wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Here we present fast algorithms to calculate that variance in arbitrary graphs and forests. As for the latter, the algorithm calculates variance in linear time with respect to the number of vertices. This paves the way for many applications that rely on an exact but fast calculation of that variance. These algorithms are based on novel arithmetic expressions for the calculation of the variance that we develop from previous theoretical work  .
Revised:  Here we present fast algorithms to calculate that variance in arbitrary graphs in o(nm^2)-time, and in forests in O(n)-time. These algorithms work on a wide range of random layouts (not only on Moon's) and are based on novel arithmetic expressions for the calculation of the variance that we develop from previous theoretical work .
Edit intent: meaning-changed
Original: Increasing the autonomy level of a robot hand to accomplish remote object manipulation tasks faster and easier is a new and promising topic in teleoperation. Such semi-autonomous telemanipulation, however, is very challenging due to the physical discrepancy between the human hand and the robot hand, along with the fine motion constraints required for the manipulation task. To overcome these challenges, the robot needs to learn how to assist the human operator in a preferred/intuitive way, which must provide effective assistance that the operator needs yet still accommodate human inputs, so the operator feels in control of the system (i.e., not counter-intuitive to the operator). Toward this goal, we develop novel data-driven approaches to stably learn what assistance is preferred from high data variance caused by the ambiguous nature of human operators.
Revised: Enabling robots to provide effective assistance yet still accommodating the operator's commands for telemanipulation of an object is very challenging because robot's assistive action is not always intuitive for human operators and human behaviors and preferences are sometimes ambiguous for the robot to interpret. Although various assistance approaches are being developed to improve the control quality from different optimization perspectives, the problem still remains in determining the appropriate approach that satisfies the fine motion constraints for the telemanipulation task and preference of the operator. To address these problems, we developed a novel preference-aware assistance knowledge learning approach. An assistance preference model learns what assistance is preferred from high data variance caused by the ambiguous nature of human operators.
Edit intent: coherence
Original:  Toward this goal, we develop novel data-driven approaches to stably learn what assistance is preferred from high data variance caused by the ambiguous nature of human operators. To avoid an extensive robot-specific training process, methods to transfer this assistance knowledge between different robot hands are discussed.
Revised:  Toward this goal, we develop novel data-driven approaches to stably learn what assistance is preferred by a human, and a stagewise model updating method ensures the learning stability while dealing with the ambiguity of human preference data. Such a preference-aware assistance knowledge enables a teleoperated robot hand to provide more active yet preferred assistance toward manipulation success. We also developed knowledge transfer methods to transfer this assistance knowledge between different robot hands are discussed.
Edit intent: meaning-changed
Original:  To avoid an extensive robot-specific training process, methods to transfer this assistance knowledge between different robot hands are discussed. Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Revised:  To avoid an extensive robot-specific training process, methods to transfer the preference knowledge across different robot hand structures to avoid extensive robot-specific training. Experiments to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Edit intent: clarity
Original:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Revised:  Experiments were conducted to telemanipulate a 3-finger hand and 2-finger hand, respectively, to use, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Edit intent: meaning-changed
Original:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Revised:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and hand over a cup have been conducted . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Edit intent: clarity
Original:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Revised:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the methods enabled the robots to effectively learn the preference knowledge and allowed knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Edit intent: clarity
Original:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Revised:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Edit intent: coherence
Original:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Revised:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots with less training efforts .
Edit intent: clarity
Original:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training efforts .
Revised:  Experiments were conducted to telemanipulate a cup for three principal tasks: usage, move, and handover by remotely controlling a 3-finger gripper and 2-finger gripper . Results demonstrated that the proposed model effectively learned the knowledge of preferred assistance, and knowledge transfer between robots allows this semi-autonomous telemanipulation strategy to be scaled up with less training effort .
Edit intent: fluency
Original:  Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps , instead of the full optimization problem. Using our method, we can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution remains feasible ).
Revised:  Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps . We can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution remains feasible ).
Edit intent: coherence
Original:  We show using an eight species community that this is an efficient and robust method for computing dynamic flux balance analysis simulations, and so is capable of simulating communities URLanisms. We demonstrate that the method gives an approximately 85 \\%DIF < speed-up URLanism over the standard and widely used method.
Revised:  We demonstrate the efficiency and robustness of our method by comparing with currently used methods on a four species community, and show that our method requires at least 91 \\%DIF < speed-up URLanism over the standard and widely used method.
Edit intent: meaning-changed
Original:  Second,  to understand how information about the coronavirus drives economic anxieties, we conduct a survey experiment in a representative sample of the US population .
Revised:  Second, leveraging two US representative survey experiments conducted in early and mid-March 2020, we document a rapid surge in economic anxieties after the arrival of the coronavirus in the US. Third, to understand how information about the coronavirus drives economic anxieties, we conduct a survey experiment in a representative sample of the US population .
