wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To estimate the treatment effect, researchers usually regress their outcome on a treatment indicator and pair fixed effects, and cluster standard errors at the unit-of-randomization level , namely, at the village level in our example .
Revised:  To estimate the treatment effect, researchers usually regress their outcome on a treatment indicator and pair fixed effects, clustering standard errors at the unit-of-randomization level , namely, at the village level in our example .
Edit intent: coherence
Original:  First, we designed a deep convolutional neural network (CNN) that took advantage of state-of-the-art modeling and solution techniques to extract stress metrics for each type of stress. Our CNN model was found to successfully distinguish between the low and high stress environments due to considering multiple factors such as planting/harvest dates, daily weather, and soil conditions. Then, we conducted a linear regression of the yield of hybrid against each stress metric, and classified the hybrid based on the slope of the regression line, since the slope of the regression line showed how sensitive a hybrid was to a specific environmental stress .
Revised:  In this paper, we present an unsupervised approach to solving this problem, which was recognized as one of the winners in the 2019 Syngenta Crop Challenge .
Edit intent: coherence
Original:  We also consider  the simple Bonferroni correction for multiple hypothesis testing, fixing it for the case of positive common correlation among assets, and a chi-bar square test against one-sided alternatives  .
Revised:  We also consider several alternative procedures, including the simple Bonferroni correction for multiple hypothesis testing, fixing it for the case of positive common correlation among assets, and a chi-bar square test against one-sided alternatives .
Edit intent: clarity
Original:  We also consider  the simple Bonferroni correction for multiple hypothesis testing, fixing it for the case of positive common correlation among assets, and a chi-bar square test against one-sided alternatives  .
Revised:  We also consider the simple Bonferroni correction for multiple hypothesis testing, fixing it for the case of positive common correlation among assets, and a chi-bar square test against one-sided alternatives , Follman's test, and Hansen's asymptotic adjustments .
Edit intent: meaning-changed
Original:  The conditional estimation test has low power under the alternative where there is little spread in the signal noise ratios of the assets, and high power under the alternative where a single asset has high signal noise ratio .
Revised:  The conditional estimation test has low power under the alternative where there is little spread in the signal noise ratios of the assets, and high power under the alternative where a single asset has high signal-noise ratio. Unlike the alternative procedures, it appears to enjoy rejection probabilities montonic in the signal-noise ratio of the selected asset .
Edit intent: meaning-changed
Original: Electrical stimulation of neural populations is a key tool for understanding neural dynamics and developing treatments. To investigate the effects of external stimulation on a basic cortical motif, we analyse the dynamical properties of an efficient mean-field neural mass model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons  and validate the results using detailed network simulations.
Revised: Electrical stimulation of neural populations is a key tool for understanding neural population dynamics and ultimately for developing clinical treatments. However, computational models of cortical networks of spiking neurons are inherently hard to simulate and analyze. We present an efficient mean-field neural mass model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons and validate the results using detailed network simulations.
Edit intent: meaning-changed
Original:  To investigate the effects of external stimulation on a basic cortical motif, we analyse the dynamical properties of an efficient mean-field neural mass model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons  and validate the results using detailed network simulations.
Revised:  To investigate the effects of external stimulation on a basic cortical motif, we analyse the dynamical properties of an efficient mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons and validate the results using detailed network simulations.
Edit intent: clarity
Original:  To investigate the effects of external stimulation on a basic cortical motif, we analyse the dynamical properties of an efficient mean-field neural mass model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons  and validate the results using detailed network simulations.
Revised:  To investigate the effects of external stimulation on a basic cortical motif, we analyse the dynamical properties of an efficient mean-field neural mass model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons of which we analyse the dynamical properties and validate the results using detailed network simulations.
Edit intent: coherence
Original:  The state space of the mean-field model and of the detailed networkare closely related. They reveal asynchronous up and down-states, bistable regions , and oscillatory regions corresponding to fast excitation-inhibition and slow excitation-adaptation feedback loops.
Revised:  Bifurcation diagrams reflecting the network's state reveal asynchronous up and down-states, bistable regions , and oscillatory regions corresponding to fast excitation-inhibition and slow excitation-adaptation feedback loops.
Edit intent: clarity
Original:  We present an efficient mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons of which we analyse the dynamical properties and validate the results using detailed network simulations.
Revised:  We evaluate a reduced mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons of which we analyse the dynamical properties and validate the results using detailed network simulations.
Edit intent: meaning-changed
Original:  We present an efficient mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons of which we analyse the dynamical properties and validate the results using detailed network simulations.
Revised:  We present an efficient mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons which can be used to efficiently study the effects of electrical stimulation on large neural populations. The rich dynamical properties of this basic cortical model are described in detail and validated using large network simulations.
