wanyu/IteraTeR_v2 huggingface dataset:
Original:  We empirically show that the region-based attack and adversarial pruning defense are either better than or competitive with existing attacks and defenses for non-parametric methods, while being considerably more generally applicable .
Revised:  We empirically show that our defense and attack are either better than or competitive with existing attacks and defenses for non-parametric methods, while being considerably more generally applicable .
Edit intent: clarity
Original:  We empirically show that the region-based attack and adversarial pruning defense are either better than or competitive with existing attacks and defenses for non-parametric methods, while being considerably more generally applicable .
Revised:  We empirically show that the region-based attack and adversarial pruning defense are either better than or competitive with prior work on non-parametric methods, while being considerably more generally applicable .
Edit intent: clarity
Original: Relighting is an essential step in artificially transferring an object from one image into another environment.
Revised: Relighting is an essential step in realistically transferring objects from a captured image into another environment.
Edit intent: clarity
Original:  For example, a believable teleconference in Augmented Reality requires a portrait recorded in the source environment to be displayed and relit consistent with the light configuration of the destination scene .
Revised:  For example, authentic telepresence in Augmented Reality requires a portrait recorded in the source environment to be displayed and relit consistent with the light configuration of the destination scene .
Edit intent: clarity
Original:  For example, a believable teleconference in Augmented Reality requires a portrait recorded in the source environment to be displayed and relit consistent with the light configuration of the destination scene .
Revised:  For example, a believable teleconference in Augmented Reality requires faces to be displayed and relit consistent with the light configuration of the destination scene .
Edit intent: clarity
Original:  The most structured model decomposes the input image into intrinsic components according to a diffuse physics-based image formation model and augments the render to relight including non-diffuse effects .
Revised:  The most structured model decomposes the input image into intrinsic components according to a diffuse physics-based image formation model . We enable non-diffuse effects .
Edit intent: coherence
Original:  To train our models and evaluate the approach , we collected portraits of 21 subjects with various expressions and poses , each in a sequence of 32 individual light sources in a controlled light stage setup  .
Revised:  To train and evaluate our model , we collected portraits of 21 subjects with various expressions and poses , each in a sequence of 32 individual light sources in a controlled light stage setup .
Edit intent: clarity
Original:  To train our models and evaluate the approach , we collected portraits of 21 subjects with various expressions and poses , each in a sequence of 32 individual light sources in a controlled light stage setup  .
Revised:  To train our models and evaluate the approach , we collected a portrait database of 21 subjects with various expressions and poses , each in a sequence of 32 individual light sources in a controlled light stage setup .
Edit intent: clarity
Original:  To train our models and evaluate the approach , we collected portraits of 21 subjects with various expressions and poses , each in a sequence of 32 individual light sources in a controlled light stage setup  .
Revised:  To train our models and evaluate the approach , we collected portraits of 21 subjects with various expressions and poses . Each sample is captured in a controlled light stage setup .
Edit intent: clarity
Original:  To train our models and evaluate the approach , we collected portraits of 21 subjects with various expressions and poses , each in a sequence of 32 individual light sources in a controlled light stage setup  .
Revised:  To train our models and evaluate the approach , we collected portraits of 21 subjects with various expressions and poses , each in a sequence of 32 individual light sources in a controlled light stage setup with 32 individual light sources .
Edit intent: clarity
Original:  Our method leads to precise and believable relighting results in challenging illumination conditions and  poses, including when the subject is facing away from the camera.
Revised:  Our method creates precise and believable relighting results in challenging illumination conditions and poses, including when the subject is facing away from the camera.
Edit intent: clarity
Original:  Our method leads to precise and believable relighting results in challenging illumination conditions and  poses, including when the subject is facing away from the camera.
Revised:  Our method leads to precise and believable relighting results in challenging illumination conditions and challenging poses, including when the subject is facing away from the camera.
Edit intent: fluency
Original:  For multi-speaker models, the proposed model is able to preserve target speaker identity during inter-speaker prosody transfer and when drawing samples from the latent prior.
Revised:  For multi-speaker models, Capacitron is able to preserve target speaker identity during inter-speaker prosody transfer and when drawing samples from the latent prior.
Edit intent: clarity
Original:  Therefore, we address this by designing an efficient subgraph isomorphism algorithm leveraging features of GPU architecture, such as massive parallelism and multi-layer memory architecture . Existing GPU-based solutions adopt a two-step output scheme, performing the same join process twice in order to write intermediate results concurrently.
