wanyu/IteraTeR_full_sent huggingface dataset:
Original:  That is, we make use of (a) Neyman-orthogonal and doubly robust score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias.
Revised:  That is, we make use of (a) Neyman-orthogonal , doubly robust, and efficient score functions, which imply the robustness of treatment effect estimation to moderate regularization biases in the machine learning-based estimation of the outcome, treatment, or sample selection models and (b) sample splitting (or cross-fitting) to prevent overfitting bias.
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Original:  We demonstrate that the proposed estimators are asymptotically normal and root-n consistent under specific regularity conditions concerning the machine learners  .
Revised:  We demonstrate that the proposed estimators are asymptotically normal and root-n consistent under specific regularity conditions concerning the machine learners and investigate their finite sample properties in a simulation study. We also apply our proposed methodology to the Job Corps data for evaluating the effect of training on hourly wages which are only observed conditional on employment .
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Original:  It is assumed that the number of i.i.d. Gaussian features grows superlinear in the number of samples. The main result is that under these conditions the Euclidean error of recovering the true regressor is of the order of the average noise level .
Revised:  A quantitative bound for the prediction error is given, relating it to the Rademacher complexity of the covariates, the norm of the minimum norm interpolator of the errors and the shape of the subdifferential around the true parameter. The general theory is illustrated with several examples: the sparse linear model with minimum \ell_1-norm or group Lasso penalty interpolation, the low rank trace regression model with nuclear norm minimization, and minimum Euclidean norm interpolation in the linear model. In case of sparsity or low-rank inducing norms, minimum norm interpolation yields a prediction error of the order of the average noise level .
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Original:  The main result is that under these conditions the Euclidean error of recovering the true regressor is of the order of the average noise level . Hence, the regressor recovered by basis pursuit is close to the truth if the average noise level is small . Lower bounds that show near optimality of the results complement the analysis .
Revised:  The main result is that under these conditions the Euclidean error of recovering the true regressor is of the order of the average noise level , provided that the overparameterization is at least a logarithmic factor larger than the number of samples . Lower bounds that show near optimality of the results complement the analysis .
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Original:  Hence, the regressor recovered by basis pursuit is close to the truth if the average noise level is small . Lower bounds that show near optimality of the results complement the analysis . In addition, these results are extended to low rank trace regression. The proofs rely on new lower tail bounds for maxima of Gaussians vectors and the spectral norm of Gaussian matrices, respectively, and might be of independent interest as they are significantly stronger than the corresponding upper tail bounds .
Revised:  Hence, the regressor recovered by basis pursuit is close to the truth if the average noise level is small . Lower bounds that show near optimality of the results complement the analysis .
Edit intent: coherence
Original:  This allows us to adapt existing results on order-independent  queues to stochastic matching models, and in particular to derive closed-form expressions for several performance metrics, like the waiting probability or the mean matching time, that can be implemented using dynamic programming.
Revised:  This allows us to adapt existing results on order-independent (loss) queues to stochastic matching models, and in particular to derive closed-form expressions for several performance metrics, like the waiting probability or the mean matching time, that can be implemented using dynamic programming.
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Original:  This paper proposes a robustness metric called Average Network Flow (ANF) , which satisfies these three properties, but is not based on graph spectra. Also, an efficient algorithm based on Gomory-Hu tree for calculating ANF is presented.
Revised:  This paper proposes a robustness metric called Average Network Flow (ANF) as a robustness metric, and proves that it increases strictly, and gives an algorithm to compute ANF with a quadratic complexity by leveraging Gomory-Hu tree for calculating ANF is presented.
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Original:  Also, an efficient algorithm based on Gomory-Hu tree for calculating ANF is presented. Finally , this paper compares ANF with eight representative existing metrics, showing that each metric has its own characteristics  in measuring network robustness and hence they are not equivalent.
Revised:  Also, an efficient algorithm based on Gomory-Hu trees. Thus, with ANF intrinsically considering global network topologies, ANF is unveiled to be a new robustness metric satisfying those three properties. Moreover , this paper compares ANF with eight representative existing metrics, showing that each metric has its own characteristics in measuring network robustness and hence they are not equivalent.
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Original:  Finally , this paper compares ANF with eight representative existing metrics, showing that each metric has its own characteristics  in measuring network robustness and hence they are not equivalent. Moreover, by focusing our comparisons on the scenarios in which network topologies change while preserving the same numbers of nodes and links, some specific observations on robustness metrics are reported.
