wanyu/IteraTeR_v2 huggingface dataset:
Original:  A Physics-Informed Neural Networks (PINNS) method is presented, learning a compact and efficient surrogate model with parameterized moving  sources and impedance boundaries, satisfying a system of coupled equations.
Revised:  A Physics-Informed Neural Networks (PINNS) method is presented, which learns a compact and efficient surrogate model with parameterized moving sources and impedance boundaries, satisfying a system of coupled equations.
Edit intent: coherence
Original:  A Physics-Informed Neural Networks (PINNS) method is presented, learning a compact and efficient surrogate model with parameterized moving  sources and impedance boundaries, satisfying a system of coupled equations.
Revised:  A Physics-Informed Neural Networks (PINNS) method is presented, learning a compact and efficient surrogate model with parameterized moving sources and impedance boundaries, and satisfies a system of coupled equations.
Edit intent: coherence
Original:  The trained model shows relative mean errors below 2\%/0.2 dB , indicating that acoustics with moving sources and impedance boundaries can be predicted in real-time using PINNs .
Revised:  The model shows relative mean errors below 2\%/0.2 dB , indicating that acoustics with moving sources and impedance boundaries can be predicted in real-time using PINNs .
Edit intent: clarity
Original: Recent Weak Supervision (WS) approaches have had widespread success in easing the bottleneck of labeling training data for machine learning by synthesizing labels from multiple potentially noisy supervision sources.
Revised: Recent Weak Supervision (WS) approaches have had widespread success in easing the bottleneck of labeling training data for machine learning by synthesizing labels from multiple potentially noisy supervision sources.
Edit intent: fluency
Original:  We first develop a piecewise Pad\'e-Chebyshev method (PiPC ) to approximate univariate piecewise smooth functions and then extend the same to a two dimensional space, leading to a piecewise bivariate Pad\'e-Chebyshev approximation (Pi2DPC) for approximating bivariate piecewise smooth functions .
Revised:  We first develop a method referred to as PiPC to approximate univariate piecewise smooth functions and then extend the same to a two dimensional space, leading to a piecewise bivariate Pad\'e-Chebyshev approximation (Pi2DPC) for approximating bivariate piecewise smooth functions .
Edit intent: clarity
Original:  We first develop a piecewise Pad\'e-Chebyshev method (PiPC ) to approximate univariate piecewise smooth functions and then extend the same to a two dimensional space, leading to a piecewise bivariate Pad\'e-Chebyshev approximation (Pi2DPC) for approximating bivariate piecewise smooth functions .
Revised:  We first develop a piecewise Pad\'e-Chebyshev method (PiPC ) to approximate univariate piecewise smooth functions and then extend the same to a two-dimensional space, leading to a piecewise bivariate Pad\'e-Chebyshev approximation (Pi2DPC) for approximating bivariate piecewise smooth functions .
Edit intent: fluency
Original:  We first develop a piecewise Pad\'e-Chebyshev method (PiPC ) to approximate univariate piecewise smooth functions and then extend the same to a two dimensional space, leading to a piecewise bivariate Pad\'e-Chebyshev approximation (Pi2DPC) for approximating bivariate piecewise smooth functions .
Revised:  We first develop a piecewise Pad\'e-Chebyshev method (PiPC ) to approximate univariate piecewise smooth functions and then extend the same to a two dimensional space, leading to a bivariate piecewise Pad\'e-Chebyshev approximation (Pi2DPC) for approximating bivariate piecewise smooth functions .
Edit intent: fluency
Original:  We first develop a piecewise Pad\'e-Chebyshev method (PiPC ) to approximate univariate piecewise smooth functions and then extend the same to a two dimensional space, leading to a piecewise bivariate Pad\'e-Chebyshev approximation (Pi2DPC) for approximating bivariate piecewise smooth functions .
Revised:  We first develop a piecewise Pad\'e-Chebyshev method (PiPC ) to approximate univariate piecewise smooth functions and then extend the same to a two dimensional space, leading to a piecewise bivariate Pad\'e-Chebyshev approximation (Pi2DPC) for approximating piecewise smooth functions .