Edit intent: meaning-changed
Original:  In this dynamical landscape, electrical stimuli can cause state transitions, such as turning on and off oscillations  .
Revised:  The biophysical parameters of the AdEx neuron can be coupled to an electric field with realistic field strengths which then can be propagated up to the population description. We show how on the edge of bifurcation, direct electrical inputs cause network state transitions, such as turning on and off oscillations .
Edit intent: meaning-changed
Original:  A critical feature of this architecture is that it is both higher-order and asymmetric: there is a vast number of perceptual states is nested under "present", but a much smaller number of possible states is nested under "absent".
Revised:  A critical feature of this architecture is that it is both higher-order and asymmetric: a vast number of perceptual states is nested under "present", but a much smaller number of possible states is nested under "absent".
Edit intent: clarity
Original:  A critical feature of this architecture is that it is both higher-order and asymmetric: there is a vast number of perceptual states is nested under "present", but a much smaller number of possible states is nested under "absent".
Revised:  A critical feature of this architecture is that it is both higher-order and asymmetric: there is a vast number of perceptual states is nested under "present", but a much smaller number of possible states nested under "absent".
Edit intent: fluency
Original:  These results show that diversity between hardware components can be exploited to achieve %DIFDELCMD < {\em %%% both fast%DIFDELCMD < {\em %%% and accurate control performance using slow or inaccurate hardware.
Revised:  These results show that diversity between hardware components can be exploited to achieve %DIFDELCMD < {\em %%% both fast%DIFDELCMD < {\em %%% and accurate control performance using slow or inaccurate hardware.
Edit intent: fluency
Original:  These results show that diversity between hardware components can be exploited to achieve %DIFDELCMD < {\em %%% both fast%DIFDELCMD < {\em %%% and accurate control performance using slow or inaccurate hardware.
Revised:  These results show that diversity between hardware components can be exploited to achieve %DIFDELCMD < {\em %%% fast%DIFDELCMD < {\em %%% and accurate control performance using slow or inaccurate hardware.
Edit intent: coherence
Original:  These results show that diversity between hardware components can be exploited to achieve %DIFDELCMD < {\em %%% both fast%DIFDELCMD < {\em %%% and accurate control performance using slow or inaccurate hardware.
Revised:  These results show that diversity between hardware components can be exploited to achieve %DIFDELCMD < {\em %%% both %DIFDELCMD < {\em %%% and accurate control performance using slow or inaccurate hardware.
Edit intent: clarity
Original:  These results show that diversity between hardware components can be exploited to achieve %DIFDELCMD < {\em %%% both fast%DIFDELCMD < {\em %%% and accurate control performance using slow or inaccurate hardware.
Revised:  These results show that diversity between hardware components can be exploited to achieve %DIFDELCMD < {\em %%% both fast%DIFDELCMD < {\em %%% both fast and accurate control performance using slow or inaccurate hardware.
Edit intent: fluency
Original: Human sensorimotor control exhibits remarkable speed and accuracy, as celebrated in Fitts' law for reaching . Much less studied is how this is possible despite being implemented by neurons and muscle components with severe speed-accuracy tradeoffs (SATs). Here we develop a theory that connects the SATs at the system and hardware levels, and use it to explain Fitts' law for reaching and related laws.
Revised: Human sensorimotor control exhibits remarkable speed and accuracy, as celebrated in Fitts' Law for reaching and pointing. While Fitts related this to Shannon's channel capacity theorem, despite widespread study of Fitts' Law, a theory that connects the SATs at the system and hardware levels, and use it to explain Fitts' law for reaching and related laws.
Edit intent: meaning-changed
Original:  Here we develop a theory that connects the SATs at the system and hardware levels, and use it to explain Fitts' law for reaching and related laws.
Revised:  Here we develop a theory that connects the SATs at the system and hardware level has not emerged. Here we describe a theory that connects hardware (neurons and muscles with inherent severe speed-accuracy tradeoffs) with system level control to explain Fitts' law for reaching and related laws.
Edit intent: meaning-changed
Original:  Such ``diversity sweet spots'' (DSSs ) are ubiquitous in biology and technology, and explain why large heterogeneities exist in biological and technical components and how both engineers and natural selection routinely evolve fast and accurate systems from imperfect hardware.
Revised:  Such "diversity-enabled sweet spots" (DESSs ) are ubiquitous in biology and technology, and explain why large heterogeneities exist in biological and technical components and how both engineers and natural selection routinely evolve fast and accurate systems from imperfect hardware.
