wanyu/IteraTeR_full_sent huggingface dataset:
Original:   In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner .
Revised:  In contrast to prior work, our results show that complexity differences can emerge at very short timescales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner .
Edit intent: fluency
Original:   In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner .
Revised:  In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner .
Edit intent: clarity
Original:   In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner .
Revised:  In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anesthesia in a previously unforeseen manner .
Edit intent: fluency
Original:   In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner . Furthering the links between physics, complexity science and neuroscience promotes the understanding of the physical basis that supports the level of conscious arousal in URLanisms .
Revised:  In contrast to prior work, our results show that complexity differences can emerge at very short time scales and across broad regions of the fly brain without the need to saturate Markov order , thus heralding the macroscopic state of anaesthesia in a previously unforeseen manner , and (2) that general anesthesia also modulates the temporal asymmetry of neural signals. Together, our results demonstrate that anesthetized brains become both less structured and more reversible .
Edit intent: meaning-changed
Original: We consider the problem of finding inscribed boxes and axis-aligned inscribed boxes of maximum volume, inside a compact and solid convex set. Our algorithms are capable of solving these two problems in any such set that can be represented with finite number of convex inequalities .
Revised: We consider the problem of finding inscribed boxes and axis-aligned ) inscribed parallelotopes and boxes in a compact convex set, defined by a finite number of convex inequalities .
Edit intent: meaning-changed
Original:  Our algorithms are capable of solving these two problems in any such set that can be represented with finite number of convex inequalities . For the axis-aligned case, we formulate the problem for higher dimensions and present an exact optimization algorithm which solves the problem in \mathcal{O(d^3+d^2n) time, where d is the dimension and n is the number of inequalities defining the convex set.
Revised:  Our algorithms are capable of solving these two problems in any such set that can be represented with finite number of convex inequalities , and presents an optimization approach for solving them. Several optimization models are developed that can be easily generalized to find other inscribed geometric shapes such as triangles, rhombi, and tetrahedrons. To find the largest axis-aligned case, we formulate the problem for higher dimensions and present an exact optimization algorithm which solves the problem in \mathcal{O(d^3+d^2n) time, where d is the dimension and n is the number of inequalities defining the convex set.
Edit intent: meaning-changed
Original:  For the axis-aligned case, we formulate the problem for higher dimensions and present an exact optimization algorithm which solves the problem in \mathcal{O(d^3+d^2n) time, where d is the dimension and n is the number of inequalities defining the convex set.
Revised:  For the axis-aligned (d^3+d^2n) time, where d is the dimension and n is the number of inequalities defining the convex set.
Edit intent: coherence
Original:  Using this exact algorithm as a subroutine, we present an \epsilon-approximation algorithm that computes (1-\epsilon)-approximation to the largest inscribed rectangle with computational complexity of O(\epsilon^{-1}n)}  . Finally, we show that how this running time can be improved to \mathcal{O(\epsilon^{-1}\log n) with a O(\epsilon^{-1}n) pre-processing time when the convex set is a polygon}  .
Revised:  Using this exact algorithm as a subroutine, we present an \epsilon-approximation algorithm that computes (1-\epsilon)-approximation to the largest inscribed rectangle with computational complexity of O(\epsilon^{-1}n)} inscribed rectangles in the higher dimensions, an interior-point method algorithm is presented and analyzed . Finally, we show that how this running time can be improved to \mathcal{O(\epsilon^{-1}\log n) with a O(\epsilon^{-1}n) pre-processing time when the convex set is a polygon} .
Edit intent: meaning-changed
Original:  Using this exact algorithm as a subroutine, we present an \epsilon-approximation algorithm that computes (1-\epsilon)-approximation to the largest inscribed rectangle with computational complexity of O(\epsilon^{-1}n)}  . Finally, we show that how this running time can be improved to \mathcal{O(\epsilon^{-1}\log n) with a O(\epsilon^{-1}n) pre-processing time when the convex set is a polygon}  .
Revised:  Using this exact algorithm as a subroutine, we present an \epsilon-approximation algorithm that computes (1-\epsilon)-approximation to the largest inscribed rectangle with computational complexity of O(\epsilon^{-1}n)} . Finally, (\epsilon^{-1}\log n) with a O(\epsilon^{-1}n) pre-processing time when the convex set is a polygon} .
Edit intent: coherence
Original:  Using this exact algorithm as a subroutine, we present an \epsilon-approximation algorithm that computes (1-\epsilon)-approximation to the largest inscribed rectangle with computational complexity of O(\epsilon^{-1}n)}  . Finally, we show that how this running time can be improved to \mathcal{O(\epsilon^{-1}\log n) with a O(\epsilon^{-1}n) pre-processing time when the convex set is a polygon}  .
