wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We also study app performance (web browsing and HTTP download) over 5G , and how Internet server selection affects the end-to-end performance for 5G users. Our experiments consume 10 TB of 5G data, conducted when 5G just made its debut in April 2019.
Revised:  We also study app performance (web browsing and HTTP download) over 5G . Our study consumes more than 15 TB of cellular data. Conducted when 5G just made its debut in April 2019.
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Original:  Our experiments consume 10 TB of 5G data, conducted when 5G just made its debut in April 2019. These experiments provide a baseline for studying how 5G performance evolves, and identify key research directions on improving 5G users' experience in a cross-layer manner. 
Revised:  Our experiments consume 10 TB of 5G data, conducted when 5G just made its debut , it provides a "baseline" for studying how 5G performance evolves, and identify key research directions on improving 5G users' experience in a cross-layer manner. 
Edit intent: coherence
Original:  These experiments provide a baseline for studying how 5G performance evolves, and identify key research directions on improving 5G users' experience in a cross-layer manner. 
Revised:  These experiments provide a baseline for studying how 5G performance evolves, and identify key research directions on improving 5G users' experience in a cross-layer manner. We have released the data collected from our study (referred to as 5Gophers) at URL
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Original:  Our approach  exploits the probability integral transform and relies on permuting estimated ``ranks'' instead of regression residuals.
Revised:  Our approach can be applied to many important prediction problems including cross-sectional prediction, k-step-ahead forecasts, synthetic controls and counterfactual prediction, and individual treatment effects prediction. Our method exploits the probability integral transform and relies on permuting estimated ``ranks'' instead of regression residuals.
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Original:  Our approach  exploits the probability integral transform and relies on permuting estimated ``ranks'' instead of regression residuals. Unlike residuals, these ranks  are independent of the covariates, which allows us to establish the conditional validity of the resulting prediction intervals under consistent estimation of the conditional distributions.
Revised:  Our approach exploits the probability integral transform and relies on permuting estimated ranks are independent of the covariates, which allows us to establish the conditional validity of the resulting prediction intervals under consistent estimation of the conditional distributions.
Edit intent: clarity
Original:  Unlike residuals, these ranks  are independent of the covariates, which allows us to establish the conditional validity of the resulting prediction intervals under consistent estimation of the conditional distributions. We also establish theoretical performance guarantees under arbitrary model misspecification.
Revised:  Unlike residuals, these ranks are independent of the covariates, which allows us to establish the conditional validity of the resulting prediction intervals under consistent estimation and also provide theoretical performance guarantees under arbitrary model misspecification.
Edit intent: clarity
Original:  We also establish theoretical performance guarantees under arbitrary model misspecification. The usefulness of the proposed method is illustrated based on two applications. First, we study the problem of predicting daily returns using realized volatility. Second, we consider a synthetic control setting where the goal is to predict a country's counterfactual GDP growth rate based on the contemporaneous GDP growth rates of other countries .
Revised:  We also establish theoretical performance guarantees under model misspecification, overfitting, and with time series data .
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Original:  When congestion is included as well, the resulting networks are denser and resemble the bipartite expander graphs that have been proposed in the supply chain literature  .
Revised:  When congestion is included as well, the resulting networks are denser and resemble the bipartite expander graphs that have been proposed in the supply chain literature , thereby, providing the first example of endogenous formation of resilient supply chain networks, without resilience being explicitly encoded in payoffs .
Edit intent: meaning-changed
Original: We study the effect of the introduction of university tuition fees on the enrollment behavior of students in Germany. For this, an appropriate Lasso-technique is crucial in order to identify the magnitude and significance of the effect due to potentially many relevant controlling factors and only a short time frame where fees existed.
Revised: We study the effect of the introduction of university tuition fees on the enrollment behavior of students during the years 2006-2014. In particular, we show how the variation in the introduction scheme across states and times can be exploited to identify the magnitude and significance of the effect due to potentially many relevant controlling factors and only a short time frame where fees existed.
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Original:  For this, an appropriate Lasso-technique is crucial in order to identify the magnitude and significance of the effect due to potentially many relevant controlling factors and only a short time frame where fees existed. We show that a post-double selection strategy combined with stability selection determines a significant negative impact of  fees on student enrollment and identifies relevant variables .
Revised:  For this, an appropriate Lasso-technique is crucial in order to identify the federal average causal effect of tuition fees by controlling for a large amount of potentially influencing attributes for state heterogeneity. We suggest a stability post-double selection strategy combined with stability selection determines a significant negative impact of fees on student enrollment and identifies relevant variables .
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Original:  We show that a post-double selection strategy combined with stability selection determines a significant negative impact of  fees on student enrollment and identifies relevant variables .