Edit intent: meaning-changed
Original:  First, we collect a global dataset on internet searches indicative of economic anxieties. We find that the arrival of coronavirus in a country led to a substantial increase in such internet searches of up to 58 percent. Second, leveraging two US representative survey experiments conducted in early and mid-March 2020 , we document a rapid surge in economic anxieties after the arrival of the coronavirus in the US.
Revised:  First, we collect a global dataset on internet searches and two representative surveys from the US , we document a rapid surge in economic anxieties after the arrival of the coronavirus in the US.
Edit intent: clarity
Original:  Second, leveraging two US representative survey experiments conducted in early and mid-March 2020 , we document a rapid surge in economic anxieties after the arrival of the coronavirus in the US. Third, to understand how information about the coronavirus affects these anxieties, we measure perceptions about the coronavirus. We find substantial heterogeneity in participants' beliefs about the mortality from and contagiousness of the virus.
Revised:  Second, leveraging two US representative survey experiments conducted in early and mid-March 2020 , we document a rapid surge in economic anxieties after the arrival of the coronavirus . We also document a large dispersion in beliefs about the mortality from and contagiousness of the virus.
Edit intent: clarity
Original:  We find substantial heterogeneity in participants' beliefs about the mortality from and contagiousness of the virus. Fourth, experimentally providing participants with information about mortality and contagiousness causally affects participants' worries regarding the aggregate economy and their personal economic situation .
Revised:  We find substantial heterogeneity in participants' beliefs about the pandemic risk factors of the coronavirus, and demonstrate that these beliefs causally affect individuals' economic anxieties .
Edit intent: clarity
Original:  Finally, we document that participants' subjective mental models understate the non-linear nature of disease spread, and that these mental models shape the extent of economic worries. These results underscore the importance of public education about the virus for successful containment as well as the need for timely measures that decrease economic hardship and anxietyduring a major global pandemic .
Revised:  Finally, we document that participants' subjective mental models understate the non-linear nature of disease spread, and that these mental models shape the extent of economic anxiety .
Edit intent: clarity
Original:  Unfortunately, even testing the significance of simple dependence measures, such as Pearson correlation, is complicated by altered sampling properties when autocorrelation is present in the individual time series .
Revised:  Unfortunately, the hypothesis tests that are used to determine statistically significant directed or multivariate relationships from time-series data often yield spurious associations (Type I errors) or omit causal relationships (Type II errors). This is due to the autocorrelation present in the individual time series .
Edit intent: clarity
Original:  Unfortunately, even testing the significance of simple dependence measures, such as Pearson correlation, is complicated by altered sampling properties when autocorrelation is present in the individual time series . Moreover, it has been recently established that commonly used multivariate dependence measures---such as Granger causality---can produce substantially inaccurate results when applying classical hypothesis-testing procedures to digitally-filtered time series. Here , we suggest thatthe digital filtering-induced bias in Granger causality is an effect of autocorrelation, and we present a principled statistical framework for the hypothesis testing of a large family of linear-dependence measures between multiple autocorrelated time series.
Revised:  Unfortunately, even testing the significance of simple dependence measures, such as Pearson correlation, is complicated by altered sampling properties when autocorrelation is present in the analysed time series -- a property that is ubiquitous across diverse applications, from brain dynamics to climate change. Here we show that, for limited data, this issue cannot be mediated by fitting a time-series model alone (e.g., in Granger causality is an effect of autocorrelation, and we present a principled statistical framework for the hypothesis testing of a large family of linear-dependence measures between multiple autocorrelated time series.
Edit intent: meaning-changed
Original:  Here , we suggest thatthe digital filtering-induced bias in Granger causality is an effect of autocorrelation, and we present a principled statistical framework for the hypothesis testing of a large family of linear-dependence measures between multiple autocorrelated time series. Our approach unifies the theoretical foundations established by Bartlett and others on variance estimators for autocorrelated signals with the more intricate multivariate measures of linear dependence. Specifically, we derive the sampling distributions and subsequent hypothesis tests for any measure that can be decomposed into terms that involve independent partial correlations, which we show includes Granger causality and mutual information under a multivariate linear-Gaussian model.
Revised:  Here , we suggest thatthe digital filtering-induced bias in Granger causality or prewhitening approaches), and instead that the degrees of freedom in statistical tests should be altered to account for the effective sample size induced by cross-correlations in the observations. This insight enabled us to derive modified hypothesis tests for any measure that can be decomposed into terms that involve independent partial correlations, which we show includes Granger causality and mutual information under a multivariate linear-Gaussian model.
