wanyu/IteraTeR_full_sent huggingface dataset:
Original:  By providing a framework for a mechanistic understanding of how developmentshapes a nervous system, our results complement interpretive analysis of how efficiency and optimality govern its design . Providing crucial insights for understanding principles that may be common across URLanisms, our study shows how the trajectory in the developmental landscape constrains the eventual spatial and network URLanization of a nervous system.
Revised:  By providing a framework for a mechanistic understanding of how developmentshapes a nervous system, our results complement interpretive analysis of how efficiency and optimality govern its design . Providing crucial insights into principles that may be common across URLanisms, our study shows how the trajectory in the developmental landscape constrains the eventual spatial and network URLanization of a nervous system.
Edit intent: fluency
Original: Data scarcity has refrained deep learning models from making greater progress in prostate images analysis using multiparametric MRI . In this paper, an efficient convolutional neural network (CNN)was developed to classify lesion malignancy for prostate cancer patients, based on which model interpretation was systematically analyzed to bridge the gap between natural images and MR images, which is the first one of its kind in the literature.
Revised: Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models from making greater progress in prostate images analysis using multiparametric MRI . In this paper, an efficient convolutional neural network (CNN)was developed to classify lesion malignancy for prostate cancer patients, based on which model interpretation was systematically analyzed to bridge the gap between natural images and MR images, which is the first one of its kind in the literature.
Edit intent: meaning-changed
Original: Data scarcity has refrained deep learning models from making greater progress in prostate images analysis using multiparametric MRI . In this paper, an efficient convolutional neural network (CNN)was developed to classify lesion malignancy for prostate cancer patients, based on which model interpretation was systematically analyzed to bridge the gap between natural images and MR images, which is the first one of its kind in the literature. The problem of small sample size was addressed and successfully tackled by feeding the intermediate features into a traditional classification algorithm known as weighted extreme learning machine, with imbalanced distribution among output categories taken into consideration. Model trained on public data set was able to generalize to data from an independent institution to make accurate prediction. The generated saliency map was found to overlay well with the lesion and could benefit clinicians for diagnosing purpose .
Revised: Data scarcity has refrained deep learning models directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. This work designs a customized workflow for the small and imbalanced data set of prostate mpMRI where features were extracted from a deep learning model and then analyzed by a traditional machine learning classifier. In addition, this work contributes to revealing how deep learning models interpret mpMRI for prostate cancer patients stratification .
Edit intent: coherence
Original:  While critical connectivity has been found to be low in these model systems, typically around k=2, which is unrealistic for natural systems, a recent study utilizing the excitation/inhibition ratio as control parameter found a new, nearly degree-independent critical point when connectivity is large.
Revised:  While critical connectivity has been found to be low in these model systems, typically around k=2, which is unrealistic for natural systems, a recent study utilizing the excitation-inhibition ratio as the control parameter found a new, nearly degree-independent critical point when connectivity is large.
Edit intent: fluency
Original:  While critical connectivity has been found to be low in these model systems, typically around k=2, which is unrealistic for natural systems, a recent study utilizing the excitation/inhibition ratio as control parameter found a new, nearly degree-independent critical point when connectivity is large.
Revised:  While critical connectivity has been found to be low in these model systems, typically around k=2, which is unrealistic for natural systems, a recent study utilizing the excitation/inhibition ratio as control parameter found a new, nearly degree independent, critical point when connectivity is large.
Edit intent: fluency
Original:  Previous methods that have been used in order to include domain growth to on-lattice reaction-diffusion models cause a build up of particles on the boundaries of the domain, which is particularly obvious when diffusion is low  .
Revised:  A previously well-used method in order to include domain growth to on-lattice reaction-diffusion models cause a build up of particles on the boundaries of the domain, which is particularly obvious when diffusion is low .
Edit intent: clarity
Original:  Previous methods that have been used in order to include domain growth to on-lattice reaction-diffusion models cause a build up of particles on the boundaries of the domain, which is particularly obvious when diffusion is low  .
Revised:  Previous methods that have been used in order to incorporate domain growth into on-lattice reaction-diffusion models cause a build up of particles on the boundaries of the domain, which is particularly obvious when diffusion is low .
