wanyu/IteraTeR_full_sent huggingface dataset:
Original: We present ElPiGraph, a method for approximating data distributions having non-trivial topological features such as the existence of excluded regionsor branching structures. Unlike many existing methods, ElPiGraph is not based on the construction of a k-nearest neighbour graph, a procedure that can perform poorly in the case of multidimensional and noisy data.
Revised: We present ElPiGraph, a method for approximating data distributions having non-trivial distributions with branching trajectories and excluded regions, with the recent single-cell transcriptomic studies of developing embryo being notable examples. Reducing the complexity and producing compact and interpretable representations of such data remains a challenging task. Most of the existing computational methods are based on exploring the local data point neighbourhood relations, a step that can perform poorly in the case of multidimensional and noisy data.
Edit intent: meaning-changed
Original:  Instead, ElPiGraphconstructs elastic principal graphs in a more robust way by minimizing elastic energy, applying graph grammars and explicitly controlling topological complexity. Using trimmed approximation error function makes ElPiGraph extremely robust to the presence of background noise without decreasing computational performance and allows it to deal with complex cases of manifold learning (for example, ElPiGraph can learn disconnected intersecting manifolds). Thanks to the quasi-quadratic nature of the elastic function, ElPiGraph performs almost as fast as a simple k-means clustering and, therefore, is much more scalable than alternative methods, and can work on large datasets containing millions of high dimensional points on a personal computer. The excellent performance of the method opens the possibility to apply resampling and to approximate complex data structures via principal graph ensembles which can be used to construct consensus principal graphs.
Revised:  Here we present ElPiGraph, a scalable and robust method for approximation of datasets with complex structures which does not require computing the complete data distance matrix or the data point neighbourhood graph. This method is able to withstand high levels of noise and is capable of approximating complex topologies via principal graph ensembles which can be used to construct consensus principal graphs.
Edit intent: meaning-changed
Original:  The excellent performance of the method opens the possibility to apply resampling and to approximate complex data structures via principal graph ensembles which can be used to construct consensus principal graphs. ElPiGraph is currently implemented in five programming languages and accompanied by a graphical user interface, which makes it a versatile tool to deal with complex data in various fields from molecular biology, where it can be used to infer pseudo-time trajectories from single-cell RNASeq , to astronomy, where it can be used to approximate complex structures in the distribution of galaxies.
Revised:  The excellent performance of the method opens the possibility to apply resampling and to approximate complex data structures via principal graph ensembles that can be combined into a consensus principal graph. ElPiGraph deals efficiently with large and complex datasets in various fields from molecular biology, where it can be used to infer pseudo-time trajectories from single-cell RNASeq , to astronomy, where it can be used to approximate complex structures in the distribution of galaxies.
Edit intent: clarity
Original:  ElPiGraph is currently implemented in five programming languages and accompanied by a graphical user interface, which makes it a versatile tool to deal with complex data in various fields from molecular biology, where it can be used to infer pseudo-time trajectories from single-cell RNASeq , to astronomy, where it can be used to approximate complex structures in the distribution of galaxies.
Revised:  ElPiGraph is currently implemented in five programming languages and accompanied by a graphical user interface, which makes it a versatile tool to deal with complex data in various fields from biology, where it can be used to infer pseudo-time trajectories from single-cell RNASeq , to astronomy, where it can be used to approximate complex structures in the distribution of galaxies.
Edit intent: clarity
Original:  ElPiGraph is currently implemented in five programming languages and accompanied by a graphical user interface, which makes it a versatile tool to deal with complex data in various fields from molecular biology, where it can be used to infer pseudo-time trajectories from single-cell RNASeq , to astronomy, where it can be used to approximate complex structures in the distribution of galaxies.
Revised:  ElPiGraph is currently implemented in five programming languages and accompanied by a graphical user interface, which makes it a versatile tool to deal with complex data in various fields from molecular biology, where it can be used to infer gene dynamics from single-cell RNASeq , to astronomy, where it can be used to approximate complex structures in the distribution of galaxies.
Edit intent: meaning-changed
Original:  ElPiGraph is currently implemented in five programming languages and accompanied by a graphical user interface, which makes it a versatile tool to deal with complex data in various fields from molecular biology, where it can be used to infer pseudo-time trajectories from single-cell RNASeq , to astronomy, where it can be used to approximate complex structures in the distribution of galaxies.
Revised:  ElPiGraph is currently implemented in five programming languages and accompanied by a graphical user interface, which makes it a versatile tool to deal with complex data in various fields from molecular biology, where it can be used to infer pseudo-time trajectories from single-cell RNA-Seq , to astronomy, where it can be used to approximate complex structures in the distribution of galaxies.
