wanyu/IteraTeR_v2 huggingface dataset:
Original: Human intelligence has the remarkable ability to quickly adapt to new tasks and environments  . Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Revised: Human intelligence has the remarkable ability to quickly adapt to new tasks and environments quickly . Starting from a very young age, humans acquire new skills and learn how to solve new tasks either by imitating the behavior of others or by following provided natural language instructions.
Edit intent: clarity
Original:  To facilitate research in this direction, we propose \emph{IGLU: Interactive Grounded Language Understanding in a Collaborative Environment .
Revised:  To facilitate research in this direction, we propose \emph{IGLU: Interactive Grounded Language Understanding in a Collaborative Environment .
Edit intent: fluency
Original:  To the author' s best knowledge, this is the first algorithm that computes risk aware optimal controls that are a function of both the process noise and measurement uncertainty.
Revised:  To the authors' best knowledge, this is the first algorithm that computes risk aware optimal controls that are a function of both the process noise and measurement uncertainty.
Edit intent: fluency
Original: Domain generalization (DG) aims to learn  from multiple known source domains a model that can generalize well to unknown target domains.
Revised: Domain generalization (DG) aims to learn from multiple known source domains for unknown target domains.
Edit intent: clarity
Original:  However, tremendous data is distributed across lots of places  nowadays that can not be shared due to privacy policies , especially in some crucial areas like finance and medical care.
Revised:  However, tremendous data is distributed across lots of places nowadays that can not be shared due to privacy protection , especially in some crucial areas like finance and medical care.
Edit intent: clarity
Original:  A dilemma is thus raised between real-world data privacy protection and simultaneous multi-source semantic learning with the shared data .
Revised:  A dilemma is thus raised between real-world applications .
Edit intent: clarity
Original:  In this paper, we investigate a separated domain generalization task with separated source datasets that can only be used locally, which is vital for real-world privacy protection .
Revised:  In this paper, we introduce a challenging setting of training a generalizable model by using distributed source datasets without directly accessing them .
Edit intent: clarity
Original:  To address semantic dislocation caused by domain shift , we further design cross-layer semantic calibration  with an attention mechanism to align each semantic level and enhance domain invariance.
Revised:  To address semantic dislocation caused by domain shift , we further introduce cross-layer semantic calibration with an attention mechanism to align each semantic level and enhance domain invariance.
Edit intent: clarity
Original:  Extensive experiments show the significant performance of our method in addressing this challenging task , which is even comparable to the previous DG methods with shared data .
Revised:  Extensive experiments show the significant performance of our method in tackling this challenging setting , which is even comparable to the previous DG methods with shared data .
Edit intent: clarity
Original:  Extensive experiments show the significant performance of our method in addressing this challenging task , which is even comparable to the previous DG methods with shared data .
Revised:  Extensive experiments show the significant performance of our method in addressing this challenging task , which is even on par or superior to the performance of the state-of-the-art DG approaches in the standard DG setting .
Edit intent: clarity
Original:  The next-best-view problem attempts to find the optimal viewpoint with the greatest information gain for the underlying task.
Revised:  The next-best-view problem attempts to find the optimal viewpoint with the greatest information gain for the underlying task.
Edit intent: fluency
Original:  We make the code publicly available for the scientific community and for reproducibility under URL https link .
Revised:  We make the code publicly available for the scientific community and for reproducibility .
Edit intent: coherence
Original: This paper presents a novel open-domain dialogue generation framework emphasizing the differentiation of speakers in multi-turn conversations.
Revised: This paper presents a novel open-domain dialogue generation model emphasizing the differentiation of speakers in multi-turn conversations.
Edit intent: clarity
Original:  Given that, we propose a speaker-aware framework, named Parallel Hierarchical Attentive Encoder-Decoder (PHAED) , that aims to model each utterance with the awareness of its speaker and contextual associations with the same speaker's previous messages.
Revised:  Given that, we propose a speaker-aware Parallel Hierarchical Attentive Encoder-Decoder (PHAED) , that aims to model each utterance with the awareness of its speaker and contextual associations with the same speaker's previous messages.
Edit intent: clarity
Original:  Given that, we propose a speaker-aware framework, named Parallel Hierarchical Attentive Encoder-Decoder (PHAED) , that aims to model each utterance with the awareness of its speaker and contextual associations with the same speaker's previous messages.
