wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The main advantage of the BooST  is that it estimates the derivatives (partial effects) of very general nonlinear models, providing more interpretation than other tree based models concerning the mapping between the covariates and the dependent variable  .
Revised:  The main advantage of the BooST is that it estimates the derivatives (partial effects) of very general nonlinear models, providing more interpretation about the mapping between the covariates and the dependent variable .
Edit intent: clarity
Original:  The main advantage of the BooST  is that it estimates the derivatives (partial effects) of very general nonlinear models, providing more interpretation than other tree based models concerning the mapping between the covariates and the dependent variable  .
Revised:  The main advantage of the BooST is that it estimates the derivatives (partial effects) of very general nonlinear models, providing more interpretation than other tree based models concerning the mapping between the covariates and the dependent variable than other tree based models, such as Random Forests .
Edit intent: meaning-changed
Original: In this paper  we introduce a new machine learning (ML) model for nonlinear regression called Boosting Smooth Transition Regression Trees (BooST)  .
Revised: In this paper we introduce a new machine learning (ML) model for nonlinear regression called Boosting Smooth Transition Regression Trees (BooST) , which is a combination of boosting algorithms with smooth transition regression trees .
Edit intent: meaning-changed
Original:  The main advantage of the BooST model is that it estimates the derivatives (partial effects) of very general nonlinear models , providing more interpretation about the mapping between the covariates and the dependent variable than other tree based models, such as Random Forests.
Revised:  The main advantage of the BooST model is the estimation of the derivatives (partial effects) of very general nonlinear models , providing more interpretation about the mapping between the covariates and the dependent variable than other tree based models, such as Random Forests.
Edit intent: coherence
Original:  The main advantage of the BooST model is that it estimates the derivatives (partial effects) of very general nonlinear models , providing more interpretation about the mapping between the covariates and the dependent variable than other tree based models, such as Random Forests.
Revised:  The main advantage of the BooST model is that it estimates the derivatives (partial effects) of very general nonlinear models . Therefore, the model can provide more interpretation about the mapping between the covariates and the dependent variable than other tree based models, such as Random Forests.
Edit intent: coherence
Original:  The main advantage of the BooST model is that it estimates the derivatives (partial effects) of very general nonlinear models , providing more interpretation about the mapping between the covariates and the dependent variable than other tree based models, such as Random Forests.
Revised:  The main advantage of the BooST model is that it estimates the derivatives (partial effects) of very general nonlinear models , providing more interpretation about the mapping between the covariates and the dependent variable than other tree-based models, such as Random Forests.
Edit intent: fluency
Original:  By using the retinal ganglion cell as a model system, we show that STNMF can detect various biophysical properties of upstream bipolar cells, including spatial receptive fields, temporal filters , and transfer nonlinearity.
Revised:  By using the retinal ganglion cell as a model system, we show that STNMF can detect various computational properties of upstream bipolar cells, including spatial receptive fields, temporal filters , and transfer nonlinearity.
Edit intent: meaning-changed
Original:  By using the retinal ganglion cell as a model system, we show that STNMF can detect various biophysical properties of upstream bipolar cells, including spatial receptive fields, temporal filters , and transfer nonlinearity.
Revised:  By using the retinal ganglion cell as a model system, we show that STNMF can detect various biophysical properties of upstream bipolar cells, including spatial receptive field, temporal filter , and transfer nonlinearity.
Edit intent: fluency
Original: In this paper, we study the  self-assembly of rectangles in a non-cooperative, 3D version of Winfree's abstract Tile Assembly Model . We prove four main results -- the first two negative and the last two positive. First, we use Catalan numbers and a restricted version of the Window Movie Lemma (Meunier, Patitz, Summers, Theyssier, Winslow and Woods, SODA 2014) to prove two new lower bounds on the minimum number of unique tile types required for the self-assembly of rectangles.