Edit intent: meaning-changed
Original:  ElPiGraph is currently implemented in five programming languages and accompanied by a graphical user interface, which makes it a versatile tool to deal with complex data in various fields from molecular biology, where it can be used to infer pseudo-time trajectories from single-cell RNASeq , to astronomy, where it can be used to approximate complex structures in the distribution of galaxies.
Revised:  ElPiGraph is currently implemented in five programming languages and accompanied by a graphical user interface, which makes it a versatile tool to deal with complex data in various fields from molecular biology, where it can be used to infer pseudo-time trajectories from single-cell RNASeq , to astronomy, where it can be used to explore complex structures in the distribution of galaxies.
Edit intent: clarity
Original:  This spatial subsampling has hindered characterizing even most basic properties of collective spiking in cortical networks. In particular, two contradictory hypotheses prevailed for over a decade: the first proposed an asynchronous irregular state, the second a critical state. While distinguishing them is straightforward in models, we show that in experiments classical approaches fail to correctly infer network dynamics because of subsampling. Deploying a novel, subsampling-invariant estimator, we find that in vivo dynamics do not comply with either hypothesis , but instead occupy a narrow "reverberating " state consistently across multiple mammalian species and cortical areas.
Revised:  This spatial subsampling has hindered characterizing even most basic properties are not known with certainty, because their assessment is hampered by spatial subsampling, i.e. the limitation that only a tiny fraction of all neurons can be recorded simultaneously with millisecond precision. Building on a novel, subsampling-invariant estimator, we find that in vivo dynamics do not comply with either hypothesis , but instead occupy a narrow "reverberating " state consistently across multiple mammalian species and cortical areas.
Edit intent: clarity
Original:  Deploying a novel, subsampling-invariant estimator, we find that in vivo dynamics do not comply with either hypothesis , but instead occupy a narrow "reverberating " state consistently across multiple mammalian species and cortical areas.
Revised:  Deploying a novel, subsampling-invariant estimator, we fit and carefully validate a minimal model for cortical spike propagation. The model interpolates between two prominent states: asynchronous and critical. We find neither of them in cortical spike recordings across various species , but instead occupy a narrow "reverberating " state consistently across multiple mammalian species and cortical areas.
Edit intent: meaning-changed
Original:  Deploying a novel, subsampling-invariant estimator, we find that in vivo dynamics do not comply with either hypothesis , but instead occupy a narrow "reverberating " state consistently across multiple mammalian species and cortical areas. A generic model tuned to this reverberating state predicts single neuron, pairwise, and population properties . With these predictions we first validate the model and then deduce network properties that are challenging to obtain experimentally, like the network timescale and strength of cortical input .
Revised:  Deploying a novel, subsampling-invariant estimator, we find that in vivo dynamics do not comply with either hypothesis , but instead identify a narrow reverberating regime. This approach enables us to predict yet unknown properties from very short recordings and for every circuit individually, including responses to minimal perturbations, intrinsic network timescales, and the strength of external input compared to recurrent activation - thereby informing about the underlying coding principles for each circuit, area, state and task .
Edit intent: clarity
Original: We introduce empirical equilibrium, the prediction in a game that selects the Nash equilibria that can be approximated by a sequence of payoff-monotone distributions, a well-documented proxy for empirically plausible behavior. Then, we reevaluate implementation theory based on this equilibrium concept .
Revised: We introduce empirical equilibrium, a refinement of Nash equilibrium. In contrast to previous refinements in the literature, empirical equilibrium is based solely on observables and does not determine as implausible all weakly dominated behavior .
Edit intent: coherence
Original:  We show that in a partnership dissolution environment with complete information, two popular auctions that are essentially equivalent for the Nash equilibrium prediction, can be expected to differ in fundamental ways when they are operated. Besides the direct policy implications, two general consequences follow. First, a mechanism designer may not be constrained by typical invariance properties. Second, a mechanism designer who does not account for the empirical plausibility of equilibria may inadvertently design implicitly biased mechanisms .
Revised:  We show that a distribution of play is an empirical equilibrium if and only if it is the limit of regular Quantal Response Equilibria associated with quantal responses that are utility maximizing in the limit. This result provides a direct link between the practice of experimental economics and our refinement .
Edit intent: meaning-changed
Original: We introduce empirical equilibrium, a refinement of Nash equilibrium . In contrast to previous refinements in the literature, empirical equilibrium is based solely on observables and does not determine as implausible all weakly dominated behavior. We show that a distribution of play is an empirical equilibrium if and only if it is the limit of regular Quantal Response Equilibria associated with quantal responses that are utility maximizing in the limit. This result provides a direct link between the practice of experimental economics and our refinement .
