recently attracts a great deal of attention due to its capacity to accurately predict residue-residue contacts in a 3D protein complex.
Revised: The inverse Potts problem to infer the Boltzmann distribution for homologous protein sequences from their single-site and pairwise amino acid frequencies recently attracts a great deal of attention due to its capacity to accurately predict residue-residue contacts in a 3D protein complex.
Edit intent: meaning-changed
Original: The inverse Potts problem to infer the Boltzmann distribution for homologous protein sequences from their single-site and pairwise  frequencies recently attracts a great deal of attention due to its capacity to accurately predict residue-residue contacts in a 3D protein complex. A Boltzmann machine for the accurate estimation of the field and coupling interactions, which is required for other studies in protein evolution and folding, is studied about learning methods , regularization models and a tuning method of regularization parameters in order to infer the interactions with reasonable characteristics . Using L_2 regularization for fields, group L_1 for couplings is shown to be very effective for parse couplings in comparison with L_2 and with L_1.
Revised: The inverse Potts problem to infer the Boltzmann distribution for homologous protein sequences from their single-site and pairwise frequencies recently attracts a great deal of attention in the studies of protein structure and evolution. We study regularization and learning methods and folding, is studied about learning methods , regularization models and a tuning method of regularization parameters in order to infer the interactions with reasonable characteristics . Using L_2 regularization for fields, group L_1 for couplings is shown to be very effective for parse couplings in comparison with L_2 and with L_1.
Edit intent: meaning-changed
Original:  A Boltzmann machine for the accurate estimation of the field and coupling interactions, which is required for other studies in protein evolution and folding, is studied about learning methods , regularization models and a tuning method of regularization parameters in order to infer the interactions with reasonable characteristics . Using L_2 regularization for fields, group L_1 for couplings is shown to be very effective for parse couplings in comparison with L_2 and with L_1.
Revised:  A Boltzmann machine for the accurate estimation of the field and coupling interactions, which is required for other studies in protein evolution and how to tune regularization parameters to correctly infer interactions in Boltzmann machine learning . Using L_2 regularization for fields, group L_1 for couplings is shown to be very effective for parse couplings in comparison with L_2 and with L_1.
Edit intent: clarity
Original:  A Boltzmann machine for the accurate estimation of the field and coupling interactions, which is required for other studies in protein evolution and folding, is studied about learning methods , regularization models and a tuning method of regularization parameters in order to infer the interactions with reasonable characteristics . Using L_2 regularization for fields, group L_1 for couplings is shown to be very effective for parse couplings in comparison with L_2 and with L_1.
Revised:  A Boltzmann machine for the accurate estimation of the field and coupling interactions, which is required for other studies in protein evolution and folding, is studied about learning methods , regularization models and a tuning method of regularization parameters in order to infer the interactions with reasonable characteristics . Using L_2 regularization for fields, group L_1 for couplings is shown to be very effective for sparse couplings in comparison with L_2 and with L_1.
Edit intent: clarity
Original:  A Boltzmann machine for the accurate estimation of the field and coupling interactions, which is required for other studies in protein evolution and folding, is studied about learning methods , regularization models and a tuning method of regularization parameters in order to infer the interactions with reasonable characteristics . Using L_2 regularization for fields, group L_1 for couplings is shown to be very effective for parse couplings in comparison with L_2 and with L_1.
Revised:  A Boltzmann machine for the accurate estimation of the field and coupling interactions, which is required for other studies in protein evolution and folding, is studied about learning methods , regularization models and a tuning method of regularization parameters in order to infer the interactions with reasonable characteristics . Using L_2 regularization for fields, group L_1 for couplings is shown to be very effective for parse couplings in comparison with L_2 and L_1.
Edit intent: fluency
Original:  Two regularization parameters for fields and couplings are tuned to yield equal values for both the sample average and the ensemble average of evolutionary energies of natural proteins. Both the averages along a learning process smoothly change and converge, but their  profiles are very different between the learning methods.
Revised:  Two regularization parameters for fields and couplings are tuned to yield equal values for both the sample and ensemble averages of evolutionary energy. Both averages smoothly change and converge, but their profiles are very different between the learning methods.
Edit intent: clarity
Original:  Most per-parameter adaptive learning methods invented for machine learning cannot learn reasonable parameters for sparse-interaction systems. A modified Adam (ModAdam) method is invented to make step-size proportional to the partial derivative for sparse couplings and to use a soft thresholding function for group L_1.
Revised:  The Adam method is modified to make stepsize proportional to the partial derivative for sparse couplings and to use a soft thresholding function for group L_1.
Edit intent: coherence
Original:  A modified Adam (ModAdam) method is invented to make step-size proportional to the partial derivative for sparse couplings and to use a soft thresholding function for group L_1.
Revised:  A modified Adam (ModAdam) method is invented to make step-size proportional to the partial derivative for sparse couplings and to use a soft-thresholding function for group L_1.