Revised:  We show that a post-double selection methodology to robustly determine the causal effect across types in the transparently modeled unknown response components. The proposed stability resampling scheme in the two LASSO selection steps efficiently mitigates the risk of model underspecification and thus biased effects when the tuition fee policy decision also depends on relevant variables for the state enrollment rates. Correct inference for the full cross-section state population in the sample requires adequate design -- rather than sampling-based standard errors. With the data-driven model selection and explicit control for spatial cross-effects we detect that tuition fees induce substantial migration effects where the mobility occurs both from fee but also from non-fee states suggesting also a general movement for quality. Overall, we find a significant negative impact of fees on student enrollment and identifies relevant variables .
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Original:  This is in contrast to  previous empirical studies and a plain linear panel regression which cannot detect any effect of tuition fees in this case. In our study, we explicitly deal with data challenges in the response variable in a transparent way and provide respective robust results. Moreover, we control for spatial cross-effects capturing the heterogeneity in the introduction scheme of fees across federal states ("Bundesl\"ander"), which can set their own educational policy. We also confirm the validity of our Lasso approach in a comprehensive simulation study .
Revised:  This is in contrast to previous empirical studies with full fixed effects linear panel regressions which generally underestimate the size and get an only insignificant effect .
Edit intent: coherence
Original: Recently spiking neural networks (SNNs) have received much attention because of its rich biological significance and its power in processing spatial and temporal information. However, most existing SNNs are static due to the fixed eigenvalues of spiking generation functions, which means that neurons fire spikes in a fixed frequency when receiving constant input signals. Thereby, the static SNNs are limited in scalability .
Revised: Recently spiking neural networks (SNNs) has attracted much attention due to its great potential of modeling time-dependent signals. The firing rate of spiking neurons is decided by control rate which is fixed manually in advance, and thus, whether the firing rate is adequate for modeling actual time series relies on fortune. Though it is demanded to have an adaptive control rate, it is a non-trivial task because the control rate and the connection weights learned during the training process are usually entangled .
Edit intent: coherence
Original:  In this paper, we clarify the bifurcation relationship that dynamic eigenvalues have a great influence on the neuron excitation frequency. And then we propose the  Bifurcation Spiking Neural Network (BSNN) for developing a dynamic SNN. Different from traditional static SNNs, BSNN takes a bifurcation system with time-varying eigenvalues as the basic building block, thus it has more powerful flexibility and is able to handle data with complex nonlinear structures.
Revised:  In this paper, we show that the Bifurcation Spiking Neural Network (BSNN) for developing a dynamic SNN. Different from traditional static SNNs, BSNN takes a bifurcation system with time-varying eigenvalues as the basic building block, thus it has more powerful flexibility and is able to handle data with complex nonlinear structures.
Edit intent: coherence
Original:  And then we propose the  Bifurcation Spiking Neural Network (BSNN) for developing a dynamic SNN. Different from traditional static SNNs, BSNN takes a bifurcation system with time-varying eigenvalues as the basic building block, thus it has more powerful flexibility and is able to handle data with complex nonlinear structures.
Revised:  And then we propose the firing rate is related to the eigenvalue of the spike generation function. Inspired by this insight, by enabling the spike generation function to have adaptable eigenvalues rather than parametric control rates, we develop the Bifurcation Spiking Neural Network (BSNN) for developing a dynamic SNN. Different from traditional static SNNs, BSNN takes a bifurcation system with time-varying eigenvalues as the basic building block, thus it has more powerful flexibility and is able to handle data with complex nonlinear structures.
Edit intent: meaning-changed
Original:  And then we propose the  Bifurcation Spiking Neural Network (BSNN) for developing a dynamic SNN. Different from traditional static SNNs, BSNN takes a bifurcation system with time-varying eigenvalues as the basic building block, thus it has more powerful flexibility and is able to handle data with complex nonlinear structures. Experiments on wide-range taskshave been conducted, including a delayed-memory XOR task, four image recognition datasets, and 25 UCR archive. The results show that the performance of BSNN is superior to the existing static SNN models .
Revised:  And then we propose the Bifurcation Spiking Neural Network (BSNN) , which has an adaptive firing rate and is insensitive to the setting of control rates. Experiments validate the effectiveness of BSNN on a broad range of tasks, showing that BSNN achieves superior performance to existing SNNs and is robust to the setting of control rates .
Edit intent: clarity
Original:  To address this gap, we study features of pairwise spiking in a large-scale %DIFDELCMD < {\it %%% in vivo dataset where presynaptic neurons were explicitly decoupled from network activity by juxtacellular stimulation.
Revised:  To address this gap, we study features of pairwise spiking in a large-scale %DIFDELCMD < {\it %%% in vivo dataset where presynaptic neurons were explicitly decoupled from network activity by juxtacellular stimulation.
