wanyu/IteraTeR_v2 huggingface dataset:
Original:  While we have restricted our attention to CNN classifiers,  GA hyperparameter optimization  can be applied within other machine learning settings , including alternative architectures for signal classification, parameter inference, or other tasks .
Revised:  While we have restricted our attention to CNN classifiers, our GA hyperparameter optimization can be applied within other machine learning settings , including alternative architectures for signal classification, parameter inference, or other tasks .
Edit intent: clarity
Original:  While we have restricted our attention to CNN classifiers,  GA hyperparameter optimization  can be applied within other machine learning settings , including alternative architectures for signal classification, parameter inference, or other tasks .
Revised:  While we have restricted our attention to CNN classifiers, GA hyperparameter optimization strategy can be applied within other machine learning settings , including alternative architectures for signal classification, parameter inference, or other tasks .
Edit intent: clarity
Original:  While we have restricted our attention to CNN classifiers,  GA hyperparameter optimization  can be applied within other machine learning settings , including alternative architectures for signal classification, parameter inference, or other tasks .
Revised:  While we have restricted our attention to CNN classifiers, GA hyperparameter optimization can be applied within other machine learning settings .
Edit intent: clarity
Original:  Our method is exact in a certain sense and we also show how our algorithm can be adapted for a Bayesian estimation of parameters and for the estimation of a past state value based on observations up to a future time.
Revised:  We also show how our algorithm can be adapted for a Bayesian estimation of parameters and for the estimation of a past state value based on observations up to a future time.
Edit intent: coherence
Original:  To this end  specifically, care was taken in the second annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring  scheme.
Revised:  To this end , specifically, care was taken in the second annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring scheme.
Edit intent: fluency
Original:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the  number of items selected from each group to a given number.
Revised:  In this work , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the number of items selected from each group to a given number.
Edit intent: clarity
Original:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the  number of items selected from each group to a given number.
Revised:  In this paper , we consider the fairness constraints , which takes into account the group membership of data items and limits the number of items selected from each group to a given number.
Edit intent: coherence
Original:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the  number of items selected from each group to a given number.
Revised:  In this paper , we consider the problem with additional , which takes into account the group membership of data items and limits the number of items selected from each group to a given number.
Edit intent: clarity
Original:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the  number of items selected from each group to a given number.
Revised:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the number of items from each group to a given number.
Edit intent: clarity
Original:  We propose efficient algorithms for this fairness-aware variant of the streaming submodular maximization problem.
Revised:  We propose efficient algorithms for the fairness-aware variant of the streaming submodular maximization problem.
Edit intent: fluency
Original:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first give a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Edit intent: clarity
Original:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Edit intent: clarity
Original:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . Moreover, we give a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Edit intent: clarity
Original:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same approximation ratio of (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Edit intent: clarity
Original:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Edit intent: clarity
Original:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer sizes and post-processing time is permitted\emph{ \emph{ .
Edit intent: fluency
Original:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (1{2}-\varepsilon) -approximation algorithm that requires O(1{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ and\emph{ .
Edit intent: coherence
Original: Our proposal of an Open Science definition as a political and legal framework where research outputs are shared and disseminated in order to be rendered visible, accessible, reusable is developed, standing over the concepts enhanced by the Budapest Open Science Initiative (BOAI), and by the Free/Open Source Software (FOSS) and Open data movements.
Revised: It is widely recognised nowadays that there is no single, accepted, unified definition of Open Science, which motivates our proposal of an Open Science definition as a political and legal framework where research outputs are shared and disseminated in order to be rendered visible, accessible, reusable is developed, standing over the concepts enhanced by the Budapest Open Science Initiative (BOAI), and by the Free/Open Source Software (FOSS) and Open data movements.
Edit intent: coherence
Original:  We elaborate this proposal through a detailed analysis of some selected EC policies , laws and the role of research evaluation practices  .
Revised:  We elaborate this proposal through a detailed analysis of some selected EC policies and laws as well as of the function of research evaluation practices .
Edit intent: clarity
Original:  The key to our approach is to explicitly integrate the principle of multi-view geometry to obtain the internal representations from observed 2D views, guaranteeing the learned implicit representations meaningful and multi-view consistent.
Revised:  The key to our approach is to explicitly integrate the principle of multi-view geometry to obtain the internal representations from observed 2D views, such that the learned implicit representations meaningful and multi-view consistent.
Edit intent: clarity
Original:  The key to our approach is to explicitly integrate the principle of multi-view geometry to obtain the internal representations from observed 2D views, guaranteeing the learned implicit representations meaningful and multi-view consistent.
Revised:  The key to our approach is to explicitly integrate the principle of multi-view geometry to obtain the internal representations from observed 2D views, guaranteeing the learned implicit representations empirically remain multi-view consistent.
Edit intent: clarity
Original:  In addition, we introduce an effective neural module to learn general features for each pixel in 2D images, allowing the constructed internal 3D representations to be remarkably general as well.
Revised:  In addition, we introduce an effective neural module to learn general features for each pixel in 2D images, allowing the constructed internal 3D representations to be general as well.
Edit intent: clarity
Original: We present a simple yet powerful implicit neural function that can represent and render arbitrarily complex 3D scenes in a single network only from 2D observations.
Revised: We present a simple yet powerful neural network that implicitly represents and renders 3D scenes in a single network only from 2D observations.
Edit intent: clarity
Original: We present a simple yet powerful implicit neural function that can represent and render arbitrarily complex 3D scenes in a single network only from 2D observations.