Edit intent: fluency
Original:  The chief advantage of these methods lie in their non dependence on any apriori knowledge of the locations and types of singularities  present in the original function.
Revised:  The chief advantage of these methods lies in their non-dependence on any apriori knowledge of the locations and types of singularities present in the original function.
Edit intent: fluency
Original:  Finally, we supplement our method with numerical results which validate its effectiveness in diminishing the Gibbs phenomenon to negligible levels.
Revised:  Finally, we supplement our methods with numerical results which validate its effectiveness in diminishing the Gibbs phenomenon to negligible levels.
Edit intent: fluency
Original:  Finally, we supplement our method with numerical results which validate its effectiveness in diminishing the Gibbs phenomenon to negligible levels.
Revised:  Finally, we supplement our method with numerical results to validate their effectiveness in diminishing the Gibbs phenomenon to negligible levels.
Edit intent: coherence
Original:  TLG is inherently a challenging task, as it requires to have comprehensive understanding of both video contents and text sentences . Previous works either tackle this task in a fully-supervised setting that requires a large amount of manual annotations or in a weakly supervised setting that cannot achieve satisfactory performance.
Revised:  TLG is inherently a challenging task, as it requires comprehensive understanding of both video contents and text sentences . Previous works either tackle this task in a fully-supervised setting that requires a large amount of manual annotations or in a weakly supervised setting that cannot achieve satisfactory performance.
Edit intent: coherence
Original:  TLG is inherently a challenging task, as it requires to have comprehensive understanding of both video contents and text sentences . Previous works either tackle this task in a fully-supervised setting that requires a large amount of manual annotations or in a weakly supervised setting that cannot achieve satisfactory performance.
Revised:  TLG is inherently a challenging task, as it requires to have comprehensive understanding of both sentence semantics and video contents . Previous works either tackle this task in a fully-supervised setting that requires a large amount of manual annotations or in a weakly supervised setting that cannot achieve satisfactory performance.
Edit intent: clarity
Original:  TLG is inherently a challenging task, as it requires to have comprehensive understanding of both video contents and text sentences . Previous works either tackle this task in a fully-supervised setting that requires a large amount of manual annotations or in a weakly supervised setting that cannot achieve satisfactory performance.
Revised:  TLG is inherently a challenging task, as it requires to have comprehensive understanding of both video contents and text sentences . Previous works either tackle this task in a fully-supervised setting that requires a large amount of manual annotations or in a weakly-supervised setting that usually cannot achieve satisfactory performance.
Edit intent: clarity
Original:  To achieve good performance with limited annotations, we tackle this task in a semi-supervised way and propose a unified Semi-supervised Temporal Language Grounding ( STLG)framework.
Revised:  To achieve good performance with limited annotations, we tackle TLG in a semi-supervised way and propose a unified Semi-supervised Temporal Language Grounding ( STLG)framework.
Edit intent: clarity
Original:  STLG consists of two parts: (1) A pseudo label generation module that produces adaptive instant pseudo labels for unlabeled data based on predictions from a teacher model;
Revised:  STLG consists of two parts: (1) A pseudo label generation module that adaptively produces instant pseudo labels for unlabeled data based on predictions from a teacher model;
Edit intent: fluency
Original:  STLG consists of two parts: (1) A pseudo label generation module that produces adaptive instant pseudo labels for unlabeled data based on predictions from a teacher model;
Revised:  STLG consists of two parts: (1) A pseudo label generation module that produces adaptive instant pseudo labels for unlabeled samples based on predictions from a teacher model;
Edit intent: clarity
Original:  (2) A self-supervised feature learning module with two sequential perturbations, i.e., time lagging and time scaling, for improving the video representation by inter-modal and intra-modal contrastive learning .
Revised:  (2) A self-supervised feature learning module with inter-modal and intra-modal contrastive learning .
Edit intent: coherence
Original:  We conduct  experiments on the ActivityNet-CD-OOD and Charades-CD-OOD datasets and the results demonstrate that our proposed STLG framework achieve competitive performance compared to fully-supervised state-of-the-art methods with only a small portion of temporal annotations.
