wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we present a new approach to improve the capability of language model pre-training on VRDs. Firstly, we introduce a new  IE model that is query-based and employs the span extraction formulation instead of the commonly used sequence labelling approach.
Revised:  In this paper, we present a new approach to improve the capability of language model pre-training on VRDs. Firstly, we introduce a new IE model that employs span extraction instead of using the common sequence labeling approach.
Edit intent: clarity
Original:  Evaluation on various datasets of popular business documents (invoices, receipts) shows that our proposed method can improve the performance of existing modelssignificantly, while providing a mechanism to accumulate model knowledge from multiple downstream IE tasks.
Revised:  Evaluation on three datasets of popular business documents (invoices, receipts) shows that our proposed method can improve the performance of existing modelssignificantly, while providing a mechanism to accumulate model knowledge from multiple downstream IE tasks.
Edit intent: meaning-changed
Original:  Evaluation on various datasets of popular business documents (invoices, receipts) shows that our proposed method can improve the performance of existing modelssignificantly, while providing a mechanism to accumulate model knowledge from multiple downstream IE tasks.
Revised:  Evaluation on various datasets of popular business documents (invoices, receipts) shows that our proposed method achieves significant improvements compared to existing models. The method also provides a mechanism for knowledge accumulation from multiple downstream IE tasks.
Edit intent: clarity
Original: This paper considers the Tikhonv regularization continuation method and the trust-region updating strategy for the linearly equality-constrained optimization problem (Trcmtr). Moreover, in order to improve its computational efficiency and robustness, the new method uses the switching preconditioned technique. That is to say , the new method uses the L-BFGS method as the preconditioned technique to improve its computational efficiency in the well-posed phase .
Revised: This paper considers the Tikhonv regularization continuation method and the trust-region updating strategy for the linearly equality-constrained optimization problem . The proposed method utilizes the linear conservation law of the regularization method such that it does not need to compute the correction step for preserving the feasibility other than the previous continuation methods and the quasi-Newton updating formulas for the linearly equality-constrained optimization problem. Moreover , the new method uses the L-BFGS method as the preconditioned technique to improve its computational efficiency in the well-posed phase .
Edit intent: meaning-changed
Original:  That is to say , the new method uses the L-BFGS method as the preconditioned technique to improve its computational efficiency in the well-posed phase . Otherwise, it uses the inverse of the regularization two-sided projected Hessian matrix as the pre-conditioner to improve its robustness.
Revised:  That is to say , the new method uses the L-BFGS method as the preconditioned technique to improve its computational efficiency in the well-posed phase , and it uses the inverse of the regularization two-sided projected Hessian matrix as the pre-conditioner to improve its robustness.
Edit intent: coherence
Original:  Numerical results also show that the new method is more robust and faster than the traditional optimization method such as the sequential quadratic programming (SQP), the alternating direction method of multipliers (ADMM) and the latest continuation method(Ptctr). The computational time of the new method is about one fifth of that of SQP for the large-scale problem .
Revised:  Numerical results also show that the new method is more robust and faster than the traditional optimization method and the recent continuation method .
Edit intent: coherence
Original:  The goal of this toolkit is twofold: firstly , to provide a broad range of capabilities to streamline , and hopefully foster the common practices of quantifying, evaluating, improving, and communicating uncertainty in the AI application development lifecycle;
Revised:  The goal of this toolkit is twofold: first , to provide a broad range of capabilities to streamline , and hopefully foster the common practices of quantifying, evaluating, improving, and communicating uncertainty in the AI application development lifecycle;
Edit intent: coherence
Original:  The goal of this toolkit is twofold: firstly , to provide a broad range of capabilities to streamline , and hopefully foster the common practices of quantifying, evaluating, improving, and communicating uncertainty in the AI application development lifecycle;
Revised:  The goal of this toolkit is twofold: firstly , to provide a broad range of capabilities to streamline as well as foster the common practices of quantifying, evaluating, improving, and communicating uncertainty in the AI application development lifecycle;
Edit intent: clarity
Original:  secondly, to disseminate the latest research and educational materials for uncertainty quantification in machine learning, and encourage further exploration of its utility and connections to other pillars of trustworthy AI such as fairness and explainability .