Revised:  Therefore, we address this by designing an efficient subgraph isomorphism algorithm leveraging features of GPU architecture, such as massive parallelism and memory hierarchy . Existing GPU-based solutions adopt a two-step output scheme, performing the same join process twice in order to write intermediate results concurrently.
Edit intent: clarity
Original:  In this paper, we focus on the superiority of stochastic symplectic methods applied to a linear stochastic oscillator , from the perspective of large deviations principle (LDP) . Based on the G\"artner--Ellis theorem, we first study the LDPs of the mean position and the mean velocity for both the exact solution of the stochastic oscillator and its numerical approximations.
Revised:  In this paper, we focus on the superiority of stochastic symplectic methods applied to a linear stochastic oscillator , from the perspective of large deviations principles for both exact and numerical solutions . Based on the G\"artner--Ellis theorem, we first study the LDPs of the mean position and the mean velocity for both the exact solution of the stochastic oscillator and its numerical approximations.
Edit intent: clarity
Original:  In this paper, we focus on the superiority of stochastic symplectic methods applied to a linear stochastic oscillator , from the perspective of large deviations principle (LDP) . Based on the G\"artner--Ellis theorem, we first study the LDPs of the mean position and the mean velocity for both the exact solution of the stochastic oscillator and its numerical approximations.
Revised:  In this paper, we focus on the superiority of stochastic symplectic methods applied to a linear stochastic oscillator , from the perspective of large deviations principle (LDP) . Based on the G\"artner--Ellis theorem, we first study the large deviations principles of the mean position and the mean velocity for both the exact solution of the stochastic oscillator and its numerical approximations.
Edit intent: clarity
Original:  In this paper, we focus on the superiority of stochastic symplectic methods applied to a linear stochastic oscillator , from the perspective of large deviations principle (LDP) . Based on the G\"artner--Ellis theorem, we first study the LDPs of the mean position and the mean velocity for both the exact solution of the stochastic oscillator and its numerical approximations.
Revised:  In this paper, we focus on the superiority of stochastic symplectic methods applied to a linear stochastic oscillator , from the perspective of large deviations principle (LDP) . Based on the G\"artner--Ellis theorem, we first study the LDPs of the mean position and the mean velocity for both the exact solution and its numerical approximations.
Edit intent: clarity
Original:  Then, by giving the conditions which make numerical methods have first order convergence in mean-square sense, we prove that  symplectic methods asymptotically preserve these two LDPs, in the sense that the modified rate functions of symplectic methods converge to the rate functions of exact solution .
Revised:  Then, we prove that symplectic methods asymptotically preserve these two LDPs, in the sense that the modified rate functions of symplectic methods converge to the rate functions of exact solution .
Edit intent: coherence
Original:  The aim is to develop for this problem a robust algorithm based on domain decomposition methods and operator splitting techniques, in which the numerical solution is achieved by solving sequentially reduced pressure, saturation-advection and  saturation-diffusion problems posed on the interfaces between the rocks.
Revised:  We develop for this problem a robust algorithm based on domain decomposition methods and operator splitting techniques, in which the numerical solution is achieved by solving sequentially reduced pressure, saturation-advection and saturation-diffusion problems posed on the interfaces between the rocks.
Edit intent: coherence
Original:  The aim is to develop for this problem a robust algorithm based on domain decomposition methods and operator splitting techniques, in which the numerical solution is achieved by solving sequentially reduced pressure, saturation-advection and  saturation-diffusion problems posed on the interfaces between the rocks.
Revised:  The aim is to develop for this problem two robust schemes based on domain decomposition methods and operator splitting techniques, in which the numerical solution is achieved by solving sequentially reduced pressure, saturation-advection and saturation-diffusion problems posed on the interfaces between the rocks.
Edit intent: clarity
Original:  The aim is to develop for this problem a robust algorithm based on domain decomposition methods and operator splitting techniques, in which the numerical solution is achieved by solving sequentially reduced pressure, saturation-advection and  saturation-diffusion problems posed on the interfaces between the rocks.
Revised:  The aim is to develop for this problem a robust algorithm based on domain decomposition methods and operator splitting techniques, in which the numerical solution is achieved by solving sequentially reduced pressure, saturation-advection and the saturation-diffusion problems posed on the interfaces between the rocks.
Edit intent: fluency
Original:   For the discretization, the advection problem is approximated in time, with the explicit Euler method where different time grids are employed to adapt to different time scales in the rocks , and in space with hybridized cell-centered finite volume method of first order of Godunov type.