Revised:  We conduct extensive experiments on the ActivityNet-CD-OOD and Charades-CD-OOD datasets and the results demonstrate that our proposed STLG framework achieve competitive performance compared to fully-supervised state-of-the-art methods with only a small portion of temporal annotations.
Edit intent: clarity
Original:  We conduct  experiments on the ActivityNet-CD-OOD and Charades-CD-OOD datasets and the results demonstrate that our proposed STLG framework achieve competitive performance compared to fully-supervised state-of-the-art methods with only a small portion of temporal annotations.
Revised:  We conduct experiments on the ActivityNet-CD-OOD and Charades-CD-OOD datasets . The results demonstrate that our proposed STLG framework achieve competitive performance compared to fully-supervised state-of-the-art methods with only a small portion of temporal annotations.
Edit intent: coherence
Original:  We conduct  experiments on the ActivityNet-CD-OOD and Charades-CD-OOD datasets and the results demonstrate that our proposed STLG framework achieve competitive performance compared to fully-supervised state-of-the-art methods with only a small portion of temporal annotations.
Revised:  We conduct experiments on the ActivityNet-CD-OOD and Charades-CD-OOD datasets and the results demonstrate that our proposed STLG framework achieve competitive performance compared to fully-supervised state-of-the-art methods while only requiring a small portion of temporal annotations.
Edit intent: clarity
Original:  We show that recent progress on fast sparse linear solvers can be leveraged to obtain faster than matrix-multiplication algorithms for any p > 1} , i.e., standard linear regression, as well as p-norm regression (for 1  < p < \infty) can be improved to go below the matrix multiplication threshold for sufficiently sparse matrices.
Revised:  We show that recent progress on fast sparse linear solvers can be leveraged to obtain faster than matrix-multiplication algorithms for any p > 1} , i.e., standard linear regression, as well as p-norm regression (for 1 < \omega, the matrix multiplication threshold for sufficiently sparse matrices.
Edit intent: clarity
Original: Learning from synthetic data is popular in avariety of robotic vision tasks such as object detection, becauselarge amount of data can be generated without annotationsby humans.
Revised: Learning from synthetic data is popular in a variety of robotic vision tasks such as object detection, becauselarge amount of data can be generated without annotationsby humans.
Edit intent: fluency
Original: Learning from synthetic data is popular in avariety of robotic vision tasks such as object detection, becauselarge amount of data can be generated without annotationsby humans.
Revised: Learning from synthetic data is popular in avariety of robotic vision tasks such as object detection, because a large amount of data can be generated without annotationsby humans.
Edit intent: fluency
Original: Learning from synthetic data is popular in avariety of robotic vision tasks such as object detection, becauselarge amount of data can be generated without annotationsby humans.
Revised: Learning from synthetic data is popular in avariety of robotic vision tasks such as object detection, becauselarge amount of data can be generated without annotations by humans.
Edit intent: fluency
Original:  However, when relying only on synthetic data,we encounter the well-known problem of the simulation-to-reality (Sim-to-Real) gap, which is hard to resolve completelyin practice.
Revised:  However, when relying only on synthetic data,we encounter the well-known problem of the simulation-to-reality (Sim-to-Real) gap, which is hard to resolve completely in practice.
Edit intent: fluency
Original:  For such cases, real human-annotated data isnecessary to bridge this gap, and in our work we focus on howto acquire this data efficiently.
Revised:  For such cases, real human-annotated data is necessary to bridge this gap, and in our work we focus on howto acquire this data efficiently.
Edit intent: fluency
Original:  Therefore, we propose a Sim-to-Real pipeline that relies on deep Bayesian active learningand aims to minimize the manual annotation efforts.
Revised:  Therefore, we propose a Sim-to-Real pipeline that relies on deep Bayesian active learning and aims to minimize the manual annotation efforts.
Edit intent: fluency
Original:  We devisea learning paradigm that autonomously selects the data thatis considered useful for the human expert to annotate.
Revised:  We devise a learning paradigm that autonomously selects the data thatis considered useful for the human expert to annotate.