Revised:  secondly, to disseminate the latest research and educational materials for uncertainty quantification in machine learning, and encourage further exploration of UQ's connections to other pillars of trustworthy AI such as fairness and explainability .
Edit intent: clarity
Original:  secondly, to disseminate the latest research and educational materials for uncertainty quantification in machine learning, and encourage further exploration of its utility and connections to other pillars of trustworthy AI such as fairness and explainability .
Revised:  secondly, to disseminate the latest research and educational materials for uncertainty quantification in machine learning, and encourage further exploration of its utility and connections to other pillars of trustworthy AI such as fairness and transparency through the dissemination of latest research and education materials .
Edit intent: clarity
Original:  PGBM achieves accurate probabilistic estimates in tasks with complex differentiable loss functions, such as hierarchical time series problems, where we observed up to 10 %DIFDELCMD < \\%%% %DIF < improvement in point forecasting performance and up to 300\\% improvement in probabilistic forecasting performance.abstract 
Revised:  PGBM achieves accurate probabilistic estimates in tasks with complex differentiable loss functions, such as hierarchical time series problems, where we observed up to 10 %DIFDELCMD < \\%%% %DIF < improvement in point forecasting performance and up to 300\\% improvement in probabilistic forecasting performance.abstract \% improvement in point forecasting performance and up to 300\% improvement in probabilistic forecasting performance.
Edit intent: meaning-changed
Original:  For n-vertex graphs with constant diameter D=O(1), Das-Sarma et al. (STOC 2011, SIAM J. Comput. 2012 ) presented an (implicit) shortcuts lower bound with (As usual, and%DIFDELCMD < \Omega() %%% hide poly-logarithmic factors.) c+d=\Omega(n^{(D-2)/(2D-2)}).
Revised:  For n-vertex graphs with constant diameter D=O(1), Elkin (STOC 2004 ) presented an (implicit) shortcuts lower bound with (As usual, and%DIFDELCMD < \Omega() %%% hide poly-logarithmic factors.) c+d=\Omega(n^{(D-2)/(2D-2)}).
Edit intent: meaning-changed
Original:  2012 ) presented an (implicit) shortcuts lower bound with (As usual, and%DIFDELCMD < \Omega() %%% hide poly-logarithmic factors.) c+d=\Omega(n^{(D-2)/(2D-2)}).
Revised:  2012 ) presented an (implicit) shortcuts lower bound with and%DIFDELCMD < \Omega() %%% hide poly-logarithmic factors.) c+d=\Omega(n^{(D-2)/(2D-2)}).
Edit intent: coherence
Original:  2012 ) presented an (implicit) shortcuts lower bound with (As usual, and%DIFDELCMD < \Omega() %%% hide poly-logarithmic factors.) c+d=\Omega(n^{(D-2)/(2D-2)}).
Revised:  2012 ) presented an (implicit) shortcuts lower bound with (As usual, %DIFDELCMD < \Omega() %%% hide poly-logarithmic factors.) c+d=\Omega(n^{(D-2)/(2D-2)}).
Edit intent: fluency
Original:  2012 ) presented an (implicit) shortcuts lower bound with (As usual, and%DIFDELCMD < \Omega() %%% hide poly-logarithmic factors.) c+d=\Omega(n^{(D-2)/(2D-2)}).
Revised:  2012 ) presented an (implicit) shortcuts lower bound with (As usual, and%DIFDELCMD < \Omega() %%% c+d=\Omega(n^{(D-2)/(2D-2)}).
Edit intent: clarity
Original:  While being conceptually motivated by singular value decomposition (SVD), our proposed nonlinear counterpart outperforms SVD by drastically reducing the memory required to approximate a data matrix with the same L_2-error for a wide range of matrix types.