Revised:  Given that, we propose a speaker-aware framework, named Parallel Hierarchical Attentive Encoder-Decoder (PHAED) model that aims to model each utterance with the awareness of its speaker and contextual associations with the same speaker's previous messages.
Edit intent: clarity
Original:  Despite the excellent theoretical properties, the model so far can only achieve comparable performance to conventional networks.
Revised:  Despite excellent theoretical properties, the model so far can only achieve comparable performance to conventional networks.
Edit intent: fluency
Original:  In this paper, we significantly boost the certified robustness of \ell_\infty-distance nets through a careful analysis of its training process.
Revised:  In this paper, we significantly boost the certified robustness of \ell_\infty-distance nets through a careful analysis of the training process.
Edit intent: clarity
Original:  Given these findings, we propose a simple approach to address the issues above by using a novel objective function that combines a scaled cross-entropy loss with clipped hinge loss.
Revised:  Given these findings, we propose a simple approach to address the above issue by designing a novel objective function that combines a scaled cross-entropy loss with clipped hinge loss.
Edit intent: clarity
Original:  Given these findings, we propose a simple approach to address the issues above by using a novel objective function that combines a scaled cross-entropy loss with clipped hinge loss.
Revised:  Given these findings, we propose a simple approach to address the issues above by using a novel objective function that combines scaled cross-entropy loss with clipped hinge loss.
Edit intent: fluency
Original:  Our experiments show that using the proposed training strategy, the certified accuracy of \ell_\infty-distance net can be dramatically improved from 33.30\% to 40.06\% on CIFAR-10 (\epsilon=8/255), meanwhile significantly outperforming other approaches in this area.
Revised:  Experiments show that using the proposed training strategy, the certified accuracy of \ell_\infty-distance net can be dramatically improved from 33.30\% to 40.06\% on CIFAR-10 (\epsilon=8/255), meanwhile significantly outperforming other approaches in this area.
Edit intent: clarity
Original:  Such a result clearly demonstrates the effectiveness and potential of \ell_\infty-distance net for certified robustness. 
Revised:  Our results clearly demonstrate the effectiveness and potential of \ell_\infty-distance net for certified robustness. 
Edit intent: clarity
Original:  Remarkably, the resultant nonlinearities reliably produce soft XOR functions, consistent with recent experimental observations about interactions between inputs in human cortical neurons.
Revised:  Remarkably, the resultant nonlinearities often produce soft XOR functions, consistent with recent experimental observations about interactions between inputs in human cortical neurons.
Edit intent: clarity
Original:  The key idea is to fuse object-centric  spatio-temporal representations throughout multiple transformer layers .
Revised:  The key idea is to fuse object-centric spatio-temporal representations throughout the network .
Edit intent: clarity
Original:  In the appearance stream, an `` Object-Region Attention '' element applies self-attention over the patches and object regions.
Revised:  In the appearance stream, an " Object-Region Attention '' element applies self-attention over the patches and object regions.
Edit intent: fluency
Original:  In the appearance stream, an `` Object-Region Attention '' element applies self-attention over the patches and object regions.
Revised:  In the appearance stream, an `` Object-Region Attention " module applies self-attention over the patches and object regions.
Edit intent: clarity
Original:  We further model object dynamics via a separate `` Object-Dynamics Module '' , which captures trajectory interactions, and show how to integrate the two streams.
Revised:  We further model object dynamics via a separate " Object-Dynamics Module '' , which captures trajectory interactions, and show how to integrate the two streams.
Edit intent: fluency
Original:  We further model object dynamics via a separate `` Object-Dynamics Module '' , which captures trajectory interactions, and show how to integrate the two streams.
Revised:  We further model object dynamics via a separate `` Object-Dynamics Module " , which captures trajectory interactions, and show how to integrate the two streams.
Edit intent: fluency
Original:  We evaluate our model on standard and compositional action recognition on Something-Something V2, standard action recognition on Epic-Kitchen100 and Diving48, and spatio-temporal action detection on AVA .
Revised:  We evaluate our model on standard and compositional action recognition on Something-Something V2, standard action recognition on Epic-Kitchen100 and Epic-Kitchen100 .
Edit intent: coherence
Original:  We show strong improvement in performance across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a transformer architecture.
Revised:  We show strong performance improvement across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a transformer architecture.