Revised:  Using this exact algorithm as a subroutine, we present an \epsilon-approximation algorithm that computes (1-\epsilon)-approximation to the largest inscribed rectangle with computational complexity of O(\epsilon^{-1}n)} . Finally, we show that how this running time can be improved to \mathcal{O(\epsilon^{-1}\log n) with a O(\epsilon^{-1}n) pre-processing time when the convex set is a polygon} a parametrized optimization approach is developed to find the largest (axis-aligned) inscribed rectangles in two-dimensional space .
Edit intent: meaning-changed
Original:   In this technical report, we describe a newly developed tool, the Video Event Reconstruction and Analysis (VERA) system , that enables the localization of a shooter from just a few videos that include the sound of gunshots using established machine learning techniques and straightforward physics models.
Revised:  Machine learning and computer vision can play a crucial role in this process. In this technical report, we describe a newly developed tool, the Video Event Reconstruction and Analysis (VERA) system , that enables the localization of a shooter from just a few videos that include the sound of gunshots using established machine learning techniques and straightforward physics models.
Edit intent: meaning-changed
Original:   In this technical report, we describe a newly developed tool, the Video Event Reconstruction and Analysis (VERA) system , that enables the localization of a shooter from just a few videos that include the sound of gunshots using established machine learning techniques and straightforward physics models.
Revised:  In this technical report, we describe the Video Event Reconstruction and Analysis (VERA) system , that enables the localization of a shooter from just a few videos that include the sound of gunshots using established machine learning techniques and straightforward physics models.
Edit intent: clarity
Original:   In this technical report, we describe a newly developed tool, the Video Event Reconstruction and Analysis (VERA) system , that enables the localization of a shooter from just a few videos that include the sound of gunshots using established machine learning techniques and straightforward physics models. This tool relies on other tools we have already developed including video synchronization and geolocation to order unstructured videos lacking metadata over time and space, and sound detection algorithms.
Revised:  In this technical report, we describe a newly developed tool, the Video Event Reconstruction and Analysis (VERA) system . This new tool brings together a variety of capabilities we have developed over the past few years ( including video synchronization and geolocation to order unstructured videos lacking metadata over time and space, and sound detection algorithms.
Edit intent: clarity
Original:  This tool relies on other tools we have already developed including video synchronization and geolocation to order unstructured videos lacking metadata over time and space, and sound detection algorithms. Both this gunshot localization tool and the previous systems it incorporates are run through a web interface that enables human-in-the-loop verification to ensure accurate estimations .
Revised:  This tool relies on other tools we have already developed including video synchronization and geolocation to order unstructured videos lacking metadata over time and space, and sound recognition algorithms) to enable the reconstruction and analysis of events captured on video. Among other uses, VERA enables the localization of a shooter from just a few videos that include the sound of gunshots .
Edit intent: clarity
Original:  We do so in the hope that this tool can be used by anyone who needs it to protect and promote human rights and public safety. We also hope that researchers and software developers will be inspired to improve and expand this system moving forward to better meet the needs of public safety and human rights .
Revised:  We hope that researchers and software developers will be inspired to improve and expand this system moving forward to better meet the needs of public safety and human rights .
Edit intent: coherence
Original:  We fill this gap in the literature by analysing investor clusters in the first two years after the IPO filing in the Helsinki Stock Exchange by using a statistically validated network method to infer investor links based on the co-occurrences of investors' trade timing for 14 IPO stocks.
Revised:  We fill this gap in the literature by analysing investor clusters in the first two years after the IPO filing in the Helsinki Stock Exchange by using a statistically validated network method to infer investor links based on the co-occurrences of investors' trade timing for 69 IPO stocks.
Edit intent: meaning-changed
Original: The New Keynesian model makes several anomalous predictions at the zero lower bound : collapse of output and inflation , and implausibly large effects of forward guidance and government spending .
Revised: The New Keynesian model makes several anomalous predictions at the zero lower bound : collapse of output and inflation collapse to implausibly low levels, and that government spending and forward guidance have implausibly large effects of forward guidance and government spending .
Edit intent: meaning-changed
Original:  As a result, when the marginal utility of wealth is sufficiently large, the dynamical system representing the equilibrium at the zero lower bound becomes a source instead of a saddle---which resolves all the anomalies.