Revised:  While being conceptually motivated by singular value decomposition (SVD), our proposed nonlinear counterpart outperforms SVD by drastically reducing the memory required to approximate a data matrix with the same L2 error for a wide range of matrix types.
Edit intent: clarity
Original:  These algorithms would also work for the weighted tree edit distance problem, which cannot be solved in truly sub-cubic time under the APSP conjecture [Bringmann, Gawrychowski, Mozes, and Weimann, SODA 2018].
Revised:  These algorithms would also work for the weighted tree edit distance problem, which cannot be solved in truly sub-cubic time under the APSP conjecture [Bringmann, Gawrychowski, Mozes, and Weimann, SODA 2018].
Edit intent: fluency
Original:  We provide an efficient technique for estimating the corresponding Lipschitz constant using extreme value theory and demonstrate that by only scaling the learning rate schedule we can increase adversarial accuracy by up to 15\%  on benchmark datasets.
Revised:  We provide an efficient technique for estimating the corresponding Lipschitz constant using extreme value theory and demonstrate that by only scaling the learning rate schedule we can increase adversarial accuracy by up to 15\% points on benchmark datasets.
Edit intent: meaning-changed
Original:  Our approach also universally increases the adversarial accuracy by 0.1\% to 0.3\%  in various state-of-the-art adversarially trained models on the AutoAttack benchmark, where every small margin of improvement is significant.
Revised:  Our approach also universally increases the adversarial accuracy by 0.1\% to 0.3\% points in various state-of-the-art adversarially trained models on the AutoAttack benchmark, where every small margin of improvement is significant.
Edit intent: meaning-changed
Original:  Applying machine learning models to these tasks requires us to create numerical representations of the changes, i. e. embeddings. Recent studies demonstrate that the best way to obtain these embeddings is to pre-train a deep neural network in an unsupervised manner on a large volume of unlabeled data and then further fine-tune it for a specific task.
Revised:  Applying machine learning to tasks that operate with code changes requires their numerical representation.
Edit intent: clarity
Original:  The pre-training consists of the model learning to apply the given change (an edit sequence) to the code in a correct way , and therefore requires only the code change itself. To increase the quality of the obtained embeddings, we only consider the changed tokens in the edit sequence .
Revised:  The pre-training consists of the model learning to apply the given change (an edit sequence) to the code in a correct way . This task requires only code changes themselves, which makes it unsupervised .
Edit intent: clarity
Original:  Yet reference protein interaction networks lack such contextualization, thereby limiting the study of where protein interactions likely occur in the human body  . Contextualized protein interactions could better characterize genes with disease-specific interactions and elucidate diseases' manifestation in specific cell types.
Revised:  Yet reference protein interaction networks lack such contextualization, thereby limiting the study of where protein interactions likely occur in the human body and how they may be altered in disease . Contextualized protein interactions could better characterize genes with disease-specific interactions and elucidate diseases' manifestation in specific cell types.
Edit intent: meaning-changed
Original:  We lay the foundations via moral philosophy's definition of social good, propose a framework to evaluate NLP tasks' direct and indirect real-world impact  , and adopt the methodology of global priorities research to identify priority causes for NLP research.
Revised:  We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate NLP tasks' direct and indirect real-world impact , and adopt the methodology of global priorities research to identify priority causes for NLP research.
Edit intent: fluency
Original:  We lay the foundations via moral philosophy's definition of social good, propose a framework to evaluate NLP tasks' direct and indirect real-world impact  , and adopt the methodology of global priorities research to identify priority causes for NLP research.
Revised:  We lay the foundations via moral philosophy's definition of social good, propose a framework to evaluate the direct and indirect real-world impact , and adopt the methodology of global priorities research to identify priority causes for NLP research.