Edit intent: clarity
Original:  Such ``diversity sweet spots'' (DSSs ) are ubiquitous in biology and technology, and explain why large heterogeneities exist in biological and technical components and how both engineers and natural selection routinely evolve fast and accurate systems from imperfect hardware.
Revised:  Such ``diversity sweet spots'' (DSSs ) are ubiquitous in biology and technology, and explain why large heterogeneities exist in biological and technical components and how both engineers and natural selection routinely evolve fast and accurate systems using imperfect hardware.
Edit intent: clarity
Original:  The latter arises naturally due to our requirements that diversification measures should be leverage invariant and related to the tail properties of the distribution of portfolio returns. This paper introduces this new framework and its relationship to standardized higher order moments of portfolio returns.
Revised:  The latter arises naturally due to our requirements that diversification measures should be leverage invariant . We introduce this new framework and its relationship to standardized higher order moments of portfolio returns.
Edit intent: coherence
Original:  This paper introduces this new framework and its relationship to standardized higher order moments of portfolio returns. Moreover, it addresses the main drawbacks of standard diversification methodologies which are based primarily on estimates of covariance matrices. Maximizing portfolio dimensionality leads to highly non-trivial optimization problems with objective functions which are typically non-convex with potentially multiple local optima.
Revised:  This paper introduces this new framework and argue the benefits relative to existing measures of diversification in the literature, before addressing the question of optimizing diversification or, equivalently, dimensionality. Maximising portfolio dimensionality leads to highly non-trivial optimization problems with objective functions which are typically non-convex with potentially multiple local optima.
Edit intent: meaning-changed
Original:  For problems of moderate size  , a deterministic Branch and Bound algorithm is developed, whereas for problems of larger size a stochastic global optimization algorithm based on Gradient Langevin Dynamics is given.
Revised:  For problems of moderate size and more akin to asset allocation problems , a deterministic Branch and Bound algorithm is developed, whereas for problems of larger size a stochastic global optimization algorithm based on Gradient Langevin Dynamics is given.
Edit intent: meaning-changed
Original:  In this paper, we make use of the Koopman observability gramian to develop an algorithm for optimal sensor placement of discrete time autonomous nonlinear dynamical systems .
Revised:  In URLanisms, genetic activity is often highly nonlinear, making it difficult to design libraries of promoters to act as reporters of the cell state. We make use of the Koopman observability gramian to develop an algorithm for optimal sensor placement of discrete time autonomous nonlinear dynamical systems .
Edit intent: meaning-changed
Original:  In this paper, we make use of the Koopman observability gramian to develop an algorithm for optimal sensor placement of discrete time autonomous nonlinear dynamical systems . The Koopman operator lifts nonlinear dynamics to a higher dimensional space where the dynamics evolve approximately linearly in time. Data in biology are often sampled sparsely in time , therefore a method is developed to compute a temporally fine-grained Koopman operator from the temporally coarse-grained Koopman operator .
Revised:  In this paper, we make use of the Koopman observability gramian to develop an algorithm for optimal sensor placement of discrete time autonomous nonlinear dynamical systems to ease the difficulty of design of the promoter library. This ease is enabled due to the fact that the Koopman operator represents the evolution of a nonlinear system linearly by lifting the states to an infinite-dimensional space of observables. The Koopman framework ideally demands high temporal resolution, but data in biology are often sampled sparsely in time , therefore a method is developed to compute a temporally fine-grained Koopman operator from the temporally coarse-grained Koopman operator .
Edit intent: meaning-changed
Original:  Data in biology are often sampled sparsely in time , therefore a method is developed to compute a temporally fine-grained Koopman operator from the temporally coarse-grained Koopman operator .
Revised:  Data in biology are often sampled sparsely in time . Therefore we compute what we call the temporally fine-grained Koopman operator from the temporally coarse-grained Koopman operator .
Edit intent: clarity
Original:  Data in biology are often sampled sparsely in time , therefore a method is developed to compute a temporally fine-grained Koopman operator from the temporally coarse-grained Koopman operator . In the case of noisy data, a closed form expression for the error in the coarse-grained Koopman operator is derived. A novel algorithm for optimal sensor placement is developed for the case of a fixed initial condition. The method is finally demonstrated on a simulation example  .
Revised:  Data in biology are often sampled sparsely in time , therefore a method is developed to compute a temporally fine-grained Koopman operator from the temporally coarse-grained Koopman operator , the latter of which is identified from the sparse data. The optimal placement of sensors then corresponds to maximizing the observability of the fine-grained system. We demonstrate the algorithm on a simulation example .
Edit intent: coherence
Original:  We studied the properties of simple recurrent neural networks trained to perform temporal tasks and also flow control tasks with temporal stimulus. We studied mainly three aspects: inner configuration sets, memory capacity with the scale of the models and finally immunity to induced damage on a trained network .