Revised:  As a result, when the marginal utility of wealth is sufficiently large, the dynamical system representing the equilibrium at the zero lower bound transforms from a saddle to a source---which resolves all the anomalies.
Edit intent: clarity
Original:  As a result, when the marginal utility of wealth is sufficiently large, the dynamical system representing the equilibrium at the zero lower bound transforms from a saddle to a source---which resolves all the anomalies.
Revised:  As a result, when the marginal utility of wealth is sufficiently large, the dynamical system representing the zero-lower-bound equilibrium transforms from a saddle to a source---which resolves all the anomalies.
Edit intent: clarity
Original: In this paper we construct a parsimonious causal model that addresses multiple issues researchers face when trying to use aggregate time-series shocks for policy evaluation: (a) potential unobserved aggregate confounders, (b) availability of various unit-level characteristics, (c) time and unit-level heterogeneity in treatment effects.
Revised: We propose a general strategy for estimating treatment effects, in contexts where the only source of exogenous variation is a sequence of aggregate time-series shocks for policy evaluation: (a) potential unobserved aggregate confounders, (b) availability of various unit-level characteristics, (c) time and unit-level heterogeneity in treatment effects.
Edit intent: clarity
Original: In this paper we construct a parsimonious causal model that addresses multiple issues researchers face when trying to use aggregate time-series shocks for policy evaluation: (a) potential unobserved aggregate confounders, (b) availability of various unit-level characteristics, (c) time and unit-level heterogeneity in treatment effects. We develop a new estimation algorithm that uses insights from treatment effects, panel, and time-series literature.
Revised: In this paper we construct a parsimonious causal model that addresses multiple issues researchers face when trying to use aggregate time-series shocks. We start by arguing that commonly used estimation procedures tend to ignore the crucial time-series literature.
Edit intent: clarity
Original:  We develop a new estimation algorithm that uses insights from treatment effects, panel, and time-series literature. We construct a variance estimatorthat is robust to arbitrary clustering pattern across geographical units. We achieve this by considering a finite population framework, where potential outcomes are treated as fixed, and all randomness comes from the exogenous shocks .
Revised:  We develop a new estimation algorithm that uses insights from treatment effects, panel, and time-series aspects of the data. Next, we develop a graphical tool and a novel test to illustrate the issues of the design using data from influential studies in development economics and macroeconomics. Motivated by these studies, we construct a new estimator, which is based on the time-series model for the aggregate shock. We analyze the statistical properties of our estimator in the practically relevant case, where both cross-sectional and time-series dimensions are of similar size .
Edit intent: meaning-changed
Original:  Finally, we illustrate our approach using data from a study on the causal relationship between foreign aid and conflict conducted in Nunn and Qian 2014 .
Revised:  Finally, we illustrate our approach using data from a study on the causal relationship between foreign aid and conflict conducted in Nunn and Qian to provide causal interpretation for our estimator, we analyze a new causal model that allows for rich unobserved heterogeneity in potential outcomes and unobserved aggregate shocks .
Edit intent: meaning-changed
Original:  Finally, to provide  causal interpretation for our estimator, we analyze a new causal model that allows for rich unobserved heterogeneity in potential outcomes and unobserved aggregate shocks.
Revised:  Finally, to provide a causal interpretation for our estimator, we analyze a new causal model that allows for rich unobserved heterogeneity in potential outcomes and unobserved aggregate shocks.
Edit intent: fluency
Original:  Finally, to provide  causal interpretation for our estimator, we analyze a new causal model that allows for rich unobserved heterogeneity in potential outcomes and unobserved aggregate shocks.
Revised:  Finally, to provide causal interpretation for our estimator, we analyze a new causal model that allows taking into account both rich unobserved heterogeneity in potential outcomes and unobserved aggregate shocks.
Edit intent: clarity
Original:  We start by arguing that commonly used estimation procedures tend to ignore crucial time-series aspects of the data. Next, we develop a graphical tool and a formal test to illustrate the issues of the design using data from prominent studies in development economics and macroeconomic. Motivated by these studies, we construct a new IV estimator, which is based on the time-series model for the aggregate shock.
Revised:  We start by arguing that commonly used estimation procedures tend to ignore crucial time-series model. We use the unit weights to control for unobserved aggregate confounders and use the time-series model for the aggregate shock.
Edit intent: meaning-changed
Original:  Motivated by these studies, we construct a new IV estimator, which is based on the time-series model for the aggregate shock. We analyze the statistical properties of ] our estimator in a practically relevant case , where both cross-sectional and time-series dimensions are of similar size.
