wanyu/IteraTeR_full_sent huggingface dataset:
Original:  As an alternative, we here explore an unsupervised algorithm applicable to intra-cranial EEG recordings that requires good sampling of the non-ictal periods , but imposes no demands on the amount of data during ictal activity.
Revised:  As an alternative, we here explore an unsupervised algorithm applicable to intra-cranial EEG recordings , detecting transient fluctuations of the power in each frequency band. Its simplicity makes it suitable for online implementation. Good sampling of the non-ictal periods , but imposes no demands on the amount of data during ictal activity.
Edit intent: meaning-changed
Original:  As an alternative, we here explore an unsupervised algorithm applicable to intra-cranial EEG recordings that requires good sampling of the non-ictal periods , but imposes no demands on the amount of data during ictal activity.
Revised:  As an alternative, we here explore an unsupervised algorithm applicable to intra-cranial EEG recordings that requires good sampling of the non-ictal periods is required, while no demands are imposed on the amount of data during ictal activity.
Edit intent: coherence
Original:  The algorithm analyses how the amount of power in each frequency band fluctuates ( an evaluation physicians are familiar with ) and can be implemented online. The analysis is performed electrode by electrode, thus providing the spatio-temporal sequence in which the affected regions are recruited into the crisis. We test it with 32 crisis registered in 5 patients , for which we also have the degree of loss of consciousness as determined from a behavioural analysis.
Revised:  We tested the method with 32 crisis registered in 5 patients , for which we also have the degree of loss of consciousness as determined from a behavioural analysis.
Edit intent: clarity
Original:  We test it with 32 crisis registered in 5 patients , for which we also have the degree of loss of consciousness as determined from a behavioural analysis. The method can achieve a precision of 85\% and a recall of 88\% in the identification of seizures, and 77\% and 88\% in the identification of recruited electrodes. The intuitive nature of the analysis allows us to identify certain physiological features that are correlated with the degree of loss of consciousness. For example, epileptic crisis in which the variance of the power in the alpha band increases at around seizure onset are particularly likely to impair consciousness. We conclude that the PCA of the distribution of power in different frequency bands provides information both about the detection and the characterization of epileptic seizures.
Revised:  We test it with 32 crisis registered in 5 patients . The area below the resulting receiver-operating characteristic curves was 87
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Original:  For a further introductory note and motivation on the theoretical concept we refer to 1 . A very detailed methodological overview of the concept of a smart derivative contract can be found in 3 .
Revised:  For a further introductory note and motivation on the theoretical concept we refer to 1 . A very detailed methodological overview of the concept of a smart derivative contract can be found in doi:10.2139/ssrn.3163074 .
Edit intent: clarity
Original:  Further, fitting by DNNs was several orders of magnitude quicker than the other methods  . Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data.
Revised:  Fitting by DNNs was several orders of magnitude quicker than the other methods . Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data.
Edit intent: coherence
Original:  Further, fitting by DNNs was several orders of magnitude quicker than the other methods  . Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data.
Revised:  Further, fitting by DNNs was several orders of magnitude quicker than the other methods but the networks may need to be re-trained for different acquisition protocols or imaged anatomical regions . Conclusion: DNNs are recommended for accurate and robust IVIM model fitting to DW-MRI data.
Edit intent: meaning-changed
Original: The time dependent behavior of the Kuramoto model, describing synchronization, has been studied numerically on small-world graphs. We determined the desynchronziation behavior , by solving this model via the 4th order Runge-Kutta algorithm on a large, weighted human connectome network and compared the results with those of a two-dimensional lattice, with additional random, long-range links.
Revised: The hypothesis, that cortical dynamics operates near criticality also suggests, that it exhibits universal critical exponents which marks the Kuramoto equation, a fundamental model for synchronization, as a prime candidate for an underlying universal model. Here, we determined the synchronization behavior of this model by solving it numerically on a large, weighted human connectome network and compared the results with those of a two-dimensional lattice, with additional random, long-range links.
Edit intent: meaning-changed
Original:  We determined the desynchronziation behavior , by solving this model via the 4th order Runge-Kutta algorithm on a large, weighted human connectome network and compared the results with those of a two-dimensional lattice, with additional random, long-range links. In the latter case a mean-field critical transition is expected and here we provide numerical results for the synchonization/desynchonization duration distributions. We find power-law tails, characterized by a critical exponent \tau_d \simeq 1.6(1). In case of the connectome we assumed a homeostatic state, by the application of normalized incoming weights . Since this graph has a topological dimension d < 4  a real synchronization phase transition is not possible in the thermodynamic limit, still we could locate a transition between partially synchronized and desynchronized states.
Revised:  We determined the desynchronziation behavior , by solving this model via the 4th order Runge-Kutta algorithm on a large, weighted human connectome network , containing 804092 nodes, in an assumed homeostatic state . Since this graph has a topological dimension d < 4 a real synchronization phase transition is not possible in the thermodynamic limit, still we could locate a transition between partially synchronized and desynchronized states.
Edit intent: meaning-changed
Original:  In case of the connectome we assumed a homeostatic state, by the application of normalized incoming weights . Since this graph has a topological dimension d < 4  a real synchronization phase transition is not possible in the thermodynamic limit, still we could locate a transition between partially synchronized and desynchronized states.
Revised:  In case of the connectome we assumed a homeostatic state, by the application of normalized incoming weights . Since this graph has a topological dimension d < 4 , a real synchronization phase transition is not possible in the thermodynamic limit, still we could locate a transition between partially synchronized and desynchronized states.
Edit intent: fluency
Original:  In this case the at the crossover point we found \tau_t \simeq 1.9(2), which is in the range of human brain experiments.abstract \le 
Revised:  In this case the at the crossover point we found \tau_t \simeq 1.9(2), which is in the range of human brain experiments.abstract For comparison, on a large two-dimensional lattice, having additional random, long-range links, we obtain a mean-field value: \tau_t \simeq 1.6(1). However, below the transition of the connectome we found global coupling control-parameter dependent exponents 1 < \tau_t\le 
Edit intent: meaning-changed
Original:  In this case the at the crossover point we found \tau_t \simeq 1.9(2), which is in the range of human brain experiments.abstract \le 
Revised:  In this case the at the crossover point we found \tau_t \simeq 1.9(2), which is in the range of human brain experiments.abstract \le 2, overlapping with the range of human brain experiments. We also studied the effects of random flipping of a small portion of link weights, mimicking a network with inhibitory interactions, and found similar results. The control-parameter dependent exponent suggests extended dynamical criticality below the transition point.
Edit intent: meaning-changed
Original: This paper is concerned with the application of recently developed statistical methods for inference in infectious disease models. We use hierarchical models as well as deterministic and stochastic epidemic processes based upon systems of ordinary differential equations. We illustrate the application of Hamiltonian Monte Carlo and Variational Inference using the freely available software Stan .
Revised: This paper is concerned with the application of recent statistical advances to inference of infectious disease dynamics. We describe the fitting of a class of epidemic models using Hamiltonian Monte Carlo and Variational Inference using the freely available software Stan .
Edit intent: clarity
Original:  We illustrate the application of Hamiltonian Monte Carlo and Variational Inference using the freely available software Stan . The methods are applied to real data from outbreaks as well as routinely collected observations.
Revised:  We illustrate the application of Hamiltonian Monte Carlo and Variational Inference using the freely available Stan software. We apply the two methods to real data from outbreaks as well as routinely collected observations.
Edit intent: clarity
Original:  Simulation results show that the UMPR with an appropriate data-dependent penalty outweighs some common estimators in  binary classification if the conditional probability of the binary outcome is misspecified , or a decision maker's preference is ignored .
Revised:  Simulation results show that the UMPR with an appropriate data-dependent penalty achieves larger generalized expected utility than some common estimators in binary classification if the conditional probability of the binary outcome is misspecified , or a decision maker's preference is ignored .
Edit intent: meaning-changed
Original:  Simulation results show that the UMPR with an appropriate data-dependent penalty outweighs some common estimators in  binary classification if the conditional probability of the binary outcome is misspecified , or a decision maker's preference is ignored .
Revised:  Simulation results show that the UMPR with an appropriate data-dependent penalty outweighs some common estimators in the traditional binary classification if the conditional probability of the binary outcome is misspecified , or a decision maker's preference is ignored .
Edit intent: meaning-changed
Original:  Simulation results show that the UMPR with an appropriate data-dependent penalty outweighs some common estimators in  binary classification if the conditional probability of the binary outcome is misspecified , or a decision maker's preference is ignored .
Revised:  Simulation results show that the UMPR with an appropriate data-dependent penalty outweighs some common estimators in binary classification if the conditional probability of the binary outcome is misspecified .
Edit intent: clarity
Original:  thus, its in-sample overfitting issue is similar to that of perceptron learning in the machine learning literature. Based on structural risk minimization, a utility-maximizing prediction rule (UMPR) is constructed to alleviate the in-sample overfitting of the maximum utility estimation.