Revised:  For the discretization, the advection problem is approximated with the explicit Euler method where different time grids are employed to adapt to different time scales in the rocks , and in space with hybridized cell-centered finite volume method of first order of Godunov type.
Edit intent: fluency
Original:   For the discretization, the advection problem is approximated in time, with the explicit Euler method where different time grids are employed to adapt to different time scales in the rocks , and in space with hybridized cell-centered finite volume method of first order of Godunov type.
Revised:  For the discretization, the advection problem is approximated in time, with the explicit Euler method in time , and in space with hybridized cell-centered finite volume method of first order of Godunov type.
Edit intent: coherence
Original:   For the discretization, the advection problem is approximated in time, with the explicit Euler method where different time grids are employed to adapt to different time scales in the rocks , and in space with hybridized cell-centered finite volume method of first order of Godunov type.
Revised:  For the discretization, the advection problem is approximated in time, with the explicit Euler method where different time grids are employed to adapt to different time scales in the rocks , and in space with the cell-centered finite volume method of first order of Godunov type.
Edit intent: clarity
Original:  That of the diffusion problem is approximated in time with the implicit Euler method and in space with a hybridized mixed finite element method as is used for the pressure problem.
Revised:  That of the diffusion problem is approximated in time with the implicit Euler method and in space with the mixed finite element method as is used for the pressure problem.
Edit intent: clarity
Original:  That of the diffusion problem is approximated in time with the implicit Euler method and in space with a hybridized mixed finite element method as is used for the pressure problem.
Revised:  That of the diffusion problem is approximated in time with the implicit Euler method and in space with a hybridized mixed finite element method , as in the pressure problem.
Edit intent: coherence
Original: With convenient access to observational data , learning individual causal effects from such data draws more attention in many influential research areas such as economics, healthcare, and education.
Revised: The convenient access to observational data , learning individual causal effects from such data draws more attention in many influential research areas such as economics, healthcare, and education.
Edit intent: fluency
Original: With convenient access to observational data , learning individual causal effects from such data draws more attention in many influential research areas such as economics, healthcare, and education.
Revised: With convenient access to observational data enables learning causal effects draws without randomized experiments, which draws increasing attention in many influential research areas such as economics, healthcare, and education.
Edit intent: clarity
Original: With convenient access to observational data , learning individual causal effects from such data draws more attention in many influential research areas such as economics, healthcare, and education.
Revised: With convenient access to observational data , learning individual causal effects from such data draws more attention in many research areas such as economics, healthcare, and education.
Edit intent: clarity
Original:  For example, we aim to study how a medicine (treatment) would affect the health condition (outcome) of a certain patient  .
Revised:  For example, we can study how a medicine (treatment) would affect the health condition (outcome) of a certain patient .
Edit intent: clarity
Original:  To validate causal inference from observational data, we need to control  the influence of confounders - the variables which causally influence both the treatment and the outcome.
Revised:  To validate causal inference from such data, we need to control the influence of confounders - the variables which causally influence both the treatment and the outcome.
Edit intent: clarity
Original:  To validate causal inference from observational data, we need to control  the influence of confounders - the variables which causally influence both the treatment and the outcome.
Revised:  To validate causal inference from observational data, we need to control the influence of variables which causally influence both the treatment and the outcome.
Edit intent: clarity
Original:  Along this line, existing work for learning individual treatment effect overwhelmingly relies on the  assumption that there are no hidden confounders .
Revised:  Existing work overwhelmingly relies on the assumption that there are no hidden confounders .
Edit intent: coherence
Original:  However, in real-world observational data, this assumption is untenable and can be unrealistic .
Revised:  However, this assumption can be untenable .
Edit intent: clarity
Original:  Some factors (e.g., socioeconomic status) are hard to measure directly and therefore become hidden confounders of observational datasets .
Revised:  Some factors (e.g., socioeconomic status) are challenging to measure and therefore become hidden confounders of observational datasets .
Edit intent: clarity
Original:  Some factors (e.g., socioeconomic status) are hard to measure directly and therefore become hidden confounders of observational datasets .
Revised:  Some factors (e.g., socioeconomic status) are hard to measure directly and therefore become hidden confounders .
Edit intent: clarity
Original:  With this fact in mind, we aim to exploit the network structure to recognize patterns of hidden confounders in the task of learning individual treatment effects from observational data.
Revised:  With this fact in mind, we aim to exploit the network structure to recognize patterns of hidden confounders for the sake of learning causal effects from observational data.