Revised: We introduce empirical equilibrium, a refinement of Nash equilibrium that is based on a non-parametric characterization of empirical distributions of behavior in games (Velez and Brown,2020b arXiv:1907.12408). The refinement can be alternatively defined as those Nash equilibria that do not refute the regular QRE theory of Goeree, Holt, and Palfrey (2005). By contrast, some empirical equilibria may refute monotone additive randomly disturbed payoff models. As a by product, we show that empirical equilibrium does not coincide with refinements based on approximation by monotone additive randomly disturbed payoff models, and further our understanding of the empirical content of these models .
Edit intent: meaning-changed
Original: Capital investment literature implicitly assumes that exponential growthof consumption can last indefinitely into the future. However, in the context of climate finance with benefits on current investments deferred to centuries from now , this process is fundamentally constrained by finite biophysical resources of Earth.
Revised: We introduce the logistic model of consumption growth, which captures a negative feedback loop preventing an unlimited growth of consumption due to finite biophysical resources of our planet. This simple dynamic model allows for derivation of the expression describing the declining long-term tail of a social discount curve. The latter plays a critical role in, e.g., climate finance with benefits on current investments deferred to centuries from now , this process is fundamentally constrained by finite biophysical resources of Earth.
Edit intent: meaning-changed
Original:  However, in the context of climate finance with benefits on current investments deferred to centuries from now , this process is fundamentally constrained by finite biophysical resources of Earth. Similarly to a population dynamics, a negative feedback mechanism will eventually prevent unlimited growth of consumption and reduce its rate. We apply this basic insight to valuation of long-term social discount rates . Combining the Ramsey optimal growth framework with the marginal-utility approach and the Verhulst logistic model, we demonstrate that inevitable slowing down of consumption growth leads to a declining long-term tail of the discount curve.
Revised:  However, in the context of climate finance with benefits on current investments deferred to centuries from now . The growth rate of consumption is irregularly evolving in time, which makes estimation of an expected term-structure of consumption growth and associated social discount rates . Combining the Ramsey optimal growth framework with the marginal-utility approach and the Verhulst logistic model, we demonstrate that inevitable slowing down of consumption growth leads to a declining long-term tail of the discount curve.
Edit intent: clarity
Original:  We apply this basic insight to valuation of long-term social discount rates . Combining the Ramsey optimal growth framework with the marginal-utility approach and the Verhulst logistic model, we demonstrate that inevitable slowing down of consumption growth leads to a declining long-term tail of the discount curve.
Revised:  We apply this basic insight to valuation of long-term social discount rates a challenging task. Nonetheless, observations show that the problem at hand is perturbative with the small parameter being the product of an average strength of fluctuations in the growth rate and its autocorrelation time. This fact permits utilization of the cumulant expansion method to derive remarkably simple expressions for the term-structure of expected consumption growth and associated discount rates in the bounded economy. Comparison with empirical data shows that the dynamic effect related to the planetary resource constrains could become a dominant mechanism responsible for a declining long-term tail of the discount curve.
Edit intent: meaning-changed
Original:  We apply this basic insight to valuation of long-term social discount rates . Combining the Ramsey optimal growth framework with the marginal-utility approach and the Verhulst logistic model, we demonstrate that inevitable slowing down of consumption growth leads to a declining long-term tail of the discount curve. This dynamic effect becomes stronger than the well-known "precautionary" effect related to uncertainty of a social planer in future growth rates of consumption in the relatively near future, which has been estimated here as about 100 years from now (the lower boundary).
Revised:  We apply this basic insight to valuation of long-term social discount rates . Combining the Ramsey optimal growth framework with the marginal-utility approach and the Verhulst logistic model, we demonstrate that inevitable slowing down of consumption growth leads to a declining long-term tail of a social planer in future growth rates of consumption in the relatively near future, which has been estimated here as about 100 years from now (the lower boundary).
Edit intent: coherence
Original:  This dynamic effect becomes stronger than the well-known "precautionary" effect related to uncertainty of a social planer in future growth rates of consumption in the relatively near future, which has been estimated here as about 100 years from now (the lower boundary).
Revised:  This dynamic effect becomes stronger than the well-known "precautionary" effect related to uncertainty of a social discount curve at the time horizon estimated here as about 100 years from now (the lower boundary).
Edit intent: clarity
Original:  This dynamic effect becomes stronger than the well-known "precautionary" effect related to uncertainty of a social planer in future growth rates of consumption in the relatively near future, which has been estimated here as about 100 years from now (the lower boundary).
Revised:  This dynamic effect becomes stronger than the well-known "precautionary" effect related to uncertainty of a social planer in future growth rates of consumption in the relatively near future, which has been estimated here as about100 years from now (the lower boundary).
