wanyu/IteraTeR_full_sent huggingface dataset:
Original: We propose that homogeneous image regions may be used to quickly segment a visual scene, when an object hypothesis is not yet available. In a cooperative model of the konio-cellular and the parvo- or magno-cellular visual pathway of primates, we demonstrate how homogeneity-selective responses can be gained from LGN neurons using a spike-latency code, and how these responses can confine edge detection to borders between mid-sized objects. We propose that spike-latency-based homogeneity detection is a general neural principle that may apply to any sensory feature and modality. The mechanism is sensitive to exact spike timing, which makes it vulnerable to noise. Nevertheless we demonstrate how the mechanism can be made robust against realistic levels of neural cross talk in the brain .
Revised: In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance .
Edit intent: coherence
Original:  We show that biphasic synaptic events do yield very sharp post synaptic currents that maintain good separation of homogeneity responses in massively noisy environments. The same technique opens up a neural method of tuning neurons to varying degrees of homogeneity. The simple neural implementation, generality, robustness, and variable tuning make homogeneity-detection an attractive scheme of computation in spiking neural networks and in the brain .
Revised:  We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic postsynaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neural networks and in the brain .
Edit intent: clarity
Original:  Defined as ensembles of random networks with given features constrained to match those of an input (empirical) network, they may for example be used to identify important features of empirical networks and their effects on dynamical processes unfolding in the network.
Revised:  Defined as random networks with given features constrained to match those of an input (empirical) network, they may for example be used to identify important features of empirical networks and their effects on dynamical processes unfolding in the network.
Edit intent: clarity
Original:  Here we propose a unified framework for classifying and understanding microcanonical RRMs (MRRMs)  . Focusing on temporal networks, we  use this framework to build a taxonomy of MRRMs that proposes a canonical naming convention, classifies them, and deduces their effects on a range of important network features.
Revised:  Here we propose a unified framework for classifying and understanding microcanonical RRMs (MRRMs) that sample networks with uniform probability . Focusing on temporal networks, we use this framework to build a taxonomy of MRRMs that proposes a canonical naming convention, classifies them, and deduces their effects on a range of important network features.
Edit intent: meaning-changed
Original:  Here we propose a unified framework for classifying and understanding microcanonical RRMs (MRRMs)  . Focusing on temporal networks, we  use this framework to build a taxonomy of MRRMs that proposes a canonical naming convention, classifies them, and deduces their effects on a range of important network features.
Revised:  Here we propose a unified framework for classifying and understanding microcanonical RRMs (MRRMs) . Focusing on temporal networks, we survey applications of MRRMs found in literature, and we use this framework to build a taxonomy of MRRMs that proposes a canonical naming convention, classifies them, and deduces their effects on a range of important network features.
Edit intent: meaning-changed
Original:  We provide two tutorials showing applications of the MRRM framework to empirical temporal networks: 1) to analyze how different features of a network affect other features and 2) to analyze how such features affect a dynamic process in the network.
Revised:  We finally provide a tutorial showing how to apply a series of MRRMs to analyze how different features of a network affect other features and 2) to analyze how such features affect a dynamic process in the network.
Edit intent: clarity
Original:  We provide two tutorials showing applications of the MRRM framework to empirical temporal networks: 1) to analyze how different features of a network affect other features and 2) to analyze how such features affect a dynamic process in the network. We finally survey applications of MRRMs found in literature .
Revised:  We provide two tutorials showing applications of the MRRM framework to empirical temporal networks: 1) to analyze how different features of a network affect other features and 2) to analyze how such features affect a dynamic process in an empirical temporal network .
Edit intent: coherence
Original: A new quantized ion channel model, called five-anchor model, is proposed based on a simple hypothesis of the first principle of the protein beam in the ion channel : the polarity of the protein beam can be changed by electron transition.
Revised: A quantized physical framework, called the five-anchor model, is proposed based on a simple hypothesis of the first principle of the protein beam in the ion channel : the polarity of the protein beam can be changed by electron transition.
Edit intent: clarity
Original: A new quantized ion channel model, called five-anchor model, is proposed based on a simple hypothesis of the first principle of the protein beam in the ion channel : the polarity of the protein beam can be changed by electron transition.
Revised: A new quantized ion channel model, called five-anchor model, is developed for a general understanding of the working mechanism of ion channels. According to the hypotheses of this model, the following two basic physical principles are assigned to each anchor : the polarity of the protein beam can be changed by electron transition.