Edit intent: clarity
Original:  In this work, we propose a novel causal inference framework, the network deconfounder, which learns representations of confounders by unraveling patterns of hidden confounders from the network structure between instances of observational data . Empirically, we perform extensive experiments to validate the effectiveness of the network deconfounder on various datasets.
Revised:  In this work, we propose a novel causal inference framework, the network deconfounder, which learns representations to unravel patterns of hidden confounders from the network structure between instances of observational data . Empirically, we perform extensive experiments to validate the effectiveness of the network deconfounder on various datasets.
Edit intent: fluency
Original:  In this work, we propose a novel causal inference framework, the network deconfounder, which learns representations of confounders by unraveling patterns of hidden confounders from the network structure between instances of observational data . Empirically, we perform extensive experiments to validate the effectiveness of the network deconfounder on various datasets.
Revised:  In this work, we propose a novel causal inference framework, the network deconfounder, which learns representations of confounders by unraveling patterns of hidden confounders from the network structure . Empirically, we perform extensive experiments to validate the effectiveness of the network deconfounder on various datasets.
Edit intent: clarity
Original: The convenient access to observational data enables learning causal effects draws without randomized experiments , which draws increasing attention in many research areas such as economics, healthcare, and education.
Revised: Convenient access to observational data enables learning causal effects draws without randomized experiments , which draws increasing attention in many research areas such as economics, healthcare, and education.
Edit intent: fluency
Original: The convenient access to observational data enables learning causal effects draws without randomized experiments , which draws increasing attention in many research areas such as economics, healthcare, and education.
Revised: The convenient access to observational data enables us to learn causal effects without randomized experiments , which draws increasing attention in many research areas such as economics, healthcare, and education.
Edit intent: fluency
Original: The convenient access to observational data enables learning causal effects draws without randomized experiments , which draws increasing attention in many research areas such as economics, healthcare, and education.
Revised: The convenient access to observational data enables learning causal effects draws without randomized experiments . This research direction draws increasing attention in many research areas such as economics, healthcare, and education.
Edit intent: coherence
Original: The convenient access to observational data enables learning causal effects draws without randomized experiments , which draws increasing attention in many research areas such as economics, healthcare, and education.
Revised: The convenient access to observational data enables learning causal effects draws without randomized experiments , which draws increasing attention in research areas such as economics, healthcare, and education.
Edit intent: clarity
Original:  For example, we can study how a medicine ( treatment) would affect the health condition (  outcome) of a certain patient with electronic health records.
Revised:  For example, we can study how a medicine ( the treatment) causally affects the health condition ( outcome) of a certain patient with electronic health records.
Edit intent: clarity
Original:  For example, we can study how a medicine ( treatment) would affect the health condition (  outcome) of a certain patient with electronic health records.
Revised:  For example, we can study how a medicine ( treatment) would affect the health condition ( the outcome) of a certain patient with electronic health records.
Edit intent: fluency
Original:  For example, we can study how a medicine ( treatment) would affect the health condition (  outcome) of a certain patient with electronic health records.
Revised:  For example, we can study how a medicine ( treatment) would affect the health condition ( outcome) of a patient using existing electronic health records.
Edit intent: clarity
Original:  To validate causal inference from such data, we need to control confounding bias - the influence of variables which causally influence both the treatment and the outcome.
Revised:  To validate causal effects learned from observational data, we need to control confounding bias - the influence of variables which causally influence both the treatment and the outcome.
Edit intent: clarity
Original:  To validate causal inference from such data, we need to control confounding bias - the influence of variables which causally influence both the treatment and the outcome.
Revised:  To validate causal inference from such data, we have to control confounding bias - the influence of variables which causally influence both the treatment and the outcome.
Edit intent: fluency
Original:  To validate causal inference from such data, we need to control confounding bias - the influence of variables which causally influence both the treatment and the outcome.
Revised:  To validate causal inference from such data, we need to control confounding bias -- the influence of variables which causally influence both the treatment and the outcome.
Edit intent: fluency
Original:  Existing work  overwhelmingly relies on the unconfoundedness assumption that there are all confounderscan be measured .
Revised:  Existing work overwhelmingly relies on the unconfoundedness assumption that there do not exist unobserved confounders .
Edit intent: clarity
Original:  However, this assumption can be untenable.
Revised:  However, this assumption is untestable and can even be untenable.
Edit intent: coherence
Original:  In fact, an important fact ignored by the majority of existing work is that observational data can come with network information that can be utilized to infer hidden confounders.
Revised:  In fact, an important fact ignored by the majority of previous work is that observational data can come with network information that can be utilized to infer hidden confounders.
Edit intent: clarity