Edit intent: clarity
Original:  However, conventional RL approaches learn control policies through trial-and-error interactions with the environment and therefore may cause disastrous consequences such as collisions when testing in real traffic.
Revised:  However, conventional RL approaches learn control policies through trial-and-error interactions with the environment and therefore may cause disastrous consequences such as collisions when testing in real-world traffic.
Edit intent: clarity
Original:  Experimental results in highway and parking traffic scenarios show that our approach outperforms the conventional RL method, as well as the state-of-the-art offline RL algorithms.
Revised:  Experimental results in highway and parking traffic scenarios show that our approach outperforms the conventional RL method, as well as state-of-the-art offline RL algorithms.
Edit intent: fluency
Original: Though image-level weakly supervised semantic segmentation (WSSS) has achieved great progress with Class Activation Map (CAM ) as the cornerstone, the large supervision gap between classification and segmentation still hampers the model to generate more complete and precise pseudo masks for segmentation.
Revised: Though image-level weakly supervised semantic segmentation (WSSS) has achieved great progress with Class Activation Maps (CAMs ) as the cornerstone, the large supervision gap between classification and segmentation still hampers the model to generate more complete and precise pseudo masks for segmentation.
Edit intent: clarity
Original:  Our method can be seamlessly incorporated into existing WSSS models without any changes to the base network and does not incur any extra inference burden.
Revised:  Our method can be seamlessly incorporated into existing WSSS models without any changes to the base networks and does not incur any extra inference burden.
Edit intent: fluency
Original:  Experiments on standard benchmark show that our method consistently improves two strong baselines by large margins, demonstrating the effectiveness of our method .
Revised:  Extensive experiments manifest that our method consistently improves two strong baselines by large margins, demonstrating the effectiveness of our method .
Edit intent: clarity
Original:  Experiments on standard benchmark show that our method consistently improves two strong baselines by large margins, demonstrating the effectiveness of our method .
Revised:  Experiments on standard benchmark show that our method consistently improves two strong baselines by large margins, demonstrating the effectiveness .
Edit intent: clarity
Original:  With the maturity of the Brain-Machine Interface, we build an efficient and effective communication system between human beings and search engines based on electroencephalogram ~ (EEG) signals, called Brain-Machine Search Interface(BMSI) system.
Revised:  With the maturity of the Brain-Machine Interface, we build an efficient and effective communication system between human beings and search engines based on electroencephalogram (EEG) signals, called Brain-Machine Search Interface(BMSI) system.
Edit intent: fluency
Original: In this paper we propose a variant of New Q-Newton's method Backtracking (which is relevant to Levenberg-Marquardt algorithm, but with the argumentation chosen by New Q-Newton's method idea for good theoretical guarantee, and with Backtracking line search added) for to use specifically with systems of m equationsin m variables.
Revised: In this paper we apply the ideas of New Q-Newton's method Backtracking (which is relevant to Levenberg-Marquardt algorithm, but with the argumentation chosen by New Q-Newton's method idea for good theoretical guarantee, and with Backtracking line search added) for to use specifically with systems of m equationsin m variables.
Edit intent: clarity
Original:  If A is a square matrix, we denote by minsp(A)=\min \{ | \lambda | : \lambda is an eigenvalue of A\}.
Revised:  If A is a square matrix, we denote by minsp(A)=\min \{ | | : \lambda is an eigenvalue of A\}.