Edit intent: fluency
Original:  We devisea learning paradigm that autonomously selects the data thatis considered useful for the human expert to annotate.
Revised:  We devisea learning paradigm that autonomously selects the data that is considered useful for the human expert to annotate.
Edit intent: fluency
Original:  Toachieve this, a Bayesian Neural Network (BNN) object detectorproviding reliable uncertain estimates is adapted to infer theinformativeness of the unlabeled data, in order to performactive learning.
Revised:  To achieve this, a Bayesian Neural Network (BNN) object detectorproviding reliable uncertain estimates is adapted to infer theinformativeness of the unlabeled data, in order to performactive learning.
Edit intent: fluency
Original:  Toachieve this, a Bayesian Neural Network (BNN) object detectorproviding reliable uncertain estimates is adapted to infer theinformativeness of the unlabeled data, in order to performactive learning.
Revised:  Toachieve this, a Bayesian Neural Network (BNN) object detector providing reliable uncertain estimates is adapted to infer theinformativeness of the unlabeled data, in order to performactive learning.
Edit intent: fluency
Original:  Toachieve this, a Bayesian Neural Network (BNN) object detectorproviding reliable uncertain estimates is adapted to infer theinformativeness of the unlabeled data, in order to performactive learning.
Revised:  Toachieve this, a Bayesian Neural Network (BNN) object detectorproviding reliable uncertain estimates is adapted to infer the informativeness of the unlabeled data, in order to performactive learning.
Edit intent: fluency
Original:  Toachieve this, a Bayesian Neural Network (BNN) object detectorproviding reliable uncertain estimates is adapted to infer theinformativeness of the unlabeled data, in order to performactive learning.
Revised:  Toachieve this, a Bayesian Neural Network (BNN) object detectorproviding reliable uncertain estimates is adapted to infer theinformativeness of the unlabeled data, in order to perform active learning.
Edit intent: fluency
Original:  In our experiments on two object detectiondata sets, we show that the labeling effort required to bridge thereality gap can be reduced to a small amount.
Revised:  In our experiments on two object detection data sets, we show that the labeling effort required to bridge thereality gap can be reduced to a small amount.
Edit intent: fluency
Original:  In our experiments on two object detectiondata sets, we show that the labeling effort required to bridge thereality gap can be reduced to a small amount.
Revised:  In our experiments on two object detectiondata sets, we show that the labeling effort required to bridge the reality gap can be reduced to a small amount.
Edit intent: fluency
Original:  Furthermore, wedemonstrate the practical effectiveness of this idea in a graspingtask on an assistive robot.
Revised:  Furthermore, we demonstrate the practical effectiveness of this idea in a graspingtask on an assistive robot.
Edit intent: fluency
Original:  Furthermore, wedemonstrate the practical effectiveness of this idea in a graspingtask on an assistive robot.
Revised:  Furthermore, wedemonstrate the practical effectiveness of this idea in a grasping task on an assistive robot.
Edit intent: fluency
Original:  Using cellular calcium dynamics as an example of this class of ODE-flux boundary interface problems  we prove the existence, uniqueness and boundedness of the solutions by applying comparison theorem, fundamental solution of the parabolic operator and a strategy used in Picard's existence theorem.
Revised:  Using the calcium model as an example of this class of ODE-flux boundary interface problems we prove the existence, uniqueness and boundedness of the solutions by applying comparison theorem, fundamental solution of the parabolic operator and a strategy used in Picard's existence theorem.
Edit intent: clarity
Original:  Using cellular calcium dynamics as an example of this class of ODE-flux boundary interface problems  we prove the existence, uniqueness and boundedness of the solutions by applying comparison theorem, fundamental solution of the parabolic operator and a strategy used in Picard's existence theorem.
Revised:  Using cellular calcium dynamics as an example of this class of ODE-flux boundary interface problems , we prove the existence, uniqueness and boundedness of the solutions by applying comparison theorem, fundamental solution of the parabolic operator and a strategy used in Picard's existence theorem.