Revised: In this paper, we study the self-assembly of rectangles in a non-cooperative, 3D version of Winfree's abstract Tile Assembly Model (aTAM), restricted to temperature-1. Using Catalan numbers, planar self-assembly and a restricted version of the Window Movie Lemma (Meunier, Patitz, Summers, Theyssier, Winslow and Woods, SODA 2014) to prove two new lower bounds on the minimum number of unique tile types required for the self-assembly of rectangles.
Edit intent: meaning-changed
Original:  First, we use Catalan numbers and a restricted version of the Window Movie Lemma (Meunier, Patitz, Summers, Theyssier, Winslow and Woods, SODA 2014) to prove two new lower bounds on the minimum number of unique tile types required for the self-assembly of rectangles. We then give a general construction for the efficient self-assembly of thin rectangles. Our construction is non-cooperative and " just-barely " 3D in the sense that it places tiles at most one step into the third dimension.
Revised:  First, we use Catalan numbers and a restricted version of the Window Movie Lemma , we derive a new lower bound on the tile complexity of thin rectangles at temperature-1 in 2D. Then, we give the first known upper bound on the tile complexity of `` just-barely " 3D in the sense that it places tiles at most one step into the third dimension.
Edit intent: meaning-changed
Original:  Finally, we give a non-cooperative, just-barely 3D optimal encoding construction that self-assembles the bits of a given binary string along the perimeter of a thin rectangleof constant height .
Revised:  Our construction, which produces a unique terminal assembly, implements a just-barely 3D optimal encoding construction that self-assembles the bits of a given binary string along the perimeter of a thin rectangleof constant height .
Edit intent: meaning-changed
Original:  Finally, we give a non-cooperative, just-barely 3D optimal encoding construction that self-assembles the bits of a given binary string along the perimeter of a thin rectangleof constant height .
Revised:  Finally, we give a non-cooperative, just-barely 3D , zig-zag counter, whose base depends on the dimensions of the target rectangle, and whose digits are encoded geometrically, vertically-oriented and in binary .
Edit intent: meaning-changed
Original:  We use our methods to study the effect of a subsidized loan program on post-education attendance in Colombia, and offer new empirical evidence on the program effects for students with test scores away from the cutoff that determined program eligibility.
Revised:  We illustrate our methods with data on a subsidized loan program on post-education attendance in Colombia, and offer new empirical evidence on the program effects for students with test scores away from the cutoff that determined program eligibility.
Edit intent: clarity
Original:  We use our methods to study the effect of a subsidized loan program on post-education attendance in Colombia, and offer new empirical evidence on the program effects for students with test scores away from the cutoff that determined program eligibility.
Revised:  We use our methods to study the effect of a subsidized loan program on post-education attendance in Colombia, and offer new evidence on program effects for students with test scores away from the cutoff that determined program eligibility.
Edit intent: clarity
Original: We study periodic, deterministic aperiodic (Thue-Morse, Fibonacci, Period Doubling, Rudin-Shapiro , Cantor, generalized Cantor , Kolakoski) and random binary sequences within the framework of the Tight-Binding wire model.
Revised: We study periodic, quasi-periodic (Thue-Morse, Fibonacci, Period Doubling, Rudin-Shapiro , Cantor, generalized Cantor , Kolakoski) and random binary sequences within the framework of the Tight-Binding wire model.
Edit intent: clarity
Original: We study periodic, deterministic aperiodic (Thue-Morse, Fibonacci, Period Doubling, Rudin-Shapiro , Cantor, generalized Cantor , Kolakoski) and random binary sequences within the framework of the Tight-Binding wire model.
Revised: We study periodic, deterministic aperiodic (Thue-Morse, Fibonacci, Period Doubling, Rudin-Shapiro ), fractal ( Cantor, generalized Cantor , Kolakoski) and random binary sequences within the framework of the Tight-Binding wire model.
Edit intent: meaning-changed
Original: We study periodic, deterministic aperiodic (Thue-Morse, Fibonacci, Period Doubling, Rudin-Shapiro , Cantor, generalized Cantor , Kolakoski) and random binary sequences within the framework of the Tight-Binding wire model.
Revised: We study periodic, deterministic aperiodic (Thue-Morse, Fibonacci, Period Doubling, Rudin-Shapiro , Cantor, generalized Cantor ), Kolakoski and random binary sequences within the framework of the Tight-Binding wire model.
Edit intent: fluency
Original: We study periodic, deterministic aperiodic (Thue-Morse, Fibonacci, Period Doubling, Rudin-Shapiro , Cantor, generalized Cantor , Kolakoski) and random binary sequences within the framework of the Tight-Binding wire model.
Revised: We study periodic, deterministic aperiodic (Thue-Morse, Fibonacci, Period Doubling, Rudin-Shapiro , Cantor, generalized Cantor , Kolakoski) and random binary sequences using a tight-binding wire model, where a site is a monomer (e.g., in DNA, a base pair).
Edit intent: meaning-changed
Original:  We use B-DNA as our prototype system , where all sequences have purines  on the same strand and start with guanine.We investigate the interplay between structure, magnitude of parameters , and spectral, localization and charge transport properties.
Revised:  We use B-DNA as our prototype system , where all sequences have purines on the same strand , i.e., our prototype binary alphabet is (G,A). Our aim is to examine the influence of sequence intricacy and magnitude of parameters , and spectral, localization and charge transport properties.
Edit intent: meaning-changed
Original: We investigate the interplay between structure, magnitude of parameters , and spectral, localization and charge transport properties. To this end, we comparatively study relevant quantities such as autocorrelation functions, integrated density of states, Lyapunov exponents, transmission coefficients and current-voltage curves , using the Landauer-Buttiker formalism.
Revised: We investigate the interplay between structure, magnitude of parameters , and spectral, localization and charge transport . We study quantities such as autocorrelation functions, integrated density of states, Lyapunov exponents, transmission coefficients and current-voltage curves , using the Landauer-Buttiker formalism.
Edit intent: clarity
Original:  To this end, we comparatively study relevant quantities such as autocorrelation functions, integrated density of states, Lyapunov exponents, transmission coefficients and current-voltage curves , using the Landauer-Buttiker formalism.
Revised:  To this end, we comparatively study relevant quantities such as autocorrelation function, eigenspectra, density of states, Lyapunov exponents, transmission coefficients and current-voltage curves , using the Landauer-Buttiker formalism.
Edit intent: meaning-changed
Original:  To this end, we comparatively study relevant quantities such as autocorrelation functions, integrated density of states, Lyapunov exponents, transmission coefficients and current-voltage curves , using the Landauer-Buttiker formalism. Our results reveal that there is a correspondence between the degree of structural complexity and the presence of correlations in the sequences and the aforementioned properties.
Revised:  To this end, we comparatively study relevant quantities such as autocorrelation functions, integrated density of states, Lyapunov exponents, transmission coefficients and current-voltage curves . We show that the degree of structural complexity and the presence of correlations in the sequences and the aforementioned properties.
Edit intent: coherence
Original:  Our results reveal that there is a correspondence between the degree of structural complexity and the presence of correlations in the sequences and the aforementioned properties. Apart from periodic segments , which display enhanced transport properties , there are several cases of deterministic aperiodic sequences that can support significant currents, depending on the Fermi level of the leads.
Revised:  Our results reveal that there is a correspondence between the degree of structural complexity and the presence of correlations decisively affect the aforementioned physical properties. Periodic segments have enhanced transport properties , there are several cases of deterministic aperiodic sequences that can support significant currents, depending on the Fermi level of the leads.
Edit intent: clarity
Original:  Apart from periodic segments , which display enhanced transport properties , there are several cases of deterministic aperiodic sequences that can support significant currents, depending on the Fermi level of the leads.
Revised:  Apart from periodic segments , which display enhanced transport properties . Specifically, in homogeneous sequences transport efficiency is maximum. There are several deterministic aperiodic sequences that can support significant currents, depending on the Fermi level of the leads.
Edit intent: meaning-changed
Original:   We investigate how the seller can use the available data to estimate the matrix that captures the dependence of observable agents' consumption decisions on the prices offered to them.
