wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The metrics of root mean square error (RMSE or accuracy) , precision, recall, and Jaccard index (JAC) are currently used in the literature but in certain conditions fail in distinguishing qualities of different nanoscopy images . In this paper, the root mean square minimum distance (RMSMD) is proposed as a quality metric for localization nanoscopy images .
Revised:  The metrics of root mean square error (RMSE or accuracy) , precision, recall, and Jaccard index (JAC) are currently used in the literature but in certain conditions fail in distinguishing qualities of different nanoscopy images in certain conditions. An experiment that an unbiased estimator achieving the Fisher information and Cramer-Rao lower bound (CRLB) localizes emitters frame by frame independently is simulated and the result demonstrates the superior sensitivity of RMSMD over the other four metrics. The RMSMD of an information-achieving nanoscopy image benchmarks localization nanoscopy images .
Edit intent: meaning-changed
Original:  In this paper, the root mean square minimum distance (RMSMD) is proposed as a quality metric for localization nanoscopy images . Its properties are analyzed and demonstrated by examples which also show the advantages over RMSE, precision, recall and JAC . As a universal and objective metric, the RMSMD can be broadly applied to applications that need to measure average, local, and mutual fitness of two sets of points.
Revised:  In this paper, the root mean square minimum distance (RMSMD) is proposed as a quality metric for localization nanoscopy images and localization algorithms . As a universal and objective metric, the RMSMD can be broadly applied to applications that need to measure average, local, and mutual fitness of two sets of points.
Edit intent: meaning-changed
Original:  Its properties are analyzed and demonstrated by examples which also show the advantages over RMSE, precision, recall and JAC . As a universal and objective metric, the RMSMD can be broadly applied to applications that need to measure average, local, and mutual fitness of two sets of points.
Revised:  Its properties are analyzed and demonstrated by examples which also show the advantages over RMSE, precision, recall and JAC . As a universal and objective metric, RMSMD can be broadly applied to applications that need to measure average, local, and mutual fitness of two sets of points.
Edit intent: coherence
Original:  Its properties are analyzed and demonstrated by examples which also show the advantages over RMSE, precision, recall and JAC . As a universal and objective metric, the RMSMD can be broadly applied to applications that need to measure average, local, and mutual fitness of two sets of points.
Revised:  Its properties are analyzed and demonstrated by examples which also show the advantages over RMSE, precision, recall and JAC . As a universal and objective metric, the RMSMD can be broadly employed in various applications to measure the mutual fitness of two sets of points.
Edit intent: clarity
Original:  An experiment that an unbiased estimator achieving the Fisher information and Cramer-Rao lower bound (CRLB) localizes emitters frame by frame independently is simulated and the result demonstrates the superior sensitivity of RMSMD over the other four metrics .
Revised:  The unbiased Gaussian estimator that achieves the Fisher information and Cramer-Rao lower bound (CRLB) localizes emitters frame by frame independently is simulated and the result demonstrates the superior sensitivity of RMSMD over the other four metrics .
Edit intent: meaning-changed
Original:  An experiment that an unbiased estimator achieving the Fisher information and Cramer-Rao lower bound (CRLB) localizes emitters frame by frame independently is simulated and the result demonstrates the superior sensitivity of RMSMD over the other four metrics .
Revised:  An experiment that an unbiased estimator achieving the Fisher information and Cramer-Rao lower bound (CRLB) of a single data frame is proposed to benchmark the quality of localization nanoscopy images and the performance of localization algorithms. The information-achieving estimator is simulated in an example and the result demonstrates the superior sensitivity of RMSMD over the other four metrics .
Edit intent: meaning-changed
Original:  An experiment that an unbiased estimator achieving the Fisher information and Cramer-Rao lower bound (CRLB) localizes emitters frame by frame independently is simulated and the result demonstrates the superior sensitivity of RMSMD over the other four metrics . The RMSMD of an information-achieving nanoscopy image benchmarks localization nanoscopy images and localization algorithms .
Revised:  An experiment that an unbiased estimator achieving the Fisher information and Cramer-Rao lower bound (CRLB) localizes emitters frame by frame independently is simulated and the result demonstrates the superior sensitivity of RMSMD over the other four metrics .
