wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Moreover, we bound the size of the neural network needed to represent each iterate in terms of the neural network representing the previous iterate, resulting in a final network whose parameters depend polynomially on d and does not depend on the volume of the domain.
Revised:  Moreover, we bound the size of the neural network needed to represent each iterate in terms of the neural network representing the previous iterate, resulting in a final network whose parameters depend polynomially on d and no dependence on the volume of the domain.
Edit intent: clarity
Original:  Through formulating the attitude, velocity and position as one group state of group of double direct spatial isometries  , the transformed INS mechanization has proven to be group affine, which opens door to log-linearity and filtering consistency .
Revised:  Through formulating the attitude, velocity and position as one group state of group of double direct spatial isometries SE2(3) , the transformed INS mechanization has proven to be group affine, which opens door to log-linearity and filtering consistency .
Edit intent: meaning-changed
Original:  Through formulating the attitude, velocity and position as one group state of group of double direct spatial isometries  , the transformed INS mechanization has proven to be group affine, which opens door to log-linearity and filtering consistency .
Revised:  Through formulating the attitude, velocity and position as one group state of group of double direct spatial isometries , the transformed INS mechanization has proven to be group affine, which means that the corresponding vector error state model will be trajectory-independent .
Edit intent: meaning-changed
Original:  The INS/GPS and INS/Odometer integration are investigated as two representatives of inertial based applications . Some application aspects of the derived error state models in the two applications are presented, which include how to select the error state model, initialization of the SE2(3) based error state covariance and feedback correction corresponding to the error state definitions.
Revised:  The INS/GPS and INS/Odometer integration are investigated as two representatives of inertial based integration . Some application aspects of the derived error state models in the two applications are presented, which include how to select the error state model, initialization of the SE2(3) based error state covariance and feedback correction corresponding to the error state definitions.
Edit intent: fluency
Original:  Therefore, the derived error state models can be used in the so-called attitude alignment for the two applications  .
Revised:  Therefore, the derived error state models can be used in the so-called attitude alignment for the two applications . Moreover, the derived right error state-space model is also very preferred for long-endurance INS/Odometer integration due to the filtering consistency caused by its less dependence on the global state estimate .
Edit intent: meaning-changed
Original:  While ensuring and evaluating fairness as well as explainability of decision masking systems has been extensively studied independently, only little effort has been investigated into studying fairness of explanations on their own - i. e. the explanations it self should be fair .
Revised:  While fairness and explainability of decision masking systems has been extensively studied independently, only little effort has been investigated into studying fairness of explanations on their own - i. e. the explanations it self should be fair .
Edit intent: clarity
Original:  While ensuring and evaluating fairness as well as explainability of decision masking systems has been extensively studied independently, only little effort has been investigated into studying fairness of explanations on their own - i. e. the explanations it self should be fair .
Revised:  While ensuring and evaluating fairness as well as explainability of decision making systems have been extensively studied independently, only little effort has been investigated into studying fairness of explanations on their own - i. e. the explanations it self should be fair .
Edit intent: clarity
Original:  While ensuring and evaluating fairness as well as explainability of decision masking systems has been extensively studied independently, only little effort has been investigated into studying fairness of explanations on their own - i. e. the explanations it self should be fair .
Revised:  While ensuring and evaluating fairness as well as explainability of decision masking systems has been extensively studied independently, only little effort has been put into studying fairness of explanations on their own - i. e. the explanations it self should be fair .
Edit intent: clarity
Original:  While ensuring and evaluating fairness as well as explainability of decision masking systems has been extensively studied independently, only little effort has been investigated into studying fairness of explanations on their own - i. e. the explanations it self should be fair .
Revised:  While ensuring and evaluating fairness as well as explainability of decision masking systems has been extensively studied independently, only little effort has been investigated into studying fairness of explanations on their own .
Edit intent: coherence
Original:   In this work we formally and empirically study individual fairness and robustness of contrasting explanations - in particular we consider counterfactual explanations as a prominent instance of contrasting explanations.
Revised:  Current explanations can be unfair to an individual: an example is given by counterfactual explanations which propose different actions to change the output class to two similar individuals. In this work we formally and empirically study individual fairness and robustness of contrasting explanations - in particular we consider counterfactual explanations as a prominent instance of contrasting explanations.
Edit intent: meaning-changed
Original: Fairness and explainability are two important and closely related requirements of decision making systems . While fairness and explainability of decision making systems have been extensively studied independently, only little effort has been put into studying fairness of explanations on their own. Current explanations can be unfair to an individual: an example is given by counterfactual explanations which propose different actions to change the output class to two similar individuals .