Edit intent: fluency
Original:  It is shown by first inferring interactions from protein sequences and then from Monte Carlo samples that the fields and couplings can be well recovered by the group L_1 and the ModAdam method. However, the distribution of evolutionary energies over natural proteins is shifted towards lower energies from that of Monte Carlo samples, indicating that there may be higher-order interactions to favor natural sequences .
Revised:  It is shown by first inferring interactions from protein sequences and then from Monte Carlo samples that the fields and couplings can be well recovered , but that recovering the pairwise correlations in the resolution of a total energy is harder for the natural proteins than for the protein-like sequences. Selective temperature for folding/structural constrains in protein evolution is also estimated .
Edit intent: meaning-changed
Original:  Our molecule generator combines two  pretrained variational autoencoders (VAEs) and a multimodal efficacy predictor - the first VAE generates transcriptomic profiles while the second conditional VAE generates novel molecular structures conditioned on the given transcriptomic profile.
Revised:  Our molecule generator combines two separately pretrained variational autoencoders (VAEs) and a multimodal efficacy predictor - the first VAE generates transcriptomic profiles while the second conditional VAE generates novel molecular structures conditioned on the given transcriptomic profile.
Edit intent: meaning-changed
Original:  Our molecule generator combines two  pretrained variational autoencoders (VAEs) and a multimodal efficacy predictor - the first VAE generates transcriptomic profiles while the second conditional VAE generates novel molecular structures conditioned on the given transcriptomic profile.
Revised:  Our molecule generator combines two pretrained variational autoencoders (VAEs) - the first VAE generates transcriptomic profiles while the second conditional VAE generates novel molecular structures conditioned on the given transcriptomic profile.
Edit intent: clarity
Original:  Our molecule generator combines two  pretrained variational autoencoders (VAEs) and a multimodal efficacy predictor - the first VAE generates transcriptomic profiles while the second conditional VAE generates novel molecular structures conditioned on the given transcriptomic profile.
Revised:  Our molecule generator combines two pretrained variational autoencoders (VAEs) and a multimodal efficacy predictor - the first VAE encodes transcriptomic profiles into a smooth, latent space which in turn is used to condition a second VAE to generate novel molecular structures conditioned on the given transcriptomic profile.
Edit intent: meaning-changed
Original:  Our molecule generator combines two  pretrained variational autoencoders (VAEs) and a multimodal efficacy predictor - the first VAE generates transcriptomic profiles while the second conditional VAE generates novel molecular structures conditioned on the given transcriptomic profile.
Revised:  Our molecule generator combines two pretrained variational autoencoders (VAEs) and a multimodal efficacy predictor - the first VAE generates transcriptomic profiles while the second conditional VAE generates novel molecular structures on the given transcriptomic profile.
Edit intent: clarity
Original:  Even when measurements of (Y,D) are confounded , the treatment effect on the subpopulation of compliers can nonetheless be identified if an instrumental variable Z is available , which is independent of (Y,D) conditional on X and the unmeasured confounder .
Revised:  Even in the presence of an unmeasured confounder of (Y,D) are confounded , the treatment effect on the subpopulation of compliers can nonetheless be identified if an instrumental variable Z is available , which is independent of (Y,D) conditional on X and the unmeasured confounder .
Edit intent: clarity
Original:  Even when measurements of (Y,D) are confounded , the treatment effect on the subpopulation of compliers can nonetheless be identified if an instrumental variable Z is available , which is independent of (Y,D) conditional on X and the unmeasured confounder .
Revised:  Even when measurements of (Y,D) , the treatment effect on the subpopulation of compliers can nonetheless be identified if an instrumental variable Z is available , which is independent of (Y,D) conditional on X and the unmeasured confounder .
Edit intent: clarity
Original:  Even when measurements of (Y,D) are confounded , the treatment effect on the subpopulation of compliers can nonetheless be identified if an instrumental variable Z is available , which is independent of (Y,D) conditional on X and the unmeasured confounder .
Revised:  Even when measurements of (Y,D) are confounded , the treatment effect on the subpopulation of compliers can nonetheless be identified if an instrumental variable Z is available .
Edit intent: clarity
Original:  In our approach, the de-biasing is itself performed by machine learning, a variant called  de-biased machine learning via regularized Riesz representers (DML-RRR ). We prove our estimator is consistent, asymptotically normal, and semi-parametrically efficient.
Revised:  In our approach, the de-biasing is itself performed by machine learning, a variant called automatic de-biased machine learning via regularized Riesz representers (DML-RRR ). We prove our estimator is consistent, asymptotically normal, and semi-parametrically efficient.
Edit intent: meaning-changed
Original:  In our approach, the de-biasing is itself performed by machine learning, a variant called  de-biased machine learning via regularized Riesz representers (DML-RRR ). We prove our estimator is consistent, asymptotically normal, and semi-parametrically efficient.
Revised:  In our approach, the de-biasing is itself performed by machine learning, a variant called de-biased machine learning (Auto-DML ). We prove our estimator is consistent, asymptotically normal, and semi-parametrically efficient.
Edit intent: meaning-changed