Revised:  Due to the presence of network externalities, an agent's consumption decision depends not only on the price offered to her, but also on the consumption decisions of (and in turn the prices offered to) her neighbors in the underlying network. We investigate how the seller can use the available data to estimate the matrix that captures the dependence of observable agents' consumption decisions on the prices offered to them.
Edit intent: meaning-changed
Original:  We establish that by using the estimated matrix the seller can construct prices that lead to a small revenue loss relative to revenue-maximizing prices under complete information, and the optimality gap vanishes relative to the size of the network . We also illustrate that the presence of latent agents leads to significant changes in the structure of the revenue-maximizing prices .
Revised:  We establish that by using the estimated matrix the seller can construct prices that lead to a small revenue loss relative to revenue-maximizing prices under complete information, and the optimality gap vanishes relative to the size of the network .
Edit intent: coherence
Original:  An investor trades a credit default swap portfolio with a defaultable counterparty, but has incomplete information about her credit quality. By constraining the actual default intensity of the counterparty within an uncertainty interval , we derive both upper and lower bounds for the XVA process .
Revised:  An investor trades a credit default swap portfolio with a risky counterparty, and hedges credit risk by taking a position in the counterparty bond. The investor does not know the expected rate of return of the counterparty bond, but he is confident that it lies within an uncertainty interval , we derive both upper and lower bounds for the XVA process .
Edit intent: meaning-changed
Original:  By constraining the actual default intensity of the counterparty within an uncertainty interval , we derive both upper and lower bounds for the XVA process . We show that these bounds may be recovered as solutions of nonlinear ordinary differential equations.
Revised:  By constraining the actual default intensity of the counterparty within an uncertainty interval , we derive both upper and lower bounds for the XVA process of the portfolio, and show that these bounds may be recovered as solutions of nonlinear ordinary differential equations.
Edit intent: meaning-changed
Original:  The superhedging value cannot be directly obtained by plugging one of the extremes of the uncertainty interval in the valuation equation, but rather depends on the relation between the XVA replicating portfolio and the close-out value throughout the life of the transaction.
Revised:  The value of the super-replicating portfolio cannot be directly obtained by plugging one of the extremes of the uncertainty interval in the valuation equation, but rather depends on the relation between the XVA replicating portfolio and the close-out value throughout the life of the transaction.
Edit intent: clarity
Original:  The value of the super-replicating portfolio cannot be directly obtained by plugging one of the extremes of the uncertainty interval in the valuation equation, but rather depends on the relation between the XVA replicating portfolio and the close-out value throughout the life of the transaction  .
Revised:  The value of the super-replicating portfolio cannot be directly obtained by plugging one of the extremes of the uncertainty interval in the valuation equation, but rather depends on the relation between the XVA replicating portfolio and the close-out value throughout the life of the transaction . Our comparative statics analysis indicates that credit contagion has a nonlinear effect on the replication strategies and on the XVA .
Edit intent: meaning-changed
Original: We extend Convolutional Neural Networks (CNNs) on flat and regular domains (e.g. 2D images) to curved surfaces embedded in 3D Euclidean space that are discretized as irregular meshes and widely used to represent geometric data in Computer Vision and Graphics. We define surface convolution on tangent spaces of a surface domain, where the convolution has two desirable properties: 1) the distortion of surface domain signals is locally minimal when being projected to the tangent space, and 2) the translation equi-variance property holds locally, by aligning tangent spaces with the canonical parallel transport that preserves metric.
Revised: Surface meshes are widely used shape representations and capture finer geometry data than point clouds or volumetric grids, but are challenging to apply CNNs directly due to their non-Euclidean structure. We use parallel frames on surface to define PFCNNs that enable effective feature learning on surface domain signals is locally minimal when being projected to the tangent space, and 2) the translation equi-variance property holds locally, by aligning tangent spaces with the canonical parallel transport that preserves metric.