Edit intent: clarity
Original:  We lay the foundations via moral philosophy's definition of social good, propose a framework to evaluate NLP tasks' direct and indirect real-world impact  , and adopt the methodology of global priorities research to identify priority causes for NLP research.
Revised:  We lay the foundations via moral philosophy's definition of social good, propose a framework to evaluate NLP tasks' direct and indirect real-world impact of NLP tasks , and adopt the methodology of global priorities research to identify priority causes for NLP research.
Edit intent: meaning-changed
Original:  While great research efforts have been devoted to exploiting deep neural networks for improved stock prediction, two major issues still exist in recent studies. First, the capture of long-range dependencies in time series is not sufficiently addressed. Second, the chaotic property of financial time series fundamentally lowers prediction performance.
Revised:  While great research efforts have been devoted to exploiting deep neural networks for improved stock prediction, two major issues that lower the performance of state-of-the-art deep learning models in forecasting future price trends.
Edit intent: clarity
Original:  Particularly, our AOT-L outperforms all the state-of-the-art competitors on three popular benchmarks, i.e., YouTube-VOS (83.7\% J&F), DAVIS 2017 (83.0\%), and DAVIS 2016 (91.0\%), while keeping better multi-object efficiency .
Revised:  Particularly, our AOT-L outperforms all the state-of-the-art competitors on three popular benchmarks, i.e., YouTube-VOS (83.7\% J&F), DAVIS 2017 (83.0\%), and DAVIS 2016 (91.0\%), while keeping more than 3X faster multi-object efficiency .
Edit intent: clarity
Original:  Particularly, our AOT-L outperforms all the state-of-the-art competitors on three popular benchmarks, i.e., YouTube-VOS (83.7\% J&F), DAVIS 2017 (83.0\%), and DAVIS 2016 (91.0\%), while keeping better multi-object efficiency .
Revised:  Particularly, our AOT-L outperforms all the state-of-the-art competitors on three popular benchmarks, i.e., YouTube-VOS (83.7\% J&F), DAVIS 2017 (83.0\%), and DAVIS 2016 (91.0\%), while keeping better multi-object run-time .
Edit intent: clarity
Original:  While eigenfunctions are discarded in this analysis, a viable reconstruction algorithm is still demonstrated, and the sacrifice of eigenfunctions realizes the theoretical goals of DMD analysis that have yet to be achieved in other contexts.
Revised:  While eigenfunctions are discarded in the general analysis, a viable reconstruction algorithm is still demonstrated, and the sacrifice of eigenfunctions realizes the theoretical goals of DMD analysis that have yet to be achieved in other contexts.
Edit intent: clarity
Original:   The manuscript concludes with the description of a Dynamic Mode Decomposition algorithm that converges when a dense collection of occupation kernels, arising from the data, are leveraged in the analysis.
Revised:  However, in the case where the domain is embedded in the range, an eigenfunction approach is still achievable, where a more typical DMD routine is established, but that leverages a finite rank representation that converges in norm. The manuscript concludes with the description of a Dynamic Mode Decomposition algorithm that converges when a dense collection of occupation kernels, arising from the data, are leveraged in the analysis.
Edit intent: meaning-changed
Original:  The unique training methodology achieves up to 2.5\\% relative WERR compared to a PPA baseline.abstract 
Revised:  The unique training methodology achieves up to 2.5\\% relative WERR compared to a PPA baseline.abstract \% relative word-error-rate reduction (WERR) compared to a PO baseline, and up to 16.0\% relative WERR in low-SNR conditions. The unique training methodology achieves up to 2.5\% relative WERR compared to a PPA baseline.
Edit intent: meaning-changed
Original:  Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with high resolution down to 2 \mu m . This technique enables detection of the morphologies of individual microaneurysms (MAs), which are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness .
Revised: Microaneurysms (MAs) are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness. Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with high resolution down to 2 \mu m . This technique enables detection of the morphologies of individual microaneurysms (MAs), which are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness .
Edit intent: fluency
Original:  Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with high resolution down to 2 \mu m . This technique enables detection of the morphologies of individual microaneurysms (MAs), which are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness .
Revised:  Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with resolution down to 2 \mu m . This technique enables detection of the morphologies of individual microaneurysms (MAs), which are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness .
Edit intent: clarity
Original:  Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with high resolution down to 2 \mu m . This technique enables detection of the morphologies of individual microaneurysms (MAs), which are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness .
Revised:  Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with high resolution down to 2 \mu m and thus allows detection of the morphologies of individual microaneurysms (MAs), which are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness .
Edit intent: coherence
Original:  Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with high resolution down to 2 \mu m . This technique enables detection of the morphologies of individual microaneurysms (MAs), which are one of the earliest signs of diabetic retinopathy (DR), a frequent complication of diabetes that can lead to visual impairment and blindness .
Revised:  Adaptive optics scanning laser ophthalmoscopy (AOSLO) provides real-time retinal images with high resolution down to 2 \mu m . This technique enables detection of the morphologies of individual MAs, a potential marker that might dictate MA pathology and affect the progression of DR .
Edit intent: clarity
Original:  We evaluate the performance of AOSLO-net using 87 DR AOSLO images demonstrating very accurate MA detection and segmentation, leading to correct MA morphological classification, while outperforming the state-of-the-art  both in accuracy and cost  .
Revised:  We evaluate the performance of AOSLO-net using 87 DR AOSLO images and our results demonstrate that the proposed model outperforms the state-of-the-art both in accuracy and cost .
Edit intent: clarity
Original:  We evaluate the performance of AOSLO-net using 87 DR AOSLO images demonstrating very accurate MA detection and segmentation, leading to correct MA morphological classification, while outperforming the state-of-the-art  both in accuracy and cost  .
Revised:  We evaluate the performance of AOSLO-net using 87 DR AOSLO images demonstrating very accurate MA detection and segmentation, leading to correct MA morphological classification, while outperforming the state-of-the-art segmentation model both in accuracy and cost .
Edit intent: clarity
Original:  We evaluate the performance of AOSLO-net using 87 DR AOSLO images demonstrating very accurate MA detection and segmentation, leading to correct MA morphological classification, while outperforming the state-of-the-art  both in accuracy and cost  .
Revised:  We evaluate the performance of AOSLO-net using 87 DR AOSLO images demonstrating very accurate MA detection and segmentation, leading to correct MA morphological classification, while outperforming the state-of-the-art both in accuracy and cost and enables correct MA morphological classification .
Edit intent: coherence
Original: Sensor technology is becoming increasingly prevalent across a multitude of fields and industries. As a result, simultaneous recordings of multiple inter-correlated signals is becoming increasingly common. With this, more problems of a practical nature emerge due to sensor clock-drift, offsets, and other complications. Processing of multiple sensor data is often dependent on the data being properly alignedin the temporal dimension. The alignment process is a necessary step before the data can be evaluated properly but it is a time consuming process, often involving significant manual labor and expertise. Regularly used methods to align sensor signals have trouble addressing real-world issues such as morphological dissimilarities, excessive noise, or very long, raw sensor signals .
Revised: Sensor technology is becoming increasingly prevalent in the biomedical field, with applications ranging from telemonitoring of people at risk, to using sensor derived information as objective endpoints in clinical trials. To fully utilize sensor information, signals from distinct sensors often have to be temporally aligned .
Edit intent: clarity
Original:   In this work, we present Deep Canonical Correlation Sensor Alignment (DCCA), a method that is specifically tailored to address these problems.
Revised:  However, due to imperfect oscillators and significant noise, commonly encountered with biomedical signals, temporal alignment of raw signals is an all but trivial problem, with, to-date, no generally applicable solution. In this work, we present Deep Canonical Correlation Sensor Alignment (DCCA), a method that is specifically tailored to address these problems.
Edit intent: meaning-changed