Revised: Fairness and explainability are two important and closely related requirements of decision making systems when these should be deployed in the real world. It is usually achieved by providing explanations of the system's behavior. A prominent and intuitive type of explanations are counterfactual explanations. Counterfactual explanations explain a behavior to the user by proposing actions -- as changes to the input -- that would cause a different (specified) behavior of the system. However, such explanation methods can be unstable with respect to small changes to the input -- i.e. even a small change in the input can lead to huge or arbitrary changes in the output and of the explanation. This could be problematic for counterfactual explanations, as two similar individuals .
Edit intent: coherence
Original:  Current explanations can be unfair to an individual: an example is given by counterfactual explanations which propose different actions to change the output class to two similar individuals .
Revised:  Current explanations can be unfair to an individual: an example is given by counterfactual explanations which propose different actions to change the output class to two similar individuals might get very different explanations. Even worse, if the recommended actions differ considerably in their complexity, one would consider such unstable (counterfactual) explanations as individually unfair.
Edit intent: meaning-changed
Original:  In this work  we formally and empirically study individual fairness and its mathematical formalization as robustness for counterfactual explanations as a prominent instance of contrasting explanations. In addition , we propose to use plausible counterfactuals instead of closest counterfactuals for improving the individual fairness of counterfactual explanations.
Revised:  In this work we formally and empirically study the robustness of counterfactual explanations in general, as well as under different models and different kinds of perturbations. Furthermore , we propose to use plausible counterfactuals instead of closest counterfactuals for improving the individual fairness of counterfactual explanations.
Edit intent: clarity
Original: We introduce Deep Adaptive Design (DAD), a general method for amortizing the cost of performing sequential adaptive experiments using the framework of Bayesian optimal experimental design (BOED) . Traditional sequential BOED approaches require substantial computational time at each stage of the experiment.
Revised: We introduce Deep Adaptive Design (DAD), a method for amortizing the cost of performing sequential adaptive experiments using the framework of Bayesian optimal experimental design (BOED) . Traditional sequential BOED approaches require substantial computational time at each stage of the experiment.
Edit intent: clarity
Original: We introduce Deep Adaptive Design (DAD), a general method for amortizing the cost of performing sequential adaptive experiments using the framework of Bayesian optimal experimental design (BOED) . Traditional sequential BOED approaches require substantial computational time at each stage of the experiment.
Revised: We introduce Deep Adaptive Design (DAD), a general method for amortizing the cost of adaptive Bayesian experimental design that allows experiments to be run in real-time . Traditional sequential BOED approaches require substantial computational time at each stage of the experiment.
Edit intent: meaning-changed
Original: We introduce Deep Adaptive Design (DAD), a general method for amortizing the cost of performing sequential adaptive experiments using the framework of Bayesian optimal experimental design (BOED) . Traditional sequential BOED approaches require substantial computational time at each stage of the experiment.
Revised: We introduce Deep Adaptive Design (DAD), a general method for amortizing the cost of performing sequential adaptive experiments using the framework of Bayesian optimal experimental design (BOED) . Traditional sequential Bayesian optimal experimental design approaches require substantial computational time at each stage of the experiment.
Edit intent: fluency
Original: We introduce Deep Adaptive Design (DAD), a general method for amortizing the cost of performing sequential adaptive experiments using the framework of Bayesian optimal experimental design (BOED) . Traditional sequential BOED approaches require substantial computational time at each stage of the experiment.
Revised: We introduce Deep Adaptive Design (DAD), a general method for amortizing the cost of performing sequential adaptive experiments using the framework of Bayesian optimal experimental design (BOED) . Traditional sequential BOED approaches require substantial computation at each stage of the experiment.
Edit intent: clarity
Original:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption on source/target data, while in practice the target data often come from a different distribution than the source data . Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning.
Revised:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption on source/target data, while in practice domain shift between source and target is common . Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning.
Edit intent: meaning-changed
Original:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption on source/target data, while in practice domain shift between source and target is common . Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning.
Revised:  This is because most learning algorithms strongly rely on the i.i.d.~assumption on source/target data, while in practice domain shift between source and target is common . Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning.
Edit intent: clarity
Original:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption on source/target data, while in practice domain shift between source and target is common . Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning.