Edit intent: fluency
Original:  Previous methods that have been used in order to include domain growth to on-lattice reaction-diffusion models cause a build up of particles on the boundaries of the domain, which is particularly obvious when diffusion is low  .
Revised:  Previous methods that have been used in order to include domain growth to on-lattice reaction-diffusion models causes a build up of particles on the boundaries of the domain, which is particularly obvious when diffusion is low .
Edit intent: fluency
Original:  Previous methods that have been used in order to include domain growth to on-lattice reaction-diffusion models cause a build up of particles on the boundaries of the domain, which is particularly obvious when diffusion is low  .
Revised:  Previous methods that have been used in order to include domain growth to on-lattice reaction-diffusion models cause a build up of particles on the boundaries of the domain, which is particularly evident when diffusion is low .
Edit intent: clarity
Original:  Previous methods that have been used in order to include domain growth to on-lattice reaction-diffusion models cause a build up of particles on the boundaries of the domain, which is particularly obvious when diffusion is low  .
Revised:  Previous methods that have been used in order to include domain growth to on-lattice reaction-diffusion models cause a build up of particles on the boundaries of the domain, which is particularly obvious when diffusion is low in comparison to the rate of domain growth .
Edit intent: meaning-changed
Original:  Here, we present a new method that addresses this problem , and demonstrate that it is accurate even for scenarios in which the previous method fails  .
Revised:  Here, we present a new method that addresses this problem , and demonstrate that it is accurate even for scenarios in which the previous method fails . Further, we discuss for which parameter regimes it is feasible to continue using the original method due to diffusion dominating the domain growth mechanism .
Edit intent: meaning-changed
Original:  For this purpose, we combine three different deep neural network structures in a novel way: A feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of economic state processes , and a generative adversarial network to identify the portfolio strategies with the most unexplained pricing information.
Revised:  For this purpose, we combine three different deep neural network structures in a novel way: a feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of economic state processes , and a generative adversarial network to identify the portfolio strategies with the most unexplained pricing information.
Edit intent: fluency
Original:  For this purpose, we combine three different deep neural network structures in a novel way: A feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of economic state processes , and a generative adversarial network to identify the portfolio strategies with the most unexplained pricing information.
Revised:  For this purpose, we combine three different deep neural network structures in a novel way: A feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of economic state processes and a generative adversarial network to identify the portfolio strategies with the most unexplained pricing information.
Edit intent: fluency
Original:  For this purpose, we combine three different deep neural network structures in a novel way: A feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of economic state processes , and a generative adversarial network to identify the portfolio strategies with the most unexplained pricing information.
Revised:  For this purpose, we combine three different deep neural network structures in a novel way: A feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of economic state processes , and a generative adversarial network to identify the portfolio strategies with the least explained pricing information.
Edit intent: clarity
Original:  Empirically, our approach outperforms out-of-sample all other benchmark approaches: Our optimal portfolio has an annual Sharpe Ratio of 2.1 , we explain 8\% of the variation in individual stock returns and explain over 90\% of average returns for all anomaly sorted portfolios.
Revised:  Empirically, our approach outperforms out-of-sample all other benchmark approaches: Our optimal portfolio has an annual Sharpe Ratio of 2.6 , we explain 8\% of the variation in individual stock returns and explain over 90\% of average returns for all anomaly sorted portfolios.
Edit intent: meaning-changed
Original: We estimate a general non-linear asset pricing model  with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information.
Revised: We propose a novel approach to estimate asset pricing models for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. Our general non-linear asset pricing model with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information.
Edit intent: meaning-changed
Original:  For this purpose, we combine three different deep neural network structures in a novel way: a feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of  economic state processes and a generative adversarial network to identify the portfolio strategies with the least explained pricing information .
Revised:  Our asset pricing model outperforms out-of-sample all other benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors. We trace its superior performance to including the no-arbitrage constraint in the estimation and to accounting for macroeconomic conditions and non-linear interactions between firm-specific characteristics. Our generative adversarial network enforces no-arbitrage by identifying the portfolio strategies with the most pricing information. Our recurrent Long-Short-Term-Memory network to find a small set of economic state processes and a generative adversarial network to identify the portfolio strategies with the least explained pricing information .