Edit intent: clarity
Original:  Specifically, we derive the sampling distributions and subsequent hypothesis tests for any measure that can be decomposed into terms that involve independent partial correlations, which we show includes Granger causality and mutual information under a multivariate linear-Gaussian model.
Revised:  Specifically, we derive the sampling distributions and subsequent hypothesis tests for any multivariate correlation-based measures of linear dependence between covariance-stationary time series, including Granger causality and mutual information under a multivariate linear-Gaussian model.
Edit intent: clarity
Original:  Specifically, we derive the sampling distributions and subsequent hypothesis tests for any measure that can be decomposed into terms that involve independent partial correlations, which we show includes Granger causality and mutual information under a multivariate linear-Gaussian model. In doing so, we provide the first exact tests for inferring linear dependence between vector autoregressive processes with limited data. Using numerical simulations and brain-imaging datasets, we demonstrate that our newly developed tests maintain the expected false-positive rate (FPR) with minimally-sufficient samples, while the classical log-likelihood ratio tests can yield an unbounded FPR depending on the parameters chosen.
Revised:  Specifically, we derive the sampling distributions and subsequent hypothesis tests for any measure that can be decomposed into terms that involve independent partial correlations, which we show includes Granger causality and mutual information with Gaussian marginals. We use both numerical simulations (generated by autoregressive models and digital filtering) as well as recorded fMRI-neuroimaging data to show that our tests are unbiased for a variety of stationary time series. Our experiments demonstrate that the commonly used F- and \chi^2-tests can induce significant false-positive rate (FPR) with minimally-sufficient samples, while the classical log-likelihood ratio tests can yield an unbounded FPR depending on the parameters chosen.
Edit intent: meaning-changed
Original:  Using numerical simulations and brain-imaging datasets, we demonstrate that our newly developed tests maintain the expected false-positive rate (FPR) with minimally-sufficient samples, while the classical log-likelihood ratio tests can yield an unbounded FPR depending on the parameters chosen.
Revised:  Using numerical simulations and brain-imaging datasets, we demonstrate that our newly developed tests maintain the expected false-positive rates of up to 100
Edit intent: meaning-changed
Original:  We show both theoretically and empirically that our method preserves important global structural information on graphs, in particular spectral information, making it well-suited for tasks on graphs including retrieval, classification, summarization, and visualization .
Revised:  We show theoretically that our method preserves important global structural information on graphs, in particular spectral information, making it well-suited for tasks on graphs including retrieval, classification, summarization, and visualization .
Edit intent: clarity
Original:  We show both theoretically and empirically that our method preserves important global structural information on graphs, in particular spectral information, making it well-suited for tasks on graphs including retrieval, classification, summarization, and visualization .
Revised:  We show both theoretically and empirically that our method preserves important global structural information on graphs, in particular spectral information, and analyze connections to existing studies. Empirically, COPT outperforms state of the art methods in graph classification on both synthetic and real datasets .
Edit intent: meaning-changed
Original:  Furthermore, our proposed method is also more flexible, being applicable to any spiking neuron model, is conservative in how many parameters are required for implementation and can be deployed in an online-fashion with minimal computational overhead.
Revised:  Furthermore, our proposed method is more flexible, being applicable to any spiking neuron model, is conservative in how many parameters are required for implementation and can be deployed in an online-fashion with minimal computational overhead.
Edit intent: clarity
Original:  We develop a Bayesian method for automated probabilistic record linkage and show it recovers 40 \% more true matches, holding accuracy constant, than comparable methods in a matching of Union Army recruitment data to the 1900 US Census for which expert-labelled true matches are known .
Revised:  We develop a Bayesian method for automated probabilistic record linkage and show it recovers more than 50 \% more true matches, holding accuracy constant, than comparable methods in a matching of Union Army recruitment data to the 1900 US Census for which expert-labelled true matches are known .
Edit intent: meaning-changed
Original:  We develop a Bayesian method for automated probabilistic record linkage and show it recovers 40 \% more true matches, holding accuracy constant, than comparable methods in a matching of Union Army recruitment data to the 1900 US Census for which expert-labelled true matches are known .
Revised:  We develop a Bayesian method for automated probabilistic record linkage and show it recovers 40 \% more true matches, holding accuracy constant, than comparable methods in a matching of military recruitment data to the 1900 US Census for which expert-labelled true matches are known .
Edit intent: clarity
Original:  We develop a Bayesian method for automated probabilistic record linkage and show it recovers 40 \% more true matches, holding accuracy constant, than comparable methods in a matching of Union Army recruitment data to the 1900 US Census for which expert-labelled true matches are known .
Revised:  We develop a Bayesian method for automated probabilistic record linkage and show it recovers 40 \% more true matches, holding accuracy constant, than comparable methods in a matching of Union Army recruitment data to the 1900 US Census for which expert-labelled matches are available .
Edit intent: clarity