Revised:  We studied the properties of simple recurrent neural networks trained to perform temporal tasks and also flow control tasks with temporal stimuli. We present the results regarding three aspects: inner configuration sets, memory capacity with the scale of the models and finally immunity to induced damage on a trained network .
Edit intent: clarity
Original:  We studied the properties of RNN trained to perform temporal and flow control tasks with temporal stimuli. We present the results regarding three aspects: inner configuration sets, memory capacity with the scale and immunity to induced damage on trained networks .
Revised:  We studied the properties of RNN trained to perform temporal and flow control tasks with time-varying stimuli. Our results show that different RNNs can solve the same task by converging to different underlying dynamics and that the performance gracefully degrades either as network size is decreased, interval duration is increased, or connectivity is damaged .
Edit intent: meaning-changed
Original:  In this paper, we propose a graphical model for to infer the causal structure that links the multiple macroeconomic variables and the assessed risk parameters,  Stress Testing Network , in which the relationships between the macroeconomic variables and the risk parameter define a "relational graph" among their time-series, where related time-series are connected by an edge.
Revised:  In this paper, we propose a graphical model for the reconstruction of the causal structure that links the multiple macroeconomic variables and the assessed risk parameters, Stress Testing Network , in which the relationships between the macroeconomic variables and the risk parameter define a "relational graph" among their time-series, where related time-series are connected by an edge.
Edit intent: clarity
Original:  In this paper, we propose a graphical model for to infer the causal structure that links the multiple macroeconomic variables and the assessed risk parameters,  Stress Testing Network , in which the relationships between the macroeconomic variables and the risk parameter define a "relational graph" among their time-series, where related time-series are connected by an edge.
Revised:  In this paper, we propose a graphical model for to infer the causal structure that links the multiple macroeconomic variables and the assessed risk parameters, it is this structure that we call Stress Testing Network , in which the relationships between the macroeconomic variables and the risk parameter define a "relational graph" among their time-series, where related time-series are connected by an edge.
Edit intent: meaning-changed
Original:  In this paper, we propose a graphical model for to infer the causal structure that links the multiple macroeconomic variables and the assessed risk parameters,  Stress Testing Network , in which the relationships between the macroeconomic variables and the risk parameter define a "relational graph" among their time-series, where related time-series are connected by an edge.
Revised:  In this paper, we propose a graphical model for to infer the causal structure that links the multiple macroeconomic variables and the assessed risk parameters, Stress Testing Network (STN). In this model, the relationships between the macroeconomic variables and the risk parameter define a "relational graph" among their time-series, where related time-series are connected by an edge.
Edit intent: clarity
Original:  The model implements techniques for rapid, global , reversible, conservative equilibration of node states followed by slow, local , irreversible, dissipative adaptation of the edge states as a means to create multiscale order.
Revised:  The model integrates techniques for rapid, global , reversible, conservative equilibration of node states followed by slow, local , irreversible, dissipative adaptation of the edge states as a means to create multiscale order.
Edit intent: fluency
Original:  The model implements techniques for rapid, global , reversible, conservative equilibration of node states followed by slow, local , irreversible, dissipative adaptation of the edge states as a means to create multiscale order.
Revised:  The model implements techniques for rapid, large-scale , reversible, conservative equilibration of node states followed by slow, local , irreversible, dissipative adaptation of the edge states as a means to create multiscale order.
Edit intent: clarity
Original:  The model implements techniques for rapid, global , reversible, conservative equilibration of node states followed by slow, local , irreversible, dissipative adaptation of the edge states as a means to create multiscale order.
Revised:  The model implements techniques for rapid, global , reversible, conservative equilibration of node states and slow, small-scale , irreversible, dissipative adaptation of the edge states as a means to create multiscale order.
Edit intent: meaning-changed
Original:  The model implements techniques for rapid, global , reversible, conservative equilibration of node states followed by slow, local , irreversible, dissipative adaptation of the edge states as a means to create multiscale order.
Revised:  The model implements techniques for rapid, global , reversible, conservative equilibration of node states followed by slow, local , irreversible, dissipative adaptation of the edge states as a means to create multi-scale order.
Edit intent: fluency
Original: This work describes a thermodynamically motivated neural network model  that URLanizes to transport charge associated with internal and external potentials while in contact with a thermal bath. Isolated networks show multi-scale dynamics and evidence of phase transitions, and externally driven networks evolve to efficiently connect external positive and negative potentials.
Revised: This work describes a thermodynamically motivated neural network model that URLanizes to transport charge associated with internal and external potentials while in contact with a thermal reservoir.
Edit intent: clarity