Edit intent: fluency
Original:  Since the collection of risk factors selected for investigation is large and highly correlated, we use dimension reduction methods , including the Least Absolute Shrinkage and Selection Operator (LASSO ) and prototype clustering , to perform the investigation.
Revised:  Since the collection of risk factors is large and highly correlated, we use dimension reduction methods , including the Least Absolute Shrinkage and Selection Operator (LASSO ) and prototype clustering , to perform the investigation.
Edit intent: clarity
Original:  Since the collection of risk factors selected for investigation is large and highly correlated, we use dimension reduction methods , including the Least Absolute Shrinkage and Selection Operator (LASSO ) and prototype clustering , to perform the investigation. For comparison with the existing literature, we compare the multi-factor model 's performance with the Fama-French 5-factor model.
Revised:  Since the collection of risk factors selected for investigation is large and highly correlated, we use dimension reduction methods , including the Least Absolute Shrinkage and Selection Operator (LASSO ) and prototype clustering ) have to be used. The multi-factor model 's performance with the Fama-French 5-factor model.
Edit intent: coherence
Original:  For comparison with the existing literature, we compare the multi-factor model 's performance with the Fama-French 5-factor model. We find that both the Fama-French 5-factor and the multi-factor model are consistent with the behavior of "large-time scale" security returns. In a goodness-of-fit test comparing the Fama-French 5-factor with the multi-factor model, the multi-factor model has a substantially larger adjusted R^{2 . Robustness tests confirm that the multi-factor model provides a reasonable characterization of security returns .
Revised:  For comparison with the existing literature, we compare the multi-factor model is shown to have a significantly better fit than the Fama-French 5-factor with the multi-factor model, the multi-factor model has a substantially larger adjusted R^{2 . Robustness tests confirm that the multi-factor model provides a reasonable characterization of security returns .
Edit intent: clarity
Original:  In a goodness-of-fit test comparing the Fama-French 5-factor with the multi-factor model, the multi-factor model has a substantially larger adjusted R^{2 . Robustness tests confirm that the multi-factor model provides a reasonable characterization of security returns .
Revised:  In a goodness-of-fit test comparing the Fama-French 5-factor model . Robustness tests confirm that the multi-factor model provides a reasonable characterization of security returns .
Edit intent: coherence
Original:  In a goodness-of-fit test comparing the Fama-French 5-factor with the multi-factor model, the multi-factor model has a substantially larger adjusted R^{2 . Robustness tests confirm that the multi-factor model provides a reasonable characterization of security returns .
Revised:  In a goodness-of-fit test comparing the Fama-French 5-factor with the multi-factor model, the multi-factor model has a substantially larger adjusted R^{2 . Robustness tests are also provided .
Edit intent: clarity
Original: This paper re-investigates the estimation of multiple factor models relaxing the convention that the number of factors is smalland using a new approach for identifying factors .
Revised: The purpose of this paper is to test a multi-factor model for realized returns implied by the generalized arbitrage pricing theory (APT) recently developed by Jarrow and Protter (2016) and Jarrow (2016). This model relaxes the convention that the number of factors is smalland using a new approach for identifying factors .
Edit intent: meaning-changed
Original:  We first obtain the collection of all possible factors and then provide a simultaneous test, security by security, of which factors are significant . Since the collection of risk factors is large and highly correlated, high-dimension methods (including the LASSO and prototype clustering) have to be used.
Revised:  We first obtain the collection of all possible risk-factors and then provide a simultaneous test, security by security, of which factors are significant . Since the collection of risk factors is large and highly correlated, high-dimension methods (including the LASSO and prototype clustering) have to be used.
Edit intent: clarity
Original:  We first obtain the collection of all possible factors and then provide a simultaneous test, security by security, of which factors are significant . Since the collection of risk factors is large and highly correlated, high-dimension methods (including the LASSO and prototype clustering) have to be used.
Revised:  We first obtain the collection of all possible factors and then provide a simultaneous test, security by security, of which risk-factors are significant for which securities . Since the collection of risk factors is large and highly correlated, high-dimension methods (including the LASSO and prototype clustering) have to be used.
Edit intent: clarity
Original:  We first obtain the collection of all possible factors and then provide a simultaneous test, security by security, of which factors are significant . Since the collection of risk factors is large and highly correlated, high-dimension methods (including the LASSO and prototype clustering) have to be used.
Revised:  We first obtain the collection of all possible factors and then provide a simultaneous test, security by security, of which factors are significant . Since the collection of risk-factors is large and highly correlated, high-dimension methods (including the LASSO and prototype clustering) have to be used.
Edit intent: clarity