Revised:  This is because most statistical learning algorithms strongly rely on the i.i.d.~assumption on source/target data, which is often violated in practice due to domain shift . Domain generalization (DG) aims to achieve OOD generalization by using only source data for model learning.
Edit intent: clarity
Original:  More precisely, we need to find a set of k centers C so as to minimize the maximum over all groups j of u \text{ in group j} d(u,C)^p. The socially fair clustering problem was independently proposed by Abbasi, Bhaskara, and Venkatasubramanian [2021] and Ghadiri, Samadi, and Vempala [2021].
Revised:  More precisely, we need to find a set of k centers C so as to minimize the maximum over all groups j of u \text{ in group j} d(u,C)^p. The socially fair clustering problem was independently proposed by Ghadiri, Samadi, and Vempala [2021] and Ghadiri, Samadi, and Vempala [2021].
Edit intent: meaning-changed
Original:  More precisely, we need to find a set of k centers C so as to minimize the maximum over all groups j of u \text{ in group j} d(u,C)^p. The socially fair clustering problem was independently proposed by Abbasi, Bhaskara, and Venkatasubramanian [2021] and Ghadiri, Samadi, and Vempala [2021].
Revised:  More precisely, we need to find a set of k centers C so as to minimize the maximum over all groups j of u \text{ in group j} d(u,C)^p. The socially fair clustering problem was independently proposed by Abbasi, Bhaskara, and Venkatasubramanian [2021] and Abbasi, Bhaskara, and Venkatasubramanian [2021].
Edit intent: coherence
Original:  Our work has two-fold implications - it provides a general framework for representation, comparison, and learning of macromolecules ; and enables quantitative chemistry-informed decision-making and iterative design in the macromolecular chemical space.
Revised:  Our work has two-fold implications - it provides a general framework for representation, comparison, and learning of macromolecules , and it enables quantitative chemistry-informed decision-making and iterative design in the macromolecular chemical space.
Edit intent: coherence
Original: Covid-19 is a large shock to the economic system and just a few months of data changed the mean of many time series in significant ways . But while Covid-19 has economic consequences, it is not an economic shock. This creates a problem for the \covid estimation of economic factors from the data.
Revised: Covid-19 is a large shock to the economic system and just a few months changed the \covid estimation of economic factors from the data.
Edit intent: clarity
Original:  This creates a problem for the \covid estimation of economic factors from the data. The persistence of its effects also suggests a need for unconventional predictors in factor-augmented regressions. This note uses covid indicators to adjust the post-covid\covid data prior to factor estimation.
Revised:  This creates a problem for the \covid estimation of economic factors and dynamic causal effects because the variations around the outbreak can be interpreted as outliers, as shifts to the distribution of existing shocks, or as addition of new shocks. I take the latter view and use\covid data prior to factor estimation.
Edit intent: meaning-changed
Original:  This note uses covid indicators to adjust the post-covid\covid data prior to factor estimation. The adjustment preserves the pre-covid variations in the factors estimates. The real activity factor in FRED-MD is estimated to be down nearly four standard deviations in March/April 2020. Using economic and covid factors to generate h step ahead prediction errors, the JLN measure of uncertainty finds covid-19 to be the third most uncertain episode recorded since 1960.
Revised:  This note uses covid indicators to adjust the post-covid\covid data prior to estimation. I find that economic uncertainty remains high at the end of 2020 even though real economic activity has recovered and
Edit intent: clarity
Original: The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-%DIFDELCMD < \covid%%% \ forecasting models inadequate.
Revised: The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many %DIFDELCMD < \covid%%% \ forecasting models inadequate.
Edit intent: clarity
Original: The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-%DIFDELCMD < \covid%%% \ forecasting models inadequate.
Revised: The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-%DIFDELCMD < \covid%%% pre-covid forecasting models inadequate.
Edit intent: fluency
Original:  Dynamic responses of variables to shocks in a VAR similar in magnitude and shape to the ones identified before 2020 can be recovered by directly or indirectly modeling %DIFDELCMD < \covid%%% \ and treating it as exogenous.
Revised:  Dynamic responses of variables to shocks in a VAR similar in magnitude and shape to the ones identified before 2020 can be recovered by directly or indirectly modeling %DIFDELCMD < \covid%%% covid and treating it as exogenous.
Edit intent: meaning-changed
Original:  These responses to economic shocks are distinctly different from those to a %DIFDELCMD < \covid%%% \ shock , and distinguishing between the two types of shocks can be important in macroeconomic modeling post-%DIFDELCMD < \covid%%%  .