Revised:  Motivated by these studies, we construct a new IV estimator, which is based on the time-series model to extract the quasi-random variation from the observed shock. We examine our algorithm's performance in a realistic simulation based on Nakamura and Steinsson ] our estimator in a practically relevant case , where both cross-sectional and time-series dimensions are of similar size.
Edit intent: meaning-changed
Original:  We analyze the statistical properties of ] our estimator in a practically relevant case , where both cross-sectional and time-series dimensions are of similar size. Finally, to provide a causal interpretation for our estimator, we analyze a new causal model that allows taking into account both rich unobserved heterogeneity in potential outcomes and unobserved aggregate shocks .
Revised:  We analyze the statistical properties of ] our estimator in a practically relevant case , where both cross-sectional and time-series dimensions are large and show how to use it to conduct robust inference .
Edit intent: clarity
Original:  We calculate this measure of systemic network concentration on a few data sets reporting exposures of financial institutions and show that typically the effect of common exposures is not fully contained by information at the level of single portfolio concentration  .
Revised:  We calculate this measure of systemic network concentration on a few data sets reporting exposures of financial institutions and show that typically the effect of common exposures is not fully contained by information at the level of single portfolio concentration measures. It also allows to quantify several aspects of the interplay between interconnectedness and credit risk .
Edit intent: meaning-changed
Original:  Although approximated, our common exposure adjustment is able to capture, with only two parameters, an aspect of systemic risk that goes beyond a view over single portfolios and analyzes the complexity of the interplay among (risk-adjusted) exposures .
Revised:  Although approximated, this common exposure adjustment is able to capture, with only two parameters, an aspect of systemic risk that goes beyond a view over single portfolios and analyzes the complexity of the interplay among (risk-adjusted) exposures .
Edit intent: clarity
Original:  Although approximated, our common exposure adjustment is able to capture, with only two parameters, an aspect of systemic risk that goes beyond a view over single portfolios and analyzes the complexity of the interplay among (risk-adjusted) exposures .
Revised:  Although approximated, our common exposure adjustment is able to capture, with only two parameters, an aspect of systemic risk that can extend single portfolios view to a system-wide one .
Edit intent: clarity
Original:  This paper proposes a refinement criterion for the identified set. Our criterion chooses a subset such that counterfactual predictions of outcomes are most stable against local perturbations of the reduced forms (e.g. the equilibrium selection rule).
Revised:  This paper shows that some parameter values can be more ``desirable'' than others for counterfactual analysis, even if they are empirically equivalent given the data. In particular, within the identified set, some counterfactual predictions can exhibit more robustness than others, against local perturbations of the reduced forms (e.g. the equilibrium selection rule).
Edit intent: clarity
Original:  Our refinement has multiple appealing features, including an intuitive characterization, lower computational cost, and stable predictions. Focusing on moment inequality models, we propose bootstrap inference on the refinement and provide generic conditions under which the inference is uniformly asymptotically valid. We present and discuss results from our Monte Carlo study and an empirical application based on a model with top-coded data.
Revised:  We provide a representation of this subset which can be used to simplify the implementation. We illustrate our message using moment inequality models, and provide an empirical application based on a model with top-coded data.
Edit intent: clarity
Original:  If measurements of input X and output Y are confounded, the causal relationship can nonetheless be identified if an instrumental variable Z is available that influences X directly, but is conditionally independent of Y given X  . The classic two-stage least squares algorithm (2SLS) simplifies the estimation problem by modeling all relationships as linear functions.
Revised:  If measurements of input X and output Y are confounded, the causal relationship can nonetheless be identified if an instrumental variable Z is available that influences X directly, but is conditionally independent of Y given X and the unmeasured confounder . The classic two-stage least squares algorithm (2SLS) simplifies the estimation problem by modeling all relationships as linear functions.
Edit intent: meaning-changed
Original:  By contrast, if players can make secret side-payments to each other, every coalition achieves a coalitional minmax value,  reducing the set of supportable payoffs to the core of the stage game.
Revised:  By contrast, if players can make secret side-payments to each other, every coalition achieves a coalitional minmax value, potentially reducing the set of supportable payoffs to the core of the stage game.
Edit intent: clarity
Original:  To estimate the treatment effect, researchers usually regress their outcome on a treatment indicator and pair fixed effects, and cluster standard errors at the unit-of-randomization level , namely, at the village level in our example .
Revised:  To estimate the treatment effect, researchers often regress their outcome on a treatment indicator and pair fixed effects, and cluster standard errors at the unit-of-randomization level , namely, at the village level in our example .
Edit intent: clarity