Edit intent: clarity
Original: A new quantized ion channel model, called five-anchor model, is proposed based on a simple hypothesis of the first principle of the protein beam in the ion channel : the polarity of the protein beam can be changed by electron transition. With this model , many unique properties and behaviors of ion channels can be explained, including the real physical working mechanism of optogenetics, the refractory period in activation of action potential, the stochastic gating pattern of single ion channel and the nerve conductance block .
Revised: A new quantized ion channel model, called five-anchor model, is proposed based on a simple hypothesis of the first principle of the protein beam in the ion channel : the polarity change induced by an electron transition and the mutual repulsion and attraction induced by an electrostatic force. Consequently , many unique properties and behaviors of ion channels can be explained, including the real physical working mechanism of optogenetics, the refractory period in activation of action potential, the stochastic gating pattern of single ion channel and the nerve conductance block .
Edit intent: meaning-changed
Original:  With this model , many unique properties and behaviors of ion channels can be explained, including the real physical working mechanism of optogenetics, the refractory period in activation of action potential, the stochastic gating pattern of single ion channel and the nerve conductance block .
Revised:  With this model , many unique phenomena, such as fast and slow inactivation, the stochastic gating pattern of single ion channel and the nerve conductance block .
Edit intent: clarity
Original:  With this model , many unique properties and behaviors of ion channels can be explained, including the real physical working mechanism of optogenetics, the refractory period in activation of action potential, the stochastic gating pattern of single ion channel and the nerve conductance block .
Revised:  With this model , many unique properties and behaviors of ion channels can be explained, including the real physical working mechanism of optogenetics, the refractory period in activation of action potential, the stochastic gating pattern and constant conductance of a single ion channel and the nerve conductance block .
Edit intent: clarity
Original:  With this model , many unique properties and behaviors of ion channels can be explained, including the real physical working mechanism of optogenetics, the refractory period in activation of action potential, the stochastic gating pattern of single ion channel and the nerve conductance block . Meanwhile, this model is consistent with our previous C-P theory. In this study, the probability calculus equation is amended and expanded to a more general scenario, thus called a general probability for differentiation with the probability calculus in C-P theory.
Revised:  With this model , many unique properties and behaviors of ion channels can be explained, including the real physical working mechanism of optogenetics, the refractory period in activation of action potential, the stochastic gating pattern of single ion channel , the difference between electrical and optical stimulation (optogenetics), nerve conduction block and the generation of an action potential, become intrinsic features of this physical model. Moreover, this model also provides a foundation for the probability equation used to calculate the results of electrical stimulation in our previous C-P theory.
Edit intent: meaning-changed
Original: We develop a theory for estimation of a high-dimensional sparse parameter \theta defined as a minimizer of a population loss function L_D(\theta,g_0)which, in addition to \theta, depends on a, potentially infinite dimensional, nuisance parameter g_0.
Revised: We propose an l1-regularized M-estimator for a high-dimensional sparse parameter \theta defined as a minimizer of a population loss function L_D(\theta,g_0)which, in addition to \theta, depends on a, potentially infinite dimensional, nuisance parameter g_0.
Edit intent: meaning-changed
Original: We develop a theory for estimation of a high-dimensional sparse parameter \theta defined as a minimizer of a population loss function L_D(\theta,g_0)which, in addition to \theta, depends on a, potentially infinite dimensional, nuisance parameter g_0. Our approach is based on estimating \theta via an \ell_1-regularized minimization of a sample analog of L_S(\theta, ), plugging in a first-stage estimate , computed on a hold-out sample.
Revised: We develop a theory for estimation of a high-dimensional sparse parameter ), plugging in a first-stage estimate , computed on a hold-out sample.
Edit intent: meaning-changed
Original:  Our approach is based on estimating \theta via an \ell_1-regularized minimization of a sample analog of L_S(\theta, ), plugging in a first-stage estimate , computed on a hold-out sample.
Revised:  Our approach is based on estimating \theta via an \ell_1-regularized minimization of a sample analog of L_S(\theta, that is identified by a class of semiparametric conditional moment restrictions (CMR). We estimate the nonparametric nuisance parameter by modern machine learning methods. Plugging the first-stage estimate , computed on a hold-out sample.
Edit intent: meaning-changed
Original:  Our approach is based on estimating \theta via an \ell_1-regularized minimization of a sample analog of L_S(\theta, ), plugging in a first-stage estimate , computed on a hold-out sample. We define a population loss to be (Neyman) orthogonal if the gradient of the loss with respect to \theta, has pathwise derivative with respect to g equal to zero, when evaluated at the true parameter and nuisance component.