Revised: We present a simple yet powerful implicit neural function that can represent and render arbitrarily complex 3D objects and scenes only from 2D observations.
Edit intent: clarity
Original:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Revised:  The network models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Edit intent: clarity
Original:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Revised:  The function models 3D geometries as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Edit intent: clarity
Original:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Revised:  The function models 3D scenes as a general radiance field, which takes a set of 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Edit intent: clarity
Original:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Revised:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Edit intent: fluency
Original:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Revised:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the 3D space, and then renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Edit intent: clarity
Original:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Revised:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of that point viewed from an arbitrary angle .
Edit intent: clarity
Original:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary angle .
Revised:  The function models 3D scenes as a general radiance field, which takes a set of posed 2D images with camera poses and intrinsics as input, constructs an internal representation for each 3D point of the scene, and renders the corresponding appearance and geometry of any 3D point viewing from an arbitrary position .
Edit intent: clarity
Original:  In addition, we introduce an effective neural module to learn general features for each pixel in 2D images , allowing the constructed internal 3D representations to be general as well . Extensive experiments demonstrate the superiority of our approach .
Revised:  In addition, we introduce an effective neural module to learn general features for each pixel in 2D images and to then project these features to 3D representations to be general as well . Extensive experiments demonstrate the superiority of our approach .
Edit intent: clarity
Original:  In addition, we introduce an effective neural module to learn general features for each pixel in 2D images , allowing the constructed internal 3D representations to be general as well . Extensive experiments demonstrate the superiority of our approach .
Revised:  In addition, we introduce an effective neural module to learn general features for each pixel in 2D images , allowing the constructed internal 3D representations to be general as well . Extensive experiments demonstrate that our method can generate high-quality and realistic novel views for novel objects, unseen categories and challenging real-world scenes .
Edit intent: clarity
Original:  The EMAC  formulation has proved to be a useful formulation because it conserves energy, momentum and angular momentum even when the divergence constraint is only weakly enforced.
Revised:  The EMAC (energy, momentum, and angular momentum conserving) formulation has proved to be a useful formulation because it conserves energy, momentum and angular momentum even when the divergence constraint is only weakly enforced.
Edit intent: clarity
Original:  We show that EMAC-Reg, when equipped with a finite element spatial discretization , is well-posed , conserves all the important quantities that EMAC does, and is optimally accurate.
Revised:  We show that EMAC-Reg, when semi-discretized with a finite element spatial discretization , is well-posed , conserves all the important quantities that EMAC does, and is optimally accurate.
Edit intent: clarity
Original:  We show that EMAC-Reg, when equipped with a finite element spatial discretization , is well-posed , conserves all the important quantities that EMAC does, and is optimally accurate.
Revised:  We show that EMAC-Reg, when equipped with a finite element spatial discretization is well-posed , conserves all the important quantities that EMAC does, and is optimally accurate.
Edit intent: fluency
Original:  We show that EMAC-Reg, when equipped with a finite element spatial discretization , is well-posed , conserves all the important quantities that EMAC does, and is optimally accurate.
Revised:  We show that EMAC-Reg, when equipped with a finite element spatial discretization , is well-posed and optimally accurate.
Edit intent: clarity
Original:  Previously, to our knowledge, this was only achieved by resorting to genericity assumptions or randomization techniques, while the best known complexity bound with a general deterministic algorithm was obtained by Keller-Gehrig in 1985 and involves logarithmic factors.
Revised:  Previously, this was only achieved by resorting to genericity assumptions or randomization techniques, while the best known complexity bound with a general deterministic algorithm was obtained by Keller-Gehrig in 1985 and involves logarithmic factors.
Edit intent: clarity
Original:  In this paper, we first present a data-driven Gaussian process based dynamic model for SLM process and then design a model predictive control to regulate the melt pool size and temperature . Physical and process constraints are considered in the controller design.
Revised:  In this paper, we first present a data-driven Gaussian process based dynamic model for SLM process and then design a model predictive control to regulate the melt pool size . Physical and process constraints are considered in the controller design.
Edit intent: coherence
Original:  The comparison results with other control design are also presented to demonstrate the efficacy of the control design.
Revised:  The comparison results with other control design demonstrate the efficacy of the control design.
Edit intent: clarity
Original:  Building upon this underlying graph, we introduce a mixing GNN module, which is responsible for two tasks: i) factorizing the team state-action value function into individual per-agent observation-action value functions, and ii) explicit credit assignment to each agent in terms of fractions of the global team reward.
Revised:  Building upon this underlying graph, we introduce a mixing GNN module, which is responsible for i) factorizing the team state-action value function into individual per-agent observation-action value functions, and ii) explicit credit assignment to each agent in terms of fractions of the global team reward.
Edit intent: clarity
Original: We investigate learning collections of languages from texts by an inductive inference machine with access to the current datum and its memory in form of states.
Revised: We investigate learning collections of languages from texts by an inductive inference machine with access to the current datum and a bounded memory in form of states.
Edit intent: clarity
Original:  The bounded memory states (BMS) learner is considered successful in case it eventually settles on a correct hypothesis while exploiting only finitely many different states.
Revised:  Such a bounded memory states (BMS) learner is considered successful in case it eventually settles on a correct hypothesis while exploiting only finitely many different states.
Edit intent: clarity
Original:  We give the complete map of all pairwise relations for an established collection of learning success restrictions .
Revised:  We give the complete map of all pairwise relations for an established collection of criteria of successfull learning .
Edit intent: clarity