Edit intent: meaning-changed
Original:  For this purpose, we combine three different deep neural network structures in a novel way: a feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of  economic state processes and a generative adversarial network to identify the portfolio strategies with the least explained pricing information .
Revised:  For this purpose, we combine three different deep neural network structures in a novel way: a feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network finds a small set of economic state processes and a generative adversarial network to identify the portfolio strategies with the least explained pricing information .
Edit intent: fluency
Original:  For this purpose, we combine three different deep neural network structures in a novel way: a feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of  economic state processes and a generative adversarial network to identify the portfolio strategies with the least explained pricing information .
Revised:  For this purpose, we combine three different deep neural network structures in a novel way: a feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of hidden economic state processes and a generative adversarial network to identify the portfolio strategies with the least explained pricing information .
Edit intent: clarity
Original:  For this purpose, we combine three different deep neural network structures in a novel way: a feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of  economic state processes and a generative adversarial network to identify the portfolio strategies with the least explained pricing information .
Revised:  For this purpose, we combine three different deep neural network structures in a novel way: a feedforward network to capture non-linearities, a recurrent Long-Short-Term-Memory network to find a small set of economic state processes . A feedforward network captures the non-linear effects of the conditioning variables .
Edit intent: meaning-changed
Original:  Our model allows us to understand what are the key factors that drive asset prices to identify mispricing of stocks and to generate the mean-variance efficient portfolio. Empirically, our approach outperforms out-of-sample all other benchmark approaches: Our optimal portfolio has an annual Sharpe Ratio of 2.6, we explain 8\% of the variation in individual stock returns and explain over 90\% of average returns for all anomaly sorted portfolios .
Revised:  Our model allows us to understand what are the key factors that drive asset prices and generate profitable investment strategies .
Edit intent: clarity
Original:  We estimate the stochastic discount factor that explains all asset returns from the conditional moment constraints implied by no-arbitrage  .
Revised:  We estimate the stochastic discount factor that explains all asset returns from the conditional moment constraints implied by no-arbitrage condition as criterion function, to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series .
Edit intent: meaning-changed
Original:  Our asset pricing model outperforms out-of-sample all other benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors . We trace its superior performance to including the no-arbitrage constraint in the estimation and to accounting for macroeconomic conditions and non-linear interactions between firm-specific characteristics. Our generative adversarial network enforces no-arbitrage by identifying the portfolio strategies with the most pricing information. Our recurrent Long-Short-Term-Memory network finds a small set of hidden economic state processes. A feedforward network captures the non-linear effects of the conditioning variables. Our model allows us to identify the key factors that drive asset prices and generate profitable investment strategies .
Revised:  Our asset pricing model outperforms out-of-sample all other benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices and generate profitable investment strategies .
Edit intent: coherence
Original: Dynamical chaos - i.e., exponential sensitivity to small perturbations - is thought to play a key functional role in a diverse range of biological systems , from underpinning flexible information processing in the brain to boosting cellular survival rates through the promotion of heterogeneous gene expression.
Revised: Dynamical chaos - i.e., exponential sensitivity to small perturbations - is thought to play a key functional role in a diverse range of complex biological processes , from underpinning flexible information processing in the brain to boosting cellular survival rates through the promotion of heterogeneous gene expression.
Edit intent: clarity
Original: Dynamical chaos - i.e., exponential sensitivity to small perturbations - is thought to play a key functional role in a diverse range of complex biological processes, from underpinning flexible information processing in the brain to boosting cellular survival rates through the promotion of heterogeneous gene expression .
Revised: Chaos, or exponential sensitivity to small perturbations - is thought to play a key functional role in a diverse range of complex biological processes, from underpinning flexible information processing in the brain to boosting cellular survival rates through the promotion of heterogeneous gene expression .