Revised:  thus, its in-sample overfitting issue is similar to that of perceptron learning . A utility-maximizing prediction rule (UMPR) is constructed to alleviate the in-sample overfitting of the maximum utility estimation.
Edit intent: clarity
Original:  Simulation results show that the UMPR with an appropriate data-dependent penalty achieves larger generalized expected utility than some common estimators in the traditional binary classification if the conditional probability of the binary outcome is misspecified.
Revised:  Simulation results show that the UMPR with an appropriate data-dependent penalty achieves larger generalized expected utility than common estimators in the traditional binary classification if the conditional probability of the binary outcome is misspecified.
Edit intent: clarity
Original:  Simulation results show that the UMPR with an appropriate data-dependent penalty achieves larger generalized expected utility than some common estimators in the traditional binary classification if the conditional probability of the binary outcome is misspecified.
Revised:  Simulation results show that the UMPR with an appropriate data-dependent penalty achieves larger generalized expected utility than some common estimators in the binary classification if the conditional probability of the binary outcome is misspecified.
Edit intent: clarity
Original: This paper considers a general form of network dependence where dependence between two sets of random variables becomes weaker as their distance in a networkgets longer. We show that such network dependence cannot be embedded as a random field on a lattice in a Euclidean space with a fixed dimension when the maximum clique increases in size as the network grows. This paper applies Doukhan and Louhichi (1999) 's weak dependence notion to network dependence by measuring dependence strength by the covariance between nonlinearly transformed random variables.
Revised: This paper is concerned with cross-sectional dependence arising because observations are interconnected through an observed network. Following Doukhan and Louhichi (1999) 's weak dependence notion to network dependence by measuring dependence strength by the covariance between nonlinearly transformed random variables.
Edit intent: meaning-changed
Original:  This paper applies Doukhan and Louhichi (1999) 's weak dependence notion to network dependence by measuring dependence strength by the covariance between nonlinearly transformed random variables. While this approach covers examples such as strong mixing random fields on a graph and conditional dependency graphs, it is most useful when dependence arises through a large functional-causal system of equations. The main results of our paper include the law of large numbers , and the central limit theorem  .
Revised:  This paper applies Doukhan and Louhichi (1999) , we measure the strength of dependence by covariances of nonlinearly transformed variables. We provide a law of large numbers , and the central limit theorem .
Edit intent: clarity
Original:  We also propose a heteroskedasticity-autocorrelation consistent variance estimator and prove its consistencyunder regularity conditions. The finite sample performance of this latter estimator is investigated through a Monte Carlo simulation study .
Revised:  We also provide a method of calculating standard errors robust to general forms of network dependence. For that purpose, we rely on a network heteroskedasticity and autocorrelation consistent (HAC) variance estimator, and show its consistency. The results rely on conditions characterized by tradeoffs between the rate of decay of dependence across a network and network's denseness. Our approach can accommodate data generated by network formation models, random fields on graphs, conditional dependency graphs, and large functional-causal systems of equations .
Edit intent: meaning-changed
Original:  We introduce a nonlinear stochastic model accounting for the joint dynamics of  two cell types, either precursor or proliferative cells. The initial precursor cell population transitions progressively to a proliferative cell population, by both spontaneous and self-amplified processes. In the mean time, the proliferative cell population may start either a linear or exponential growing phase. A key issue is to determine whether cell proliferation is concomitant or posterior to cell transition, and to assess both the time needed for all precursor cells to complete transition and the corresponding increase in the  cell number with respect to the initial cell number.
Revised:  We introduce a nonlinear stochastic model accounting for the joint dynamics of two cell types, and allowing us to investigate the potential impact of a feedback from proliferative cells onto precursor cells. To tackle the key issue of whether cell proliferation is concomitant or posterior to cell transition, and to assess both the time needed for all precursor cells to complete transition and the corresponding increase in the cell number with respect to the initial cell number.
Edit intent: clarity
Original:  A key issue is to determine whether cell proliferation is concomitant or posterior to cell transition, and to assess both the time needed for all precursor cells to complete transition and the corresponding increase in the  cell number with respect to the initial cell number.
Revised:  A key issue is to determine whether cell proliferation is concomitant or posterior to cell awakening, we assess both the time needed for all precursor cells to complete transition and the corresponding increase in the cell number with respect to the initial cell number.