Revised:  Finally , this paper compares ANF with eight representative existing metrics, showing that each metric has its own characteristics in measuring network robustness and it is recommended to apply several metrics together to gain a comprehensive view. Finally, by experimenting on the scenarios in which network topologies change while preserving the same numbers of nodes and links, some specific observations on robustness metrics are reported.
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Original: We introduce a new approach to deep learning on 3D surfaces such as meshes or point clouds. Our key insight is that a simple learned diffusion layer can spatially share data in a principled manner, replacing operations like convolution and pooling which are complicated and expensive on surfaces.
Revised: We introduce a new approach to deep learning on 3D surfaces , based on the insight that a simple learned diffusion layer can spatially share data in a principled manner, replacing operations like convolution and pooling which are complicated and expensive on surfaces.
Edit intent: coherence
Original:  Our key insight is that a simple learned diffusion layer can spatially share data in a principled manner, replacing operations like convolution and pooling which are complicated and expensive on surfaces.
Revised:  Our key insight is that a simple diffusion layer is highly effective for spatial communication. The resulting networks automatically generalize across different samplings and resolutions of a surface -- a basic property which is crucial for practical applications. Our networks can be discretized on various geometric representations such as triangle meshes or point clouds, and can even be trained on one representation then applied to another. We optimize the spatial support of diffusion as a continuous network parameter ranging from purely local to totally global, removing the burden of manually choosing neighborhood sizes.
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Original:  The only other ingredients in our network are a spatial gradient operation, which uses dot-products of derivatives to encode tangent-invariant filters, and a multi-layer perceptron applied independently at each point . The resulting architecture, which we call DiffusionNet, is remarkably simple, efficient, and scalable. Continuously optimizing for spatial support avoids the need to pick neighborhood sizes or filter widths a priori, or worry about their impact on network size/training time. Furthermore, the principled, geometric nature of these networks makes them agnostic to the underlying representation and insensitive to discretization. In practice, this means significant robustness to mesh sampling, and even the ability to train on a mesh and evaluate on a point cloud. Our experiments demonstrate that these networks achieve state-of-the-art results for a variety of tasks on both meshes and point clouds, including surface classification, segmentation, and non-rigid correspondence.
Revised:  The only other ingredients in our network are a spatial gradient operation, which uses dot-products of derivatives to encode tangent-invariant filters, and a multi-layer perceptron applied independently at each point , and spatial gradient features to support directional filters. The resulting networks are simple, robust, and efficient. Here, we focus primarily on triangle mesh surfaces, and demonstrate state-of-the-art results for a variety of tasks on both meshes and point clouds, including surface classification, segmentation, and non-rigid correspondence.
Edit intent: clarity
Original:  We first examine a state-of-the-art generative QA model, T5, and examine whether its probabilities are well calibrated, finding the answer is a relatively emphatic no.
Revised:  We first examine a state-of-the-art generative QA model, T5, BART, and GPT-2 -- and study whether their probabilities on QA tasks are well calibrated, finding the answer is a relatively emphatic no.
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Original:  Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle et al. (2018) and is a practical tool to solve nonsmooth large-scale optimization problems.
Revised: The Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle et al. (2018) and is a practical tool to solve nonsmooth large-scale optimization problems.
Edit intent: fluency
Original:  Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle et al. (2018) and is a practical tool to solve nonsmooth large-scale optimization problems.
Revised:  Stochastic Primal-Dual Hybrid Gradient (SPDHG) was proposed by Chambolle et al. (2018) and is an efficient algorithm to solve some nonsmooth large-scale optimization problems.
Edit intent: clarity
Original:  The proof makes use of a classical supermartingale result, and also rewrites the algorithm as a sequence of random continuous operators in the primal-dual space. We compare our analysis with a similar argument by Alacaoglu et al., and give sufficient conditions for an unproven claim in their proof .
Revised:  We also look into its application to parallel Magnetic Resonance Imaging reconstruction in order to test performance of SPDHG. Our numerical results show that for a range of settings SPDHG converges significantly faster than its deterministic counterpart .
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Original:  I characterize an informativeness condition under which this retention rule is endogenous, provided that the principal uses an opaque information policy -- she observes the delegate's policy choice and outcomes, but not the effort.
Revised:  I characterize an "informativeness condition" under which this retention rule is endogenous, provided that the principal uses an opaque information policy -- she observes the delegate's policy choice and outcomes, but not the effort.
Edit intent: fluency
Original:  I characterize an informativeness condition under which this retention rule is endogenous, provided that the principal uses an opaque information policy -- she observes the delegate's policy choice and outcomes, but not the effort.
Revised:  I characterize an informativeness condition under which this retention rule is endogenous, provided that the principal uses an opaque information policy : she observes the delegate's policy choice and outcomes, but not the effort.
Edit intent: fluency
Original:  Compared to stiffness-optimal closed cell plate material, the material class reduces the Young's modulus to a range from 79\% to 58\%, and improves the uniaxial buckling strength to a range from 180\% to 767\%.
Revised:  Compared to stiffness-optimal closed-cell plate material, the material class reduces the Young's modulus to a range from 79\% to 58\%, and improves the uniaxial buckling strength to a range from 180\% to 767\%.
Edit intent: fluency
Original:  Compared to stiffness-optimal closed cell plate material, the material class reduces the Young's modulus to a range from 79\% to 58\%, and improves the uniaxial buckling strength to a range from 180\% to 767\%.
Revised:  Compared to stiffness-optimal closed cell plate material, the material class reduces the Young's modulus to a range from 79\% to 58\%, but improves the uniaxial buckling strength to a range from 180\% to 767\%.
Edit intent: coherence
Original:  This paper mainly can be divided into 4 steps: 1) Planning stable cycloid trajectory for initial step and subsequent step for climbing upstairs . 2) Inverse Kinematics using  unsupervised artificial neural network with knot shifting procedure for jerk minimization.
Revised:  This paper mainly can be divided into 3 steps: 1) Planning stable cycloid trajectory for initial step and subsequent step for climbing upstairs . 2) Inverse Kinematics using unsupervised artificial neural network with knot shifting procedure for jerk minimization.
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Original:  This paper mainly can be divided into 4 steps: 1) Planning stable cycloid trajectory for initial step and subsequent step for climbing upstairs . 2) Inverse Kinematics using  unsupervised artificial neural network with knot shifting procedure for jerk minimization.
Revised:  This paper mainly can be divided into 4 steps: 1) Planning stable cycloid trajectory for initial step and subsequent step for climbing upstairs and Inverse Kinematics using unsupervised artificial neural network with knot shifting procedure for jerk minimization.
Edit intent: coherence
Original:  This paper mainly can be divided into 4 steps: 1) Planning stable cycloid trajectory for initial step and subsequent step for climbing upstairs . 2) Inverse Kinematics using  unsupervised artificial neural network with knot shifting procedure for jerk minimization.
Revised:  This paper mainly can be divided into 4 steps: 1) Planning stable cycloid trajectory for initial step and subsequent step for climbing upstairs . 2) Inverse Kinematics using an unsupervised artificial neural network with knot shifting procedure for jerk minimization.
Edit intent: fluency
Original:  3 ) Modeling Dynamics for Toe foot biped model using Lagrange Dynamics along with contact modeling using spring damper system , and finally 4) Real time joint angle trajectory tracking optimization using Temporal Quantized Lagrange Dynamics which takes inverse kinematics output from neural network as its inputs.
Revised:  2 ) Modeling Dynamics for Toe foot biped model using Lagrange Dynamics along with contact modeling using spring damper system , and finally 4) Real time joint angle trajectory tracking optimization using Temporal Quantized Lagrange Dynamics which takes inverse kinematics output from neural network as its inputs.
Edit intent: fluency
Original:  3 ) Modeling Dynamics for Toe foot biped model using Lagrange Dynamics along with contact modeling using spring damper system , and finally 4) Real time joint angle trajectory tracking optimization using Temporal Quantized Lagrange Dynamics which takes inverse kinematics output from neural network as its inputs.
Revised:  3 ) Modeling Dynamics for Toe foot biped model using Lagrange Dynamics along with contact modeling using spring-damper system followed by developing Neural Network Temporal Quantized Lagrange Dynamics which takes inverse kinematics output from neural network as its inputs.
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Original:  The goal of this paper is to introduce a corrected implementation of linear interpolatory subdivision schemes addressing both properties at the same time: regularity and adaption to singularities with high order of accuracy. In both cases of point-value data and of cell-average data,  we are able to construct a subdivision-based algorithm producing approximations with high precision and limit functions with high piecewise regularity  and without diffusion nor oscillations in the presence of discontinuities.