Edit intent: clarity
Original: Bayesian agents learn about a moving target, such as a commodity price, using private signals and their network neighbors' estimates. The weights agents place on these sources of information are endogenously determined by the precisions and correlations of the sources; the weights, in turn, determine future correlations. We study stationary equilibria-ones in which all of these quantities are constant over time. Equilibria in linear updating rules always exist, yielding a Bayesian learning model as tractable as the commonly-used DeGroot heuristic. Equilibria and the comparative statics of learning outcomes can be readily computed even in large networks. Substantively, we identify pervasive inefficiencies in Bayesian learning.In any stationary equilibrium where agentsput positive weights on neighbors' actions, learning is Pareto inefficient in a generic network: agents rationally overuse social information and underuse their private signals .
Revised: Bayesian agents learn about a moving target, such as a commodity price, using private signals and the past actions of their neighbors. In contrast to most models of social learning in a network, the target being learned about is moving around. We ask: when can a group aggregate information quickly, keeping up with the changing environment? First, if private signal distributions are diverse enough across agents, then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others. Second, without such diversity, Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient. Third, good aggregation requires anti-imitation; without it, agents' estimates are inefficiently confounded by "echoes." Our stationary equilibrium learning rules incorporate past information by taking linear combinations of other agents' past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations. The resulting tractability can facilitate structural estimation of equilibrium learning models and testing against behavioral alternatives, as well as the analysis of welfare and influence .
Edit intent: meaning-changed
Original:  First, if private signal distributions are diverse enough across agents , then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others . Second, without such diversity,  Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient .
Revised:  First, if each agent has access to neighbors with sufficiently diverse kinds of signals , then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others . Second, without such diversity, Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient .
Edit intent: clarity
Original:  First, if private signal distributions are diverse enough across agents , then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others . Second, without such diversity,  Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient .
Revised:  First, if private signal distributions are diverse enough across agents , then Bayesian learning achieves good information aggregation . Second, without such diversity, Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient .
Edit intent: clarity
Original:  First, if private signal distributions are diverse enough across agents , then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others . Second, without such diversity,  Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient .
Revised:  First, if private signal distributions are diverse enough across agents , then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others . Second, without such diversity, there are cases in which Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient .
Edit intent: clarity
Original:  First, if private signal distributions are diverse enough across agents , then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others . Second, without such diversity,  Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient .
Revised:  First, if private signal distributions are diverse enough across agents , then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others . Second, without such diversity, Bayesian information aggregation necessarily falls far short of good aggregation benchmarks, and can be Pareto-inefficient .
Edit intent: clarity
Original:  First, if private signal distributions are diverse enough across agents , then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others . Second, without such diversity,  Bayesian information aggregation can fall far short of good aggregation benchmarks, and can be Pareto-inefficient .
Revised:  First, if private signal distributions are diverse enough across agents , then Bayesian learning achieves good information aggregation as long as individuals observe sufficiently many others . Second, without such diversity, Bayesian information aggregation can fall far short of efficient benchmarks .
Edit intent: coherence
Original:  Our stationary equilibrium learning rules incorporate past information by taking linear combinations of other agents' past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations.
Revised:  Agents' stationary equilibrium learning rules incorporate past information by taking linear combinations of other agents' past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations.
Edit intent: fluency
Original:  Third, good aggregation can be achieved only if agents "anti-imitate" some neighbors: otherwise, equilibrium estimates are inefficiently confounded by "echoes. " Agents' stationary equilibrium learning rules incorporate past information by taking linear combinations of other agents' past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations .
Revised:  Third, good aggregation requires agents who understand correlations in neighbors' actions with the sophistication needed to concentrate on recent developments and filter out older, outdated information. In stationary equilibrium, agents' learning rules incorporate past information by taking linear combinations of other agents' past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations .
Edit intent: meaning-changed
Original:  " Agents' stationary equilibrium learning rules incorporate past information by taking linear combinations of other agents' past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations . We discuss how the resulting tractability is useful for structural estimation of equilibrium learning models and testing against behavioral alternatives .
Revised:  " Agents' stationary equilibrium learning rules incorporate past information by taking linear combinations of other agents' past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations .
Edit intent: clarity
Original:  In contrast to most models of social learning in a network , the target being learned about is moving around. We ask: when can a group aggregate information quickly, keeping up with the changing state? First, if each agent has access to neighbors with sufficiently diverse kinds of signals, then Bayesian learning achieves good informationaggregation. Second, without such diversity, there are cases in which Bayesian information aggregation necessarily falls far short of efficient benchmarks. Third, good aggregation requires agents who understand correlations in neighbors' actions with the sophistication needed to concentrate on recent developments and filter out older, outdated information.
Revised:  In contrast to most models of social learning in a network . We characterize equilibrium learning and social influence in this setting. We then examine when agents can aggregate information well, responding quickly to recent changes. A key sufficient condition for good aggregation is that each individual's neighbors have sufficiently different types of private information. In contrast, when signals are homogeneous, aggregation is suboptimal on any network. We also examine behavioral versions of the model, and show that achieving good aggregation requires agents who understand correlations in neighbors' actions with the sophistication needed to concentrate on recent developments and filter out older, outdated information.
Edit intent: meaning-changed
Original:  Third, good aggregation requires agents who understand correlations in neighbors' actions with the sophistication needed to concentrate on recent developments and filter out older, outdated information. In stationary equilibrium, agents' learning rules incorporate past information by taking linear combinations of other agents' past estimates (as in the simple DeGroot heuristic), and we characterize the coefficients in these linear combinations .
Revised:  Third, good aggregation requires agents who understand correlations in neighbors' actions . The model provides a Bayesian foundation for a tractable learning dynamic in networks, closely related to the DeGroot model, and offers new tools for counterfactual and welfare analyses .
Edit intent: meaning-changed
Original: In this paper we will consider a generalized extension of the Eisenberg-Noe model of financial contagion to allow for time dynamics in both discrete and continuous time. Derivation and interpretation of the financial implications will be provided . Emphasis will be placed on the  continuous-time framework and its formulation as a differential equation driven by the operating cash flows . Mathematical results on existence and uniqueness of firm wealths under the discrete and continuous-time models will be provided .
Revised: In this paper we will consider a generalized extension of the Eisenberg-Noe model of financial contagion to allow for time dynamics of the interbank liabilities . Emphasis will be placed on the continuous-time framework and its formulation as a differential equation driven by the operating cash flows . Mathematical results on existence and uniqueness of firm wealths under the discrete and continuous-time models will be provided .
Edit intent: clarity
Original:  Derivation and interpretation of the financial implications will be provided . Emphasis will be placed on the  continuous-time framework and its formulation as a differential equation driven by the operating cash flows . Mathematical results on existence and uniqueness of firm wealths under the discrete and continuous-time models will be provided .
Revised:  Derivation and interpretation of the financial implications will be provided . Emphasis will be placed on the construction, existence, and uniqueness of the continuous-time framework and its formulation as a differential equation driven by the operating cash flows . Mathematical results on existence and uniqueness of firm wealths under the discrete and continuous-time models will be provided .
Edit intent: meaning-changed
Original:  Derivation and interpretation of the financial implications will be provided . Emphasis will be placed on the  continuous-time framework and its formulation as a differential equation driven by the operating cash flows . Mathematical results on existence and uniqueness of firm wealths under the discrete and continuous-time models will be provided .
Revised:  Derivation and interpretation of the financial implications will be provided . Emphasis will be placed on the continuous-time framework and its formulation as a differential equation driven by the operating cash flows .
Edit intent: coherence
Original: In this paper we will consider a generalized extension of the Eisenberg-Noe model of financial contagion to allow for time dynamics of the interbank liabilities . Emphasis will be placed on the construction, existence, and uniqueness of the continuous-time framework and its formulation as a differential equation driven by the operating cash flows.
Revised: In this paper we introduce a generalized extension of the Eisenberg-Noe model of financial contagion to allow for time dynamics of the interbank liabilities . Emphasis will be placed on the construction, existence, and uniqueness of the continuous-time framework and its formulation as a differential equation driven by the operating cash flows.
Edit intent: clarity
Original: In this paper we will consider a generalized extension of the Eisenberg-Noe model of financial contagion to allow for time dynamics of the interbank liabilities . Emphasis will be placed on the construction, existence, and uniqueness of the continuous-time framework and its formulation as a differential equation driven by the operating cash flows. Finally, the financial implications of time dynamics will be considered.