Revised:  Our approach is based on estimating \theta via an \ell_1-regularized minimization of a sample analog of L_S(\theta, ), plugging in a first-stage estimate into the CMR, we construct the M-estimator loss function for the target parameter so that its gradient is insensitive (formally, Neyman-orthogonal) with respect to g equal to zero, when evaluated at the true parameter and nuisance component.
Edit intent: clarity
Original:  We define a population loss to be (Neyman) orthogonal if the gradient of the loss with respect to \theta, has pathwise derivative with respect to g equal to zero, when evaluated at the true parameter and nuisance component. We show that orthogonality implies a second-order impact of the first stage nuisance error on the second stage target parameter estimate. Our approach applies to both convex and non-convex losses, albeit the latter case requires a small adaptation of our method with a preliminary estimation step of the target parameter. Our result enables oracle convergence rates for \theta under assumptions on the first stage rates, typically of the order of n^{-1/4 . We apply our theory to high-dimensional versions of standard estimation problems in statistics and econometrics, such as: estimation of conditional moment models with missing data, estimation of structural utilities in games of incomplete information and estimation of treatment effects in regression models with non-linear link functions.
Revised:  We define a population loss to be (Neyman) orthogonal if the gradient of the loss with respect to \theta, has pathwise derivative with respect to the first-stage regularization bias. As a result, the estimator achieves oracle convergence rate \sqrt{k \log p/n . We apply our theory to high-dimensional versions of standard estimation problems in statistics and econometrics, such as: estimation of conditional moment models with missing data, estimation of structural utilities in games of incomplete information and estimation of treatment effects in regression models with non-linear link functions.
Edit intent: meaning-changed
Original:  Our result enables oracle convergence rates for \theta under assumptions on the first stage rates, typically of the order of n^{-1/4 . We apply our theory to high-dimensional versions of standard estimation problems in statistics and econometrics, such as: estimation of conditional moment models with missing data, estimation of structural utilities in games of incomplete information and estimation of treatment effects in regression models with non-linear link functions.
Revised:  Our result enables oracle convergence rates for \theta under assumptions on the first stage rates, typically of the order of n^{-1/4 . We apply our results to conditional moment models with missing data, estimation of structural utilities in games of incomplete information and estimation of treatment effects in regression models with non-linear link functions.
Edit intent: clarity
Original:  Our result enables oracle convergence rates for \theta under assumptions on the first stage rates, typically of the order of n^{-1/4 . We apply our theory to high-dimensional versions of standard estimation problems in statistics and econometrics, such as: estimation of conditional moment models with missing data, estimation of structural utilities in games of incomplete information and estimation of treatment effects in regression models with non-linear link functions.
Revised:  Our result enables oracle convergence rates for \theta under assumptions on the first stage rates, typically of the order of n^{-1/4 . We apply our theory to high-dimensional versions of standard estimation problems in statistics and econometrics, such as: estimation of conditional moment models with missing data, games of incomplete information and estimation of treatment effects in regression models with non-linear link functions.
Edit intent: clarity
Original:  Our result enables oracle convergence rates for \theta under assumptions on the first stage rates, typically of the order of n^{-1/4 . We apply our theory to high-dimensional versions of standard estimation problems in statistics and econometrics, such as: estimation of conditional moment models with missing data, estimation of structural utilities in games of incomplete information and estimation of treatment effects in regression models with non-linear link functions.
Revised:  Our result enables oracle convergence rates for \theta under assumptions on the first stage rates, typically of the order of n^{-1/4 . We apply our theory to high-dimensional versions of standard estimation problems in statistics and econometrics, such as: estimation of conditional moment models with missing data, estimation of structural utilities in games of incomplete information and treatment effects in regression models with non-linear link functions.
Edit intent: fluency
Original: We propose an l1-regularized M-estimator for a high-dimensional sparse parameter that is identified by a class of semiparametric conditional moment restrictions ( CMR).
Revised: We propose an l1-regularized M-estimator for a high-dimensional sparse M-estimation by allowing the loss function to depend on a functional nuisance parameter, which we estimate by modern machine learning tools. For a class of semiparametric conditional moment restrictions ( CMR).
Edit intent: meaning-changed
Original: We propose an l1-regularized M-estimator for a high-dimensional sparse parameter that is identified by a class of semiparametric conditional moment restrictions ( CMR). We estimate the nonparametric nuisance parameter by modern machine learning methods. Plugging the first-stage estimate into the CMR, we construct the M-estimator loss functionfor the target parameter so that its gradient is insensitive (formally, Neyman-orthogonal) with respect to the first-stage regularization bias.