Revised:  These responses to economic shocks are distinctly different from those to a %DIFDELCMD < \covid%%% covid shock which are much larger but shorter lived. Disentangling the two types of shocks can be important in macroeconomic modeling post-%DIFDELCMD < \covid%%% .
Edit intent: meaning-changed
Original:  These responses to economic shocks are distinctly different from those to a %DIFDELCMD < \covid%%% \ shock , and distinguishing between the two types of shocks can be important in macroeconomic modeling post-%DIFDELCMD < \covid%%%  .
Revised:  These responses to economic shocks are distinctly different from those to a %DIFDELCMD < \covid%%% \ shock , and distinguishing between the two types of shocks can be important in macroeconomic modeling %DIFDELCMD < \covid%%% .
Edit intent: clarity
Original:  These responses to economic shocks are distinctly different from those to a %DIFDELCMD < \covid%%% \ shock , and distinguishing between the two types of shocks can be important in macroeconomic modeling post-%DIFDELCMD < \covid%%%  .
Revised:  These responses to economic shocks are distinctly different from those to a %DIFDELCMD < \covid%%% \ shock , and distinguishing between the two types of shocks can be important in macroeconomic modeling post-%DIFDELCMD < \covid%%% post-covid .
Edit intent: meaning-changed
Original: Periodic structures such as solid crystalline materials or textiles were previously studied as isolated structures without taking into account the continuity of their configuration spaces.
Revised: Periodic structures such as solid crystalline materials or textiles were previously classified in discrete and coarse ways that depend on manual choices or are unstable under perturbations. Since crystal structures are determined in a rigid form, their finest natural equivalence is defined by rigid motion or isometry, which preserves inter-point distances. Due to atomic vibrations, isometry classes of periodic point sets form a continuous space whose geometry and topology were unknown.
Edit intent: meaning-changed
Original:  The key new problem in Periodic Geometry is an isometry classification of periodic point sets. A required complete invariant should continuously change under point perturbations, because atoms always vibrate in real crystals. The main objects of Periodic Topology are embeddings of curves in a thickened plane that are invariant under lattice translations. Such periodic knots were classified in the past up to continuous deformations (isotopies) that keep a fixed lattice structure, hence are realized in a fixed thickened torus. The more practical equivalence is a periodic isotopyin a thickened plane without fixing a lattice basis.
Revised:  The key new problem in Periodic Geometry is to unambiguously parameterize this space of isometry classes by continuous coordinates that allow a complete reconstruction of any crystal. The major part of this manuscript reviews the recently developed isometry invariants to resolve the above problem: (1) density functions computed from higher order Voronoi zones, (2) distance-based invariants that allow ultra-fast visualizations of huge crystal datasets, and (3) the complete invariant isoset (a DNA-type code) with a first continuous metric on all periodic crystals. The main goal of Periodic Topology is to classify textiles up to periodic isotopy, which is a continuous deformation of a thickened plane without fixing a lattice basis.
Edit intent: meaning-changed
Original:  To ad-dress these issues, we propose a method to generate syntheticimages from existing categorical emotion datasets using facemorphing, with full control over the resulting sample distri-bution as well as dimensional labels in the circumplex space  , while achieving augmentation factors of at least 20x or more.
Revised:  To address these issues, we propose a method to generate syntheticimages from existing categorical emotion datasets using facemorphing, with full control over the resulting sample distri-bution as well as dimensional labels in the circumplex space , while achieving augmentation factors of at least 20x or more.
Edit intent: fluency
Original:  To ad-dress these issues, we propose a method to generate syntheticimages from existing categorical emotion datasets using facemorphing, with full control over the resulting sample distri-bution as well as dimensional labels in the circumplex space  , while achieving augmentation factors of at least 20x or more.
Revised:  To ad-dress these issues, we propose a method to generate synthetic images from existing categorical emotion datasets using facemorphing, with full control over the resulting sample distri-bution as well as dimensional labels in the circumplex space , while achieving augmentation factors of at least 20x or more.
Edit intent: fluency
Original:  To ad-dress these issues, we propose a method to generate syntheticimages from existing categorical emotion datasets using facemorphing, with full control over the resulting sample distri-bution as well as dimensional labels in the circumplex space  , while achieving augmentation factors of at least 20x or more.
Revised:  To ad-dress these issues, we propose a method to generate syntheticimages from existing categorical emotion datasets using face morphing as well as dimensional labels in the circumplex space , while achieving augmentation factors of at least 20x or more.
Edit intent: clarity