Edit intent: fluency
Original:  Also, we denote by A^{\intercal the transpose of A. Given F:R^m\rightarrow R^m a C^1 function, we denote by H(x)=JF(x) the Jacobian of F, and f(x)=||} F (x) ||^2 . If x\in \mathbb{R^m is such that F(x)}%DIFDELCMD < \not%%%  = 0, we define various : \delta (x)be the first element \delta _j in \{\delta _0, \delta _1\ x)+\delta _j ||F(x)|| ^{\tau )\geq \min \{ ||F(x)||^{\tau} ,1\};
Revised:  Also, we denote by A^{\intercal the transpose of A. Given F:R^m\rightarrow R^m a C^1 function, we denote by H(x)=JF(x) the Jacobian of F, and f(x)=||} F ||^2 . If x\in \mathbb{R^m is such that F(x)}%DIFDELCMD < \not%%% = 0, we define various : \delta (x)be the first element \delta _j in \{\delta _0, \delta _1\ x)+\delta _j ||F(x)|| ^{\tau )\geq \min \{ ||F(x)||^{\tau} ,1\};
Edit intent: coherence
Original:  Also, we denote by A^{\intercal the transpose of A. Given F:R^m\rightarrow R^m a C^1 function, we denote by H(x)=JF(x) the Jacobian of F, and f(x)=||} F (x) ||^2 . If x\in \mathbb{R^m is such that F(x)}%DIFDELCMD < \not%%%  = 0, we define various : \delta (x)be the first element \delta _j in \{\delta _0, \delta _1\ x)+\delta _j ||F(x)|| ^{\tau )\geq \min \{ ||F(x)||^{\tau} ,1\};
Revised:  Also, we denote by A^{\intercal the transpose of A. Given F:R^m\rightarrow R^m a C^1 function, we denote by H(x)=JF(x) the Jacobian of F, and f(x)=||} F (x) ||^2 . If x\in \mathbb{R^m is such that F(x)}%DIFDELCMD < \not%%% , where F = 0, we define various : \delta (x)be the first element \delta _j in \{\delta _0, \delta _1\ x)+\delta _j ||F(x)|| ^{\tau )\geq \min \{ ||F(x)||^{\tau} ,1\};
Edit intent: coherence
Original:  Also, we denote by A^{\intercal the transpose of A. Given F:R^m\rightarrow R^m a C^1 function, we denote by H(x)=JF(x) the Jacobian of F, and f(x)=||} F (x) ||^2 . If x\in \mathbb{R^m is such that F(x)}%DIFDELCMD < \not%%%  = 0, we define various : \delta (x)be the first element \delta _j in \{\delta _0, \delta _1\ x)+\delta _j ||F(x)|| ^{\tau )\geq \min \{ ||F(x)||^{\tau} ,1\};
Revised:  Also, we denote by A^{\intercal the transpose of A. Given F:R^m\rightarrow R^m a C^1 function, we denote by H(x)=JF(x) the Jacobian of F, and f(x)=||} F (x) ||^2 . If x\in \mathbb{R^m is such that F(x)}%DIFDELCMD < \not%%% = 0, we define various : \delta (x)be the first element \delta _j in \{\delta _0, \delta _1\ x)+\delta ||F(x)|| ^{\tau )\geq \min \{ ||F(x)||^{\tau} ,1\};
Edit intent: coherence
Original:  Also, we denote by A^{\intercal the transpose of A. Given F:R^m\rightarrow R^m a C^1 function, we denote by H(x)=JF(x) the Jacobian of F, and f(x)=||} F (x) ||^2 . If x\in \mathbb{R^m is such that F(x)}%DIFDELCMD < \not%%%  = 0, we define various : \delta (x)be the first element \delta _j in \{\delta _0, \delta _1\ x)+\delta _j ||F(x)|| ^{\tau )\geq \min \{ ||F(x)||^{\tau} ,1\};
Revised:  Also, we denote by A^{\intercal the transpose of A. Given F:R^m\rightarrow R^m a C^1 function, we denote by H(x)=JF(x) the Jacobian of F, and f(x)=||} F (x) ||^2 . If x\in \mathbb{R^m is such that F(x)}%DIFDELCMD < \not%%% = 0, we define various : \delta (x)be the first element \delta _j in \{\delta _0, \delta _1\ x)+\delta _j ||F(x)|| )\geq \min \{ ||F(x)||^{\tau} ,1\};
Edit intent: coherence
Original:  A}  instead of \nabla ^2f} (x) =H(x)^{\intercal +\delta (x) || F (x)||^{\tau} Id;
Revised:  A} instead of \nabla ^2f} (x) =H(x)^{\intercal +\delta || F (x)||^{\tau} Id;
Edit intent: fluency
Original:  A}  instead of \nabla ^2f} (x) =H(x)^{\intercal +\delta (x) || F (x)||^{\tau} Id;
Revised:  A} instead of \nabla ^2f} (x) =H(x)^{\intercal +\delta (x) || \nabla f (x)||^{\tau} Id;
Edit intent: clarity
Original:  \%\textcolor{red Firstly, the boundary of initial proposals in MIL is extended to different positions according to well-designed sequential order.
Revised:  Firstly, the boundary of initial proposals in MIL is extended to different positions according to well-designed sequential order.
Edit intent: coherence
Original:  We also describe the learning curve and summarize experiences from platform adopters.
Revised:  We also describe the learning curve and summarize experiences of platform adopters.
Edit intent: fluency
Original:  To that end we make the following contributions: (1) we propose an attention-based pooling mechanism to aggregate visual speech representations;
Revised:  To this end, we make the following contributions: (1) we propose an attention-based pooling mechanism to aggregate visual speech representations;
Edit intent: coherence
Original:  (3) we propose a training pipeline that balances the lip reading performance with other key factors such as data and compute efficiency .
Revised:  (3) we propose a training pipeline that balances the lip reading network .
Edit intent: clarity
Original:  Such works are fundamentally based on a (implicit) %DIFDELCMD < {\em %DIFDELCMD < %%% volumetric representation of occupancy, allowing them to model diverse scene structure including translucent objects and atmospheric obscurants.
Revised:  Such works are fundamentally based on a (implicit) %DIFDELCMD < {\em %DIFDELCMD < %%% volumetric representation of occupancy, allowing them to model diverse scene structure including translucent objects and atmospheric obscurants.
Edit intent: fluency
Original:  But because the vast majority of real-world scenes are composed of well-defined surfaces, we introduce a %DIFDELCMD < {\em %%% surface analog of such implicit models called Neural Reflectance Surfaces (NeRS).
Revised:  But because the vast majority of real-world scenes are composed of well-defined surfaces, we introduce a %DIFDELCMD < {\em %%% surface analog of such implicit models called Neural Reflectance Surfaces (NeRS).
Edit intent: fluency
Original:  Furthermore , we demonstrate that  the HJ safety value can be learned directly on vision context , the highest-dimensional problem studied via the method  to-date.
Revised:  Furthermore , we demonstrate that the HJ safety value can be learned directly on vision context -- the highest-dimensional problem studied via the method to-date.
Edit intent: fluency
Original:  Furthermore , we demonstrate that  the HJ safety value can be learned directly on vision context , the highest-dimensional problem studied via the method  to-date.
Revised:  Furthermore , we demonstrate that the HJ safety value can be learned directly on vision context , the highest-dimensional problem studied via the method , to-date.
Edit intent: fluency
Original:  We show the expressive power of our library in optimizing |Haskell | let expressions, expressing several |Haskell | refactorings and solving several language processing tasks of the LDTA Tool Challenge.
Revised:  We show the expressive power of our library in optimizing Haskell let expressions, expressing several |Haskell | refactorings and solving several language processing tasks of the LDTA Tool Challenge.
Edit intent: fluency
Original:  We show the expressive power of our library in optimizing |Haskell | let expressions, expressing several |Haskell | refactorings and solving several language processing tasks of the LDTA Tool Challenge.
Revised:  We show the expressive power of our library in optimizing |Haskell | let expressions, expressing several Haskell refactorings and solving several language processing tasks of the LDTA Tool Challenge.
Edit intent: fluency
Original:  In this paper, we propose a novel de-biasing approach that explicitly generates additional images using texture representations of oppositely labeled images to enlarge the training dataset and mitigate the effect of biases when training a classifier.
Revised:  In this paper, we propose a novel debiasing approach that explicitly generates additional images using texture representations of oppositely labeled images to enlarge the training dataset and mitigate the effect of biases when training a classifier.
Edit intent: fluency
Original:  In this paper, we propose a novel de-biasing approach that explicitly generates additional images using texture representations of oppositely labeled images to enlarge the training dataset and mitigate the effect of biases when training a classifier.
Revised:  In this paper, we propose a novel de-biasing approach that explicitly generates additional images using texture representations of different labeled images to enlarge the training dataset and mitigate the effect of biases when training a classifier.
Edit intent: clarity
Original:  In this paper, we propose a novel de-biasing approach that explicitly generates additional images using texture representations of oppositely labeled images to enlarge the training dataset and mitigate the effect of biases when training a classifier.
Revised:  In this paper, we propose a novel de-biasing approach that explicitly generates additional images using texture representations of oppositely labeled images to enlarge the training dataset and mitigate the bias effects when training a classifier.
Edit intent: clarity
Original:  Every new generated image contains similar spatial information from a source image while transferring textures from a target image of opposite label.
Revised:  Every new generated image contains similar content information from a source image while transferring textures from a target image of opposite label.
Edit intent: clarity