Edit intent: fluency
Original:  Using cellular calcium dynamics as an example of this class of ODE-flux boundary interface problems  we prove the existence, uniqueness and boundedness of the solutions by applying comparison theorem, fundamental solution of the parabolic operator and a strategy used in Picard's existence theorem.
Revised:  Using cellular calcium dynamics as an example of this class of ODE-flux boundary interface problems we prove the existence, uniqueness and boundedness of the solution by applying comparison theorem, fundamental solution of the parabolic operator and a strategy used in Picard's existence theorem.
Edit intent: fluency
Original: This paper proposes a stable volume and its variant, a stable sub-volume  for more reliable data analysis using persistent homology.
Revised: This paper proposes a stable volume and a stable volume variant, referred to as a stable sub-volume for more reliable data analysis using persistent homology.
Edit intent: clarity
Original: This paper proposes a stable volume and its variant, a stable sub-volume  for more reliable data analysis using persistent homology.
Revised: This paper proposes a stable volume and its variant, a stable sub-volume , for more reliable data analysis using persistent homology.
Edit intent: fluency
Original:  In the previous research, an optimal cycle and similar ideas are proposed to identify the homological structure corresponding to each birth-death pair in a persistence diagram.
Revised:  In prior research, an optimal cycle and similar ideas are proposed to identify the homological structure corresponding to each birth-death pair in a persistence diagram.
Edit intent: fluency
Original:  In the previous research, an optimal cycle and similar ideas are proposed to identify the homological structure corresponding to each birth-death pair in a persistence diagram.
Revised:  In the previous research, an optimal cycle and similar ideas have been proposed to identify the homological structure corresponding to each birth-death pair in a persistence diagram.
Edit intent: fluency
Original:  It is helpful for data analysis using persistent homology .
Revised:  While this is helpful for data analysis using persistent homology .
Edit intent: coherence
Original:  We implement stable volumes and sub-volumes on HomCloud, persistent homology based data analysis software .
Revised:  We implement stable volumes and sub-volumes on HomCloud, a data analysis software .
Edit intent: clarity
Original: The presence of internal feedback pathways ( IFP ) is an ubiquitous yet unexplained phenomenon in the brain.
Revised: The presence of internal feedback pathways ( IFPs ) is an ubiquitous yet unexplained phenomenon in the brain.
Edit intent: clarity
Original:  Motivated by experimental observations on 1) motor-related signals in visual areas, and 2) massively distributed processing in the brain, we approach this problem from a sensorimotor standpoint and make use of distributed optimal controllers to explain IFP .
Revised:  Motivated by experimental observations on 1) motor-related signals in visual areas, and 2) massively distributed processing in the brain, we approach this problem from a sensorimotor standpoint and make use of distributed optimal controllers to explain IFPs .
Edit intent: fluency
Original:  Based on the SLS controller, we make qualitative theoretical predictions about IFP that has strong alignment with experimental and imaging studies.
Revised:  Based on the SLS controller, we make qualitative theoretical predictions about IFPs that has strong alignment with experimental and imaging studies.
Edit intent: clarity
Original:  this `mesocircuit' requires extreme amounts of IFP and memory for proper function.
Revised:  this `mesocircuit' requires extreme amounts of IFPs and memory for proper function.
Edit intent: fluency
Original:  This is the first theory that can replicate the massive amounts of IFP in the brain purely from a priori principles, providing a new and promising theoretical basis upon which we can build to better understand the inner workings of the brain.
Revised:  This is the first theory that can replicate the massive amounts of IFPs in the brain purely from a priori principles, providing a new and promising theoretical basis upon which we can build to better understand the inner workings of the brain.
Edit intent: fluency
Original:  However, we note that there exists a significant gap between the objective forms of model pre-training and fine-tuning, resulting in a need for quantities of labeled data to stimulate the visual grounding capability of VL-PTMs for downstream tasks.
Revised:  However, we note that there exists a significant gap between the objective forms of model pre-training and fine-tuning, resulting in a need for large amounts of labeled data to stimulate the visual grounding capability of VL-PTMs for downstream tasks.
Edit intent: clarity
Original:  In this way, our prