wanyu/IteraTeR_v2 huggingface dataset:
Original:  The solution of this differential equation is then used to update the evolution of the Lie-Poisson dynamics by means of the exponential map.
Revised:  The solution of this differential equation updates the evolution of the Lie-Poisson dynamics by means of the exponential map.
Edit intent: clarity
Original:  The constructed numerical method conserves Casimirs invariants exactly, which is important for long time integration.
Revised:  The constructed numerical method conserves Casimir-invariants exactly, which is important for long time integration.
Edit intent: fluency
Original:  In this work, we give a simple 1/e - selectable greedy single item OCRS, and then proceed to show that it is optimal .
Revised:  In this work, we give simple 1/e - selectable greedy single item OCRS, and then proceed to show that it is optimal .
Edit intent: fluency
Original:  In this work, we propose a simple and effective face recognition solution ( QMagFace ) that combines a quality-aware comparison score with a recognition model based on a magnitude-aware angular margin loss.
Revised:  In this work, we propose a simple and effective face recognition solution ( QMag-Face ) that combines a quality-aware comparison score with a recognition model based on a magnitude-aware angular margin loss.
Edit intent: clarity
Original:  The code for QMagFace is publicly available .
Revised:  The code for QMagFace is publicly available 
Edit intent: fluency
Original:  With a careful combination of the Fourier factorization, a shared kernel integral operator across all layers, the Markov property, and residual connections, F-FNOs achieve a six-fold reduction in error on the most turbulent setting of the Navier- Stokes benchmark dataset.
Revised:  With a careful combination of the Fourier factorization, a shared kernel integral operator across all layers, the Markov property, and residual connections, F-FNOs achieve a six-fold reduction in error on the most turbulent setting of the Navier-Stokes benchmark dataset.
Edit intent: fluency
Original:  In this paper, the theory of CE is introduced and the thermodynamic interpretation of CE is presented with N-particle correlated system in equilibrium states.
Revised:  In this paper, the theory of CE is introduced and the thermodynamic interpretation of CE is presented with N-particle correlated systems in equilibrium states.
Edit intent: fluency
Original: Experience of live video streaming can be improved if future bandwidth can be predicted more accurately at the video uploader side.
Revised: Experience of live video streaming can be improved if future bandwidth can be predicted more accurately on the video uploader side.
Edit intent: fluency
Original:  Thus follows a natural question, how to make predictions both easily and precisely in real networks ? Researchers have developed many prediction algorithms in the literature, from where a simple algorithm comes out, Arithmetic Mean (AM). Based on that, we are purposing a new method called Empirical Conditional Mean (ECM), hoping to utilize more information in the past data to get a more accurate prediction without loss of practicality.
Revised:  Thus follows a natural question, how to make predictions both easily and precisely in real networks . Researchers have developed many prediction algorithms in the literature, from where a simple algorithm comes out, Arithmetic Mean (AM). Based on that, we are purposing a new method called Empirical Conditional Mean (ECM), hoping to utilize more information in the past data to get a more accurate prediction without loss of practicality.
Edit intent: fluency
Original:  Our experiments found that when the frequency of measuring the network throughput is low, the ECM algorithm performs better than the commonly used AM one, which means ECM can send more data out while not having  higher loss rate.
Revised:  Our experiments found that when the frequency of measuring the network throughput is low, the ECM algorithm performs better than the commonly used AM one, which means ECM can send more data out while not having a higher loss rate.
Edit intent: fluency
Original:  Hence ECM can be helpful for those who especially have relatively limited networks to reach a more considerable a balance between frame loss rate and video quality.
Revised:  Hence ECM can be helpful for those who especially have relatively limited networks to reach a more considerable balance between frame loss rate and video quality.
Edit intent: clarity
Original: Acronym disambiguation means finding the correct meaning of an ambiguous acronym  in a given sentence from the dictionary , which is one of the key points for scientific document understanding (SDU@AAAI-22).
Revised: Acronym disambiguation means finding the correct meaning of an ambiguous acronym in a given sentence , which is one of the key points for scientific document understanding (SDU@AAAI-22).
Edit intent: coherence
Original:  Such representations from pre-trained MLMs are not ideal for the acronym disambiguation from the given dictionary .
Revised:  Such representations from pre-trained MLMs are not ideal for the acronym disambiguation task .
Edit intent: clarity
Original:  Specifically, we design a novel continual contrastive pre-training method that enhances the pre-trained model's generalization ability by learning the isotropic and discriminative distribution of the acronym sentence representations .
Revised:  Specifically, we design a continual contrastive pre-training method that enhances the pre-trained model's generalization ability by learning the isotropic and discriminative distribution of the acronym sentence representations .
Edit intent: clarity
Original:  Specifically, we design a novel continual contrastive pre-training method that enhances the pre-trained model's generalization ability by learning the isotropic and discriminative distribution of the acronym sentence representations .
Revised:  Specifically, we design a novel continual contrastive pre-training method that enhances the pre-trained model's generalization ability by learning the phrase-level contrastive distributions between true meaning and ambiguous phrases .
Edit intent: clarity
Original:  The proposed LiVLR is lightweight and shows its superiority on two VideoQA benchmarks, MRSVTT-QA and KnowIT VQA.
Revised:  The proposed LiVLR is lightweight and shows its performance advantage on two VideoQA benchmarks, MRSVTT-QA and KnowIT VQA.
Edit intent: clarity
Original:  Furthermore, it works in an 'open set' manner, with semantic classes defined on the fly by the user. iLabel 's underlying model is a multilayer perceptron (MLP) trained from scratch in real-time to learn a joint neural scene representation.
Revised:  Furthermore, it works in an 'open set' manner, with semantic classes defined on the fly by the user. ILabel 's underlying model is a multilayer perceptron (MLP) trained from scratch in real-time to learn a joint neural scene representation.
Edit intent: fluency
Original: The deep learning-based tomographic image reconstruction  have been attracting much attention among these years.
Revised: The deep learning-based tomographic image reconstruction methods have been attracting much attention among these years.
Edit intent: clarity
Original:  Third, with the classical Swin transformer , we further designed the high-quality reconstruction transformer (i.e., Recformer) to improve the reconstruction performance.
Revised:  Third, with the classical Swin Transformer , we further designed the high-quality reconstruction transformer (i.e., Recformer) to improve the reconstruction performance.
Edit intent: fluency
Original:  The trained network was transferred to the real cardiac CT dataset with 48 views, the reconstruction results further validated the advantages of our MIST-net and further demonstrated the good robustness of our MIST in clinical applications.
Revised:  The trained network was transferred to the real cardiac CT dataset with 48 views, the reconstruction results further validated the advantages of our MIST-net , which demonstrated the good robustness of our MIST in clinical applications.
Edit intent: coherence
Original:  The trained network was transferred to the real cardiac CT dataset with 48 views, the reconstruction results further validated the advantages of our MIST-net and further demonstrated the good robustness of our MIST in clinical applications.
Revised:  The trained network was transferred to the real cardiac CT dataset with 48 views, the reconstruction results further validated the advantages of our MIST-net and further demonstrated the good robustness of our MIST-net in clinical applications.
Edit intent: fluency
Original:  The trained network was transferred to the real cardiac CT dataset with 48 views, the reconstruction results further validated the advantages of our MIST-net , which demonstrated the good robustness of our MIST-net in clinical applications.
Revised:  The trained network was transferred to the real cardiac CT dataset to further validate the advantages of our MIST-net , which demonstrated the good robustness of our MIST-net in clinical applications.
Edit intent: clarity
Original:  The trained network was transferred to the real cardiac CT dataset with 48 views, the reconstruction results further validated the advantages of our MIST-net , which demonstrated the good robustness of our MIST-net in clinical applications.
Revised:  The trained network was transferred to the real cardiac CT dataset with 48 views, the reconstruction results further validated the advantages of our MIST-net as well as good robustness in clinical applications.
Edit intent: coherence
Original: Machine learning (ML) has demonstrated the promise for accurate andefficient property prediction of molecules and crystalline materials.
Revised: Machine learning (ML) has demonstrated the promise for accurate and efficient property prediction of molecules and crystalline materials.
Edit intent: fluency
Original:  Todevelop highly accurate ML models for chemical structure property pre-diction , datasets with sufficient samples are required.
Revised:  To develop highly accurate ML models for chemical structure property pre-diction , datasets with sufficient samples are required.
Edit intent: fluency
Original:  Todevelop highly accurate ML models for chemical structure property pre-diction , datasets with sufficient samples are required.
Revised:  Todevelop highly accurate ML models for chemical structure property prediction , datasets with sufficient samples are required.
Edit intent: fluency
Original:  However, obtainingclean and sufficient data of chemical properties can be expensive andtime-consuming , which greatly limits the performance of ML models.
Revised:  However, obtaining clean and sufficient data of chemical properties can be expensive andtime-consuming , which greatly limits the performance of ML models.
Edit intent: fluency
Original:  However, obtainingclean and sufficient data of chemical properties can be expensive andtime-consuming , which greatly limits the performance of ML models.
Revised:  However, obtainingclean and sufficient data of chemical properties can be expensive and time-consuming , which greatly limits the performance of ML models.
Edit intent: fluency
Original:  Inspired by the success of data augmentations in computer vision andnatural language processing, we developed AugLiChem: the data aug-mentation library for chemical structures.
Revised:  Inspired by the success of data augmentations in computer vision and natural language processing, we developed AugLiChem: the data aug-mentation library for chemical structures.
Edit intent: fluency
Original:  Inspired by the success of data augmentations in computer vision andnatural language processing, we developed AugLiChem: the data aug-mentation library for chemical structures.
Revised:  Inspired by the success of data augmentations in computer vision andnatural language processing, we developed AugLiChem: the data augmentation library for chemical structures.
Edit intent: fluency
Original:  Augmentation methods forboth crystalline systems and molecules are introduced, which can beutilized for fingerprint-based ML models and Graph Neural Networks(GNNs).
Revised:  Augmentation methods for both crystalline systems and molecules are introduced, which can beutilized for fingerprint-based ML models and Graph Neural Networks(GNNs).
Edit intent: fluency
Original:  Augmentation methods forboth crystalline systems and molecules are introduced, which can beutilized for fingerprint-based ML models and Graph Neural Networks(GNNs).
Revised:  Augmentation methods forboth crystalline systems and molecules are introduced, which can be utilized for fingerprint-based ML models and Graph Neural Networks(GNNs).
Edit intent: fluency
Original:  We show that using our augmentation strategies significantlyimproves the performance of ML models, especially when using GNNs.
Revised:  We show that using our augmentation strategies significantly improves the performance of ML models, especially when using GNNs.
Edit intent: fluency
Original:  In addition, the augmentations that we developed can be used as adirect plug-in module during training and have demonstrated the effec-tiveness when implemented with different GNN models through theAugliChem library.
Revised:  In addition, the augmentations that we developed can be used as a direct plug-in module during training and have demonstrated the effec-tiveness when implemented with different GNN models through theAugliChem library.
Edit intent: fluency
Original:  In addition, the augmentations that we developed can be used as adirect plug-in module during training and have demonstrated the effec-tiveness when implemented with different GNN models through theAugliChem library.
Revised:  In addition, the augmentations that we developed can be used as adirect plug-in module during training and have demonstrated the effectiveness when implemented with different GNN models through theAugliChem library.
Edit intent: fluency
Original:  In addition, the augmentations that we developed can be used as adirect plug-in module during training and have demonstrated the effec-tiveness when implemented with different GNN models through theAugliChem library.
Revised:  In addition, the augmentations that we developed can be used as adirect plug-in module during training and have demonstrated the effec-tiveness when implemented with different GNN models through the AugliChem library.
Edit intent: fluency
Original:  The Python-based package for our implementa-tion of Auglichem: Data augmentation library for chemical structures, is publicly available at: URL
Revised:  The Python-based package for our implementation of Auglichem: Data augmentation library for chemical structures, is publicly available at: URL
Edit intent: fluency
Original:  Our solution juxtaposes the problem of automated semantification as classification versus clustering where the two methods are on opposite ends of the method complexity spectrum.
Revised:  Our solution contrasts the problem of automated semantification as classification versus clustering where the two methods are on opposite ends of the method complexity spectrum.
Edit intent: fluency
Original:  Our solution juxtaposes the problem of automated semantification as classification versus clustering where the two methods are on opposite ends of the method complexity spectrum.
Revised:  Our solution juxtaposes the problem of automated semantification as labeling versus clustering where the two methods are on opposite ends of the method complexity spectrum.
Edit intent: clarity
Original:  Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art classification approach.
Revised:  Characteristically modeling our problem, we find the clustering solution significantly outperforms a deep neural network state-of-the-art labeling approach.
Edit intent: clarity
Original:  To solve this problem, we present an end-to-end framework for constructing a hallucinated NeRF, dubbed as H-NeRF . Specifically, we propose an appearance hallucination module to handle time-varying appearances and transfer them to novel views.
Revised:  To solve this problem, we present an end-to-end framework for constructing a hallucinated NeRF, dubbed as Ha-NeRF . Specifically, we propose an appearance hallucination module to handle time-varying appearances and transfer them to novel views.
Edit intent: fluency
Original:  CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation ( nnUnet\_T ).
Revised:  CE-CT scans from a cohort of 119 pathology-proven PDAC patients and a cohort of 123 patients without PDAC were used to train a nnUnet for automatic lesion detection and segmentation ( nnUnet_T ).
Edit intent: fluency
Original:  Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor ( nnUnet\_TP ), (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures ( nnUnet\_MS ). An external, publicly available test set was used to compare the performance of the three networks.
Revised:  Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor ( nnUnet_TP ), (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures ( nnUnet\_MS ). An external, publicly available test set was used to compare the performance of the three networks.
Edit intent: fluency
Original:  Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor ( nnUnet\_TP ), (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures ( nnUnet\_MS ). An external, publicly available test set was used to compare the performance of the three networks.
Revised:  Two additional nnUnets were trained to investigate the impact of anatomy integration: (1) segmenting the pancreas and tumor ( nnUnet\_TP ), (2) segmenting the pancreas, tumor, and multiple surrounding anatomical structures ( nnUnet_MS ). An external, publicly available test set was used to compare the performance of the three networks.
Edit intent: fluency
Original:  The nnUnet\_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors <2cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.
Revised:  The nnUnet_MS achieved the best performance, with an area under the receiver operating characteristic curve of 0.91 for the whole test set and 0.88 for tumors <2cm, showing that state-of-the-art deep learning can detect small PDAC and benefits from anatomy information.
Edit intent: clarity
Original:  To identify efficient precoding matrices, an optimization approach is used.
Revised:  To collect optimal precoding matrices, an optimization approach is used.
Edit intent: clarity
Original:  We also show that this two-level encoding improves denoising efficiency and naturally facil itates various downstream tasks including few-shot conditional sampling. 
Revised:  We also show that this two-level encoding improves denoising efficiency and naturally facilitates various downstream tasks including few-shot conditional sampling. 
Edit intent: fluency
Original:  We investigate the impact of FFA on the performance of two theory-inspired, state-of-the-art EAs, the Greedy (2+1) GA and the Self-Adjusting (1+  lambda,lambda)) GA. FFA improves their performance significantly on some problems that are hard for them.
Revised:  We investigate the impact of FFA on the performance of two theory-inspired, state-of-the-art EAs, the Greedy (2+1) GA and the Self-Adjusting (1+ ( lambda,lambda)) GA. FFA improves their performance significantly on some problems that are hard for them.
Edit intent: fluency
Original:  Moreover, in almost all theoretically studied cases, the index sequences \{i_{n}\} of matrices maximizing the norms of the corresponding matrix products turned out to be periodic or so-called Sturmian sequences, which entails a whole set of ``good'' properties of the sequences \{A_{i_{n}}\}, in particular the existence of a limiting frequency of occurrence of each matrix factor A_{i}\inA in them.
Revised:  Moreover, in almost all theoretically studied cases, the index sequences \{i_{n}\} of matrices maximizing the norms of the corresponding matrix products turned out to be periodic or so-called Sturmian sequences, which entails a whole set of "good" properties of the sequences \{A_{i_{n}}\}, in particular the existence of a limiting frequency of occurrence of each matrix factor A_{i}\inA in them.
Edit intent: fluency
Original: It was noted already in the 90s that many classic graph classes, such as interval, chordal, and bipartite graphs, can be characterized by the existence of an ordering of the vertices avoiding some ordered subgraphs, called patterns .
Revised: It was noted already in the 90s that many classic graph classes, such as interval, chordal, and bipartite graphs, can be characterized by the existence of an ordering of the vertices avoiding some ordered subgraphs, called patterns .
Edit intent: fluency
Original:  One of the few graph classes characterized by a pattern on four vertices is the class of intersection graphs of rectangles that are said to be grounded on a line .
Revised:  One of the few graph classes characterized by a pattern on four vertices is the class of intersection graphs of rectangles that are said to be grounded on a line .
Edit intent: fluency
Original:  To address this challenge, we hypothesize that even if most of the decisions based on different FEMs are viewed as weak decisions , which are not available for all classes, they still perform decently in some specific categories.
Revised:  To address this challenge, we hypothesize that even if most of the decisions based on different FEMs are viewed as weak decisions , which are not available for all classes, they still perform decently in some specific categories.
Edit intent: fluency
Original:  We provide a complete converge analysis, which is based on the properties of the iterative Bregman projections (IBP) method with greedy control.
Revised:  We provide a complete convergence analysis, which is based on the properties of the iterative Bregman projections (IBP) method with greedy control.
Edit intent: fluency
Original:  The RS schemes divides the messages of the users into two separate common and private portions so that interference is managed robustly.
Revised:  The RS approach divides the messages of the users into two separate common and private portions so that interference is managed robustly.
Edit intent: clarity
Original:  However, dialogue-oriented open-ended conversations such as teaching a student about scale factor can be difficult to model.
Revised:  However, dialogue-oriented open-ended conversations such as teaching a student about scale factors can be difficult to model.
Edit intent: fluency
Original:  This paper presents a high-fidelity, AI-based classroom simulator to help teachers rehearse research-based mathematical questioning skills  .
Revised:  This paper builds a text-based interactive conversational agent to help teachers rehearse research-based mathematical questioning skills .
Edit intent: clarity
Original:  This paper presents a high-fidelity, AI-based classroom simulator to help teachers rehearse research-based mathematical questioning skills  .
Revised:  This paper presents a high-fidelity, AI-based classroom simulator to help teachers practice mathematical questioning skills .
Edit intent: clarity