Edit intent: fluency
Original:  We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Revised:  We then construct biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Edit intent: clarity
Original:  We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Revised:  We then develop biophysical models of paired spike trains to reproduce the observed phenomenology of in vivo monosynaptic interactions , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Edit intent: clarity
Original:  We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Revised:  We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and %DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Edit intent: clarity
Original:  We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Revised:  We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Edit intent: coherence
Original:  We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Revised:  We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo %DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Edit intent: fluency
Original:  We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
Revised:  We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% firing irregularity. A key characteristic of these models is that the paired neurons are coupled by rapidly-fluctuating background inputs. We quantify a monosynapse's causal effect by comparing the postsynaptic train with its counterfactual, when the monosynapse is removed. Subsequently, we develop statistical techniques for estimating this causal effect from the pre- and post-synaptic spike trains.
Edit intent: meaning-changed
Original:  Despite the prevalence and detrimental impact of lodging on crop yields, there is little consensus on how to phenotype plants in the field for lodging-resistance and thus breed for mechanically resilient plants.
Revised:  Despite the prevalence and detrimental impact of lodging on crop yields, there is little consensus on how to phenotype plants in the field for lodging resistance and thus breed for mechanically resilient plants.
Edit intent: fluency
Original:  Because the core problems appear cognitive rather than statistical, we review some simple proposals to aid researchers in interpreting statistical outputs. These proposals emphasize logical and information concepts over probability, and thus may be more robust to common misinterpretations than are traditional descriptions.
Revised:  Because the core problems appear cognitive rather than statistical, we review simple aids to statistical interpretations. These aids emphasize logical and information concepts over probability, and thus may be more robust to common misinterpretations than are traditional descriptions.
Edit intent: clarity
Original:  We use the Shannon transform of the P-value p, also known as the  surprisal or S-value s= -log (p), to provide a measure of the information supplied by the testing procedure against these assumptions , and to help calibrate intuitions against simple physical experiments like coin tossing.
Revised:  We use the Shannon transform of the P-value p, also known as the binary surprisal or S-value s= -log (p), to provide a measure of the information supplied by the testing procedure against these assumptions , and to help calibrate intuitions against simple physical experiments like coin tossing.
Edit intent: meaning-changed
Original:  We use the Shannon transform of the P-value p, also known as the  surprisal or S-value s= -log (p), to provide a measure of the information supplied by the testing procedure against these assumptions , and to help calibrate intuitions against simple physical experiments like coin tossing.
Revised:  We use the Shannon transform of the P-value p, also known as the surprisal or S-value s= -\log_{2 (p), to provide a measure of the information supplied by the testing procedure against these assumptions , and to help calibrate intuitions against simple physical experiments like coin tossing.
Edit intent: meaning-changed
Original:  We use the Shannon transform of the P-value p, also known as the  surprisal or S-value s= -log (p), to provide a measure of the information supplied by the testing procedure against these assumptions , and to help calibrate intuitions against simple physical experiments like coin tossing.
Revised:  We use the Shannon transform of the P-value p, also known as the surprisal or S-value s= -log (p), to measure the information supplied by the testing procedure against these assumptions , and to help calibrate intuitions against simple physical experiments like coin tossing.
Edit intent: clarity
Original:  We use the Shannon transform of the P-value p, also known as the  surprisal or S-value s= -log (p), to provide a measure of the information supplied by the testing procedure against these assumptions , and to help calibrate intuitions against simple physical experiments like coin tossing.
Revised:  We use the Shannon transform of the P-value p, also known as the surprisal or S-value s= -log (p), to provide a measure of the information supplied by the testing procedure , and to help calibrate intuitions against simple physical experiments like coin tossing.
Edit intent: clarity
Original:  We also advise tabulating or graphing test statistics for alternative hypotheses, and interval estimates for different percentile levels, to thwart fallacies arising from arbitrary dichotomies  .
Revised:  We also advise tabulating or graphing test statistics for alternative hypotheses, and interval estimates for different percentile levels, to thwart fallacies arising from arbitrary dichotomies . Finally, we reinterpret P-values and interval estimates in unconditional terms, which describe compatibility of data with the entire set of analysis assumptions. We illustrate these methods with a reanalysis of data from an existing record-based cohort study. In line with other recent recommendations, we advise that teaching materials and research reports discuss P-values as measures of compatibility rather than significance, compute P-values for alternative hypotheses whenever they are computed for null hypotheses, and interpret interval estimates as showing values of high compatibility with data, rather than regions of confidence. Our recommendations emphasize cognitive devices for displaying the compatibility of the observed data with various hypotheses of interest, rather than focusing on single hypothesis tests or interval estimates .
Edit intent: clarity
Original:  However, there is no theory explaining the effectiveness of layered control architectures that connects speed-accuracy trade-offs (SATs) in neurophysiology to the resulting SATs in sensorimotor control.
Revised:  Here we introduce a driving task to study how a mountain biker mitigates the immediate disturbance of trail bumps and responds to changes in trail direction. We manipulated the time delays and accuracy of the control input from the wheel as a surrogate for manipulating the characteristics of neurons in the control loop. The observed speed-accuracy trade-offs (SATs) in neurophysiology to the resulting SATs in sensorimotor control.
Edit intent: meaning-changed