Revised: We propose an l1-regularized M-estimator for a high-dimensional sparse parameter that is identified by a class of semiparametric conditional moment restrictions ( CMRs), we explicitly derive the loss function. We first adjust the moment function so that the gradient of the future M-estimator loss is insensitive (formally, Neyman-orthogonal) with respect to the first-stage regularization bias.
Edit intent: clarity
Original:  As a result, the estimator achieves oracle convergence rate \sqrt{k \log p/n oracle knows the true first stage and solves only a parametric problem.
Revised:  We then take the loss function to be an indefinite integral of the adjusted moment function with respect to the single-index. The proposed l1-regularized M-estimator achieves the oracle convergence rate \sqrt{k \log p/n oracle knows the true first stage and solves only a parametric problem.
Edit intent: meaning-changed
Original: This paper contributes to the literature on high-dimensional sparse M-estimation by allowing the loss function to depend on a functional nuisance parameter, which we estimate by modern machine learning tools.
Revised: This paper contributes to the literature on high-dimensional sparse parameter identified by a single index conditional moment restriction (CMR). In addition to this parameter, the moment function can also depend on a functional nuisance parameter, which we estimate by modern machine learning tools.
Edit intent: meaning-changed
Original: This paper contributes to the literature on high-dimensional sparse M-estimation by allowing the loss function to depend on a functional nuisance parameter, which we estimate by modern machine learning tools.
Revised: This paper contributes to the literature on high-dimensional sparse M-estimation by allowing the loss function to depend on a nuisance function, such as the propensity score or the conditional choice probability, which we estimate by modern machine learning tools.
Edit intent: meaning-changed
Original:  We first adjust the moment function so that the gradient of the future M-estimator loss is insensitive (formally, Neyman-orthogonal) with respect to the first-stage regularization bias  .
Revised:  We first adjust the moment function so that the gradient of the future M-estimator loss is insensitive (formally, Neyman-orthogonal) with respect to the first-stage regularization bias , preserving the single index property .
Edit intent: meaning-changed
Original:  Our framework nests a novel approach to modeling heterogeneous treatment effects with a binary dependent variable. In addition, we apply our results to conditional moment models with missing data and static games of incomplete information. Finally, we generalize our results to generic extremum estimation with a nuisance component .
Revised:  We demonstrate our method by estimating the short-term heterogeneous impact of Connecticut's Jobs First welfare reform experiment on women's welfare participation decision .
Edit intent: meaning-changed
Original:  A sequence of large-scale regressions with LASSO is applied to reduce the dimensionality , and an overall penalty level is carefully chosen by a block multiplier bootstrap procedure to account for multiplicity of the equations and dependencies in the data.
Revised:  A sequence of regressions with many regressors using LASSO (Least Absolute Shrinkage and Selection Operator) is applied for variable selection purpose , and an overall penalty level is carefully chosen by a block multiplier bootstrap procedure to account for multiplicity of the equations and dependencies in the data.
Edit intent: meaning-changed
Original:  The method uses data from a first-stage pilot experiment to determine how to stratify in a second wave of the experiment, where the objective is to minimize the variance of an estimator for the average treatment effect (ATE).
Revised:  The method uses data from a first-wave experiment in order to determine how to stratify in a second wave of the experiment, where the objective is to minimize the variance of an estimator for the average treatment effect (ATE).
Edit intent: clarity
Original:  By using the pilot to estimate a stratification tree, we simultaneously select which covariates to use for stratification, how to stratify over these covariates, as well as the assignment probabilities within these strata.
Revised:  By using the first wave to estimate a stratification tree, we simultaneously select which covariates to use for stratification, how to stratify over these covariates, as well as the assignment probabilities within these strata.
Edit intent: meaning-changed
Original:  By using the pilot to estimate a stratification tree, we simultaneously select which covariates to use for stratification, how to stratify over these covariates, as well as the assignment probabilities within these strata.
Revised:  By using the pilot to estimate a stratification tree, we simultaneously select which covariates to use for stratification, how to stratify over these covariates, as well as the assign- ment probabilities within these strata.
Edit intent: fluency
Original:  In a simulation study, we find that our method is most effective when the response model exhibits some amount of "sparsity" with respect to the covariates, but can be effective in other contexts as well, as long as the pilot sample size used to estimate the stratification tree is not prohibitively small.
Revised:  In a simulation study, we find that our method is most effective when the response model exhibits some amount of "sparsity" with respect to the covariates, but can be effective in other contexts as well, as long as the first-wave sample size used to estimate the stratification tree is not prohibitively small.
Edit intent: meaning-changed