Revised: In this paper we will consider a generalized extension of the Eisenberg-Noe model of financial contagion to allow for time dynamics of the interbank liabilities , including a dynamic examination of default risk. Such a system allows us to distinguish between defaults resulting from either insolvency or illiquidity, and to analyze the resulting effects on the financial implications of time dynamics will be considered.
Edit intent: meaning-changed
Original:  Here we show that if \omega is known, the \omega-cloud alone uniquely determines P, and we give a linear-time reconstruction algorithm. Furthermore, even if we only know that \omega < \pi/ 2, we can still reconstruct P , albeit in cubic time in the number of vertices.
Revised:  Here we show that if \omega is known, the \omega-cloud alone is given. We give two conditions under which it uniquely defines P: (i) when \omega < \pi is fixed/given, or (ii) when what is known is that \omega < \pi/ 2, we can still reconstruct P , albeit in cubic time in the number of vertices.
Edit intent: meaning-changed
Original:  Furthermore, even if we only know that \omega < \pi/ 2, we can still reconstruct P , albeit in cubic time in the number of vertices.
Revised:  Furthermore, even if we only know that \omega < \pi/ 2. We show that if neither of these two conditions hold, then P may not be unique. We show that, when the uniqueness conditions hold, the polygon P can be reconstructed in O(n) time with O(1) working space in addition to the input, where n is the number of arcs in the number of vertices.
Edit intent: meaning-changed
Original:  Furthermore, even if we only know that \omega < \pi/ 2, we can still reconstruct P , albeit in cubic time in the number of vertices. This reduces to quadratic time if in addition we are given the location of one of the vertices of P .
Revised:  Furthermore, even if we only know that \omega < \pi/ 2, we can still reconstruct P , albeit in cubic time in the input \omega-cloud .
Edit intent: meaning-changed
Original:  These wires define homotopy classes with respect to the vertices of G (for the combinatorial embedding of G shared by \Gamma_I and \Gamma_O). These homotopy classes can be represented by orthogonal polylines in \Gamma_I . We argue that the structural difference between the two drawings can be captured by the spirality of the wires in \Gamma_I, which guides our morph from \Gamma_I to \Gamma_O.
Revised:  We can find corresponding wires in \Gamma_I . We argue that the structural difference between the two drawings can be captured by the spirality of the wires in \Gamma_I, which guides our morph from \Gamma_I to \Gamma_O.
Edit intent: meaning-changed
Original:  These homotopy classes can be represented by orthogonal polylines in \Gamma_I . We argue that the structural difference between the two drawings can be captured by the spirality of the wires in \Gamma_I, which guides our morph from \Gamma_I to \Gamma_O.
Revised:  These homotopy classes can be represented by orthogonal polylines in \Gamma_I that share topological properties with the wires in \Gamma_O. The structural difference between the two drawings can be captured by the spirality of the wires in \Gamma_I, which guides our morph from \Gamma_I to \Gamma_O.
Edit intent: meaning-changed
Original: Time irreversibility, a fundamental property of complex system, can be characterized by the directional asymmetry of probabilistic distributions, which is quantified by equalities-involved permutation relative entropy in our nonlinear dynamics analysis of heart rates . Equal values are not rare in discrete heartbeats because of the limits of resolution of signals collection, and more importantly equal states contain underlying important cardiac regulation information which is neglected by the original permutative method and some measurements of time irreversibility .
Revised: Symbolic relative entropy, an efficient nonlinear complexity parameter measuring probabilistic divergences of symbolic sequences, is proposed in our nonlinear dynamics analysis of heart rates . Equal values are not rare in discrete heartbeats because of the limits of resolution of signals collection, and more importantly equal states contain underlying important cardiac regulation information which is neglected by the original permutative method and some measurements of time irreversibility .
Edit intent: clarity
Original:  which is quantified by equalities-involved permutation relative entropy in our nonlinear dynamics analysis of heart rates . Equal values are not rare in discrete heartbeats because of the limits of resolution of signals collection, and more importantly equal states contain underlying important cardiac regulation information which is neglected by the original permutative method and some measurements of time irreversibility .
Revised:  which is quantified by equalities-involved permutation relative entropy in our nonlinear dynamics analysis of heart rates considering equal states. Equalities are not rare in discrete heartbeats because of the limits of resolution of signals collection, and more importantly equal states contain underlying important cardiac regulation information which is neglected by the original permutative method and some measurements of time irreversibility .
Edit intent: clarity