Edit intent: clarity
Original: Dynamical chaos - i.e., exponential sensitivity to small perturbations - is thought to play a key functional role in a diverse range of complex biological processes, from underpinning flexible information processing in the brain to boosting cellular survival rates through the promotion of heterogeneous gene expression .
Revised: Dynamical chaos - i.e., exponential sensitivity to small perturbations , appears everywhere in nature. Moreover, chaos is predicted to play diverse functional roles in living systems .
Edit intent: clarity
Original:  But, classic chaos-detection tools are highly sensitive to measurement noise , which has made it difficult to distinguish between stochastic, periodic, and chaotic biological processes. To aid in the experimental study of chaotic systems, we here combine several new mathematical tools into a fully automated processing pipeline, which can accurately assess the presence and degree of chaos in a diverse range of measurements, biological or otherwise, even when those measurements are noisy , and even for difficult edge cases.
Revised:  But, classic chaos-detection tools are highly sensitive to measurement noise , which has made it difficult to detect chaos in domains, like biology, where measurements are noisy. However, newer tools promise to overcome these limitations. Here, we combine several such tools into an automated processing pipeline, which can accurately assess the presence and degree of chaos in a diverse range of measurements, biological or otherwise, even when those measurements are noisy , and even for difficult edge cases.
Edit intent: clarity
Original:  To aid in the experimental study of chaotic systems, we here combine several new mathematical tools into a fully automated processing pipeline, which can accurately assess the presence and degree of chaos in a diverse range of measurements, biological or otherwise, even when those measurements are noisy , and even for difficult edge cases.
Revised:  To aid in the experimental study of chaotic systems, we here combine several new mathematical tools into a fully automated processing pipeline, and show that our pipeline can detect the presence (or absence) of chaos in a diverse range of measurements, biological or otherwise, even when those measurements are noisy , and even for difficult edge cases.
Edit intent: coherence
Original:  To aid in the experimental study of chaotic systems, we here combine several new mathematical tools into a fully automated processing pipeline, which can accurately assess the presence and degree of chaos in a diverse range of measurements, biological or otherwise, even when those measurements are noisy , and even for difficult edge cases.
Revised:  To aid in the experimental study of chaotic systems, we here combine several new mathematical tools into a fully automated processing pipeline, which can accurately assess the presence and degree of chaos in noisy recordings, even for difficult edge cases.
Edit intent: clarity
Original:  As a first-pass application of our method, we provide evidence in favor of the hypothesis that heart rate variability is not chaotic  , and instead reflects a nonlinear stochastic process in both health and disease.
Revised:  As a first-pass application of our method, we provide evidence in favor of the hypothesis that heart rate variability is not chaotic as some have proposed , and instead reflects a nonlinear stochastic process in both health and disease.
Edit intent: meaning-changed
Original:  As a first-pass application of our method, we provide evidence in favor of the hypothesis that heart rate variability is not chaotic  , and instead reflects a nonlinear stochastic process in both health and disease.
Revised:  As a first-pass application of our method, we provide evidence in favor of the hypothesis that heart rate variability is not chaotic , and instead reflects a stochastic process in both health and disease.
Edit intent: clarity
Original:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other ``structural'' features of the model, e.g. equilibrium conditions, are maintained.
Revised:  In particular, we show how to construct the smallest and largest values of the counterfactual as the distribution of unobservables spans nonparametric neighborhoods of the researcher's assumed specification while other `structural' features of the model, e.g. equilibrium conditions, are maintained.
Edit intent: fluency
Original: Researchers frequently make parametric assumptions about the distribution of unobservables when formulating structural models. Such assumptions are typically motived by computational convenience rather than economic theory and are often untestable. Counterfactuals can be particularly sensitive to such assumptions, threatening the credibility of structural modeling exercises. To address this issue, we leverage insights from the literature on ambiguity and model uncertainty to propose a tractable econometric framework for characterizing the sensitivity of counterfactuals with respect to a researcher's assumptions about the distribution of unobservables in a class of structural models.
Revised: We propose a framework for characterizing the sensitivity of counterfactuals with respect to a researcher's assumptions about the distribution of unobservables in a class of structural models.
Edit intent: meaning-changed