Revised:  The goal of this paper is to introduce a new approach having both properties of full accuracy and regularity. In order to obtain it, we propose a three-stage algorithm: first, the data is smoothed by subtracting an appropriate non-smooth data sequence; then a chosen high order linear approximation operator is applied to the smoothed data and finally, an approximation with the proper singularity structure is reinstated by correcting the smooth approximation with the non-smooth element used in the first stage. We apply this approach to both cases of point-value data and of cell-average data, we are able to construct a subdivision-based algorithm producing approximations with high precision and limit functions with high piecewise regularity and without diffusion nor oscillations in the presence of discontinuities.
Edit intent: coherence
Original:  In both cases of point-value data and of cell-average data,  we are able to construct a subdivision-based algorithm producing approximations with high precision and limit functions with high piecewise regularity  and without diffusion nor oscillations in the presence of discontinuities.
Revised:  In both cases of point-value data and of cell-average data, using the 4-point subdivision algorithm in the second stage. Using the proposed approach we are able to construct a subdivision-based algorithm producing approximations with high precision and limit functions with high piecewise regularity and without diffusion nor oscillations in the presence of discontinuities.
Edit intent: meaning-changed
Original:  In both cases of point-value data and of cell-average data,  we are able to construct a subdivision-based algorithm producing approximations with high precision and limit functions with high piecewise regularity  and without diffusion nor oscillations in the presence of discontinuities.
Revised:  In both cases of point-value data and of cell-average data, we are able to construct approximations with high precision and limit functions with high piecewise regularity and without diffusion nor oscillations in the presence of discontinuities.
Edit intent: clarity
Original:  In both cases of point-value data and of cell-average data,  we are able to construct a subdivision-based algorithm producing approximations with high precision and limit functions with high piecewise regularity  and without diffusion nor oscillations in the presence of discontinuities.
Revised:  In both cases of point-value data and of cell-average data, we are able to construct a subdivision-based algorithm producing approximations with high precision , with high piecewise regularity and without diffusion nor oscillations in the presence of discontinuities.
Edit intent: clarity
Original:  In both cases of point-value data and of cell-average data,  we are able to construct a subdivision-based algorithm producing approximations with high precision and limit functions with high piecewise regularity  and without diffusion nor oscillations in the presence of discontinuities.
Revised:  In both cases of point-value data and of cell-average data, we are able to construct a subdivision-based algorithm producing approximations with high precision and limit functions with high piecewise regularity , and without diffusion nor oscillations in the presence of discontinuities.
Edit intent: fluency
Original:  Experimental results show  evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, and gun laws . Motivated reasoning from these messages leads people's beliefs to become more polarized and less accurate, even though the messages are uninformative.
Revised:  Experimental results show novel evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, and gun laws . Motivated reasoning from these messages leads people's beliefs to become more polarized and less accurate, even though the messages are uninformative.
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Original:  Experimental results show  evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, and gun laws . Motivated reasoning from these messages leads people's beliefs to become more polarized and less accurate, even though the messages are uninformative.
Revised:  Experimental results show evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, gun laws, and the performance of other subjects . Motivated reasoning from these messages leads people's beliefs to become more polarized and less accurate, even though the messages are uninformative.
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Original:  Experimental results show  evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, and gun laws . Motivated reasoning from these messages leads people's beliefs to become more polarized and less accurate, even though the messages are uninformative.
Revised:  Experimental results show evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, and gun laws . Motivated reasoning from messages on these topics leads people's beliefs to become more polarized and less accurate, even though the messages are uninformative.
Edit intent: clarity
Original:  This paper creates a novel experimental design to identify motivated reasoning from Bayesian updating when people enter into the experiment with endogenously different beliefs. It analyzes how subjects assess the veracity of information sources that tell them the median of their belief distribution is too high or too low.
Revised:  This paper creates a novel experimental design to identify motivated reasoning from Bayesian updating across a variety of factual questions; the paradigm analyzes how subjects assess the veracity of information sources that tell them the median of their belief distribution is too high or too low.
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Original:  A Bayesian would infer nothing about the source veracity from this message, but a motivated reasoner would believe the source were more truthful when it reports the direction that he is more motivated to believe. Experimental results show novel evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, gun laws, and the performance of other subjects.
Revised:  A Bayesian would infer nothing about the source veracity from this message, but a motivated reasoner would believe the source were more truthful when it reports the direction that they find more attractive. I find novel evidence for politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender, climate change, gun laws, and the performance of other subjects.
Edit intent: meaning-changed