Edit intent: clarity
Original:  We define surface convolution on tangent spaces of a surface domain, where the convolution has two desirable properties: 1) the distortion of surface domain signals is locally minimal when being projected to the tangent space, and 2) the translation equi-variance property holds locally, by aligning tangent spaces with the canonical parallel transport that preserves metric. For computation, we rely on a parallel N-direction frame field on the surface that minimizes field variation and therefore is as compatible as possible to and approximates the parallel transport. On the tangent spaces equipped with parallel frames, the computation of surface convolution becomes standard routine. The frames have rotational symmetry which we disambiguate by constructing the covering space of surface induced by the parallel frames and grouping the feature maps into N sets accordingly; convolution is computed on the N branches of the cover space with respective feature maps while the kernel weights are shared. To handle irregular points of a discrete mesh while sharing kernel weights, we make the convolution semi-discrete, i.e. the convolution kernels are polynomial functions, and their convolution with discrete surface points becomes sampling and weighted summation.
Revised:  We define surface convolution on tangent spaces of a surface domain, where the convolution has two desirable properties: 1) the distortion of surface meshes by mimicking standard convolutions faithfully. In particular, the convolution semi-discrete, i.e. the convolution kernels are polynomial functions, and their convolution with discrete surface points becomes sampling and weighted summation.
Edit intent: coherence
Original:  To handle irregular points of a discrete mesh while sharing kernel weights, we make the convolution semi-discrete, i.e. the convolution kernels are polynomial functions, and their convolution with discrete surface points becomes sampling and weighted summation. Pooling and unpooling operations are computed along a mesh hierarchy built through simplification. The presented surface CNNs allow effective deep learning on meshes. We show that for tasks of classification, segmentation and non-rigid registration , surface CNNs using only raw input signals achieve superior performances than previous models using sophisticated input features  .
Revised:  To handle irregular points of a discrete mesh while sharing kernel weights, we make the convolution of PFCNN not only maps local surface patches onto flat tangent planes, but also aligns the tangent planes such that they locally form a flat Euclidean structure, thus enabling recovery of standard convolutions. The alignment is achieved by the tool of locally flat connections borrowed from discrete differential geometry, which can be efficiently encoded and computed by parallel frame fields. In addition, the lack of canonical axis on surface is handled by sampling with the frame directions. Experiments show that for tasks of classification, segmentation and non-rigid registration , surface CNNs using only raw input signals achieve superior performances than previous models using sophisticated input features .
Edit intent: meaning-changed
Original:  We show that for tasks of classification, segmentation and non-rigid registration , surface CNNs using only raw input signals achieve superior performances than previous models using sophisticated input features  .
Revised:  We show that for tasks of classification, segmentation and non-rigid registration , surface CNNs using only raw input signals achieve superior performances than previous models using sophisticated input features than state-of-the-art surface based CNNs .
Edit intent: meaning-changed
Original:  In particular, we prove a Fundamental Theorem of Asset Pricing and a Superhedging Theorem which both encompass the formulations of [ BN15  ] and [ BFH+16  ].
Revised:  In particular, we prove a Fundamental Theorem of Asset Pricing and a Superhedging Theorem , which encompass the formulations of [ BN15 ] and [ BFH+16 ].
Edit intent: clarity
Original:  In particular, we prove a Fundamental Theorem of Asset Pricing and a Superhedging Theorem which both encompass the formulations of [ BN15  ] and [ BFH+16  ].
Revised:  In particular, we prove a Fundamental Theorem of Asset Pricing and a Superhedging Theorem which both encompass the formulations of [ Bouchard, B., ] and [ BFH+16 ].
Edit intent: meaning-changed
Original:  In particular, we prove a Fundamental Theorem of Asset Pricing and a Superhedging Theorem which both encompass the formulations of [ BN15  ] and [ BFH+16  ].
Revised:  In particular, we prove a Fundamental Theorem of Asset Pricing and a Superhedging Theorem which both encompass the formulations of [ BN15 Nutz, M. (2015). Arbitrage and duality in nondominated discrete-time models. The Annals of Applied Probability, 25(2), 823-859 ] and [ BFH+16 ].
Edit intent: meaning-changed