Edit intent: clarity
Original:  A key issue is to determine whether cell proliferation is concomitant or posterior to cell transition, and to assess both the time needed for all precursor cells to complete transition and the corresponding increase in the  cell number with respect to the initial cell number.
Revised:  A key issue is to determine whether cell proliferation is concomitant or posterior to cell transition, and to assess both the time needed for all precursor cells to awake, and the corresponding increase in the cell number with respect to the initial cell number.
Edit intent: clarity
Original:  A key issue is to determine whether cell proliferation is concomitant or posterior to cell transition, and to assess both the time needed for all precursor cells to complete transition and the corresponding increase in the  cell number with respect to the initial cell number.
Revised:  A key issue is to determine whether cell proliferation is concomitant or posterior to cell transition, and to assess both the time needed for all precursor cells to complete transition and the corresponding increase in the total cell number with respect to the initial cell number.
Edit intent: clarity
Original: We expand on Santos Silva and Tenreyro (2010)'s observation that estimates from Poisson models are not guaranteed to exist by documenting necessary and sufficient conditions for the existence of estimates for a wide class of generalized linear models (GLMs).
Revised: A fundamental problem with nonlinear estimation models is that estimates are not guaranteed to exist by documenting necessary and sufficient conditions for the existence of estimates for a wide class of generalized linear models (GLMs).
Edit intent: clarity
Original: We expand on Santos Silva and Tenreyro (2010)'s observation that estimates from Poisson models are not guaranteed to exist by documenting necessary and sufficient conditions for the existence of estimates for a wide class of generalized linear models (GLMs).
Revised: We expand on Santos Silva and Tenreyro (2010)'s observation that estimates from Poisson models are not guaranteed to exist . However, while non-existence is a well-studied issue for binary choice models, it presents significant challenges for other models as well and is not as well-understood in more general settings. These challenges are only magnified for models that feature many fixed effects and other high-dimensional parameters. We address the current ambiguity surrounding this topic by studying the conditions that govern the existence of estimates for a wide class of generalized linear models (GLMs).
Edit intent: meaning-changed
Original: This paper uses  potential outcome time series to provide a nonparametric framework for quantifying dynamic causal effects in macroeconometrics. This provides sufficient conditions for the nonparametric identification of dynamic causal effects as well as clarify the causal content of several common assumptions and methods in macroeconomics. Our key identifying assumption is shown to be non-anticipating treatments which enables nonparametric inference on dynamic causal effects. Next, we provide a formal definition of a `shock' and this leads to a shocked potential outcome time series. This is a nonparametric statement of the Frisch-Slutzky paradigm. The common additional assumptions that the causal effects are additive and that the treatments are shocks place substantial restrictions on the underlying dynamic causal estimands.We use this structure to causally interpret several common estimation strategies. We provide sufficient conditions under which local projections is causally interpretable and show that the standard assumptions for local projections with an instrument are not sufficient to identify dynamic causal effects. We finally show that the structural vector moving average form is causally equivalent to a restricted potential outcome time series under the usual invertibility assumption .
Revised: This paper uses potential outcome time series to provide a nonparametric framework for quantifying dynamic causal effects in time series experiments. Four innovations are developed in this paper: ``instrumental paths'', treatments which are ``shocks'', ``linear potential outcomes'' and the ``causal response function.'' Potential outcome time series are then used to provide a nonparametric causal interpretation of impulse response functions, generalized impulse response functions, local projections and LP-IV .
Edit intent: meaning-changed
Original:  Since the code is built around the reghdfe package  it has similar syntax and supports many of the same functionalities. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.
Revised:  Because the code is built around the reghdfe package it has similar syntax and supports many of the same functionalities. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.
Edit intent: fluency
Original:  Since the code is built around the reghdfe package  it has similar syntax and supports many of the same functionalities. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.
Revised:  Since the code is built around the reghdfe package , it has similar syntax and supports many of the same functionalities. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.
Edit intent: fluency
Original:  Because the code is built around the reghdfe package, it has similar syntax and supports many of the same functionalities  . ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.
Revised:  Because the code is built around the reghdfe package, it has similar syntax , supports many of the same functionalities . ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.
Edit intent: fluency
Original:  Because the code is built around the reghdfe package, it has similar syntax and supports many of the same functionalities  . ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.
Revised:  Because the code is built around the reghdfe package, it has similar syntax and supports many of the same functionalities , and benefits from reghdfe's fast convergence properties for computing high-dimensional least squares problems. Performance is further enhanced by some new techniques we introduce for accelerating HDFE-IRLS estimation specifically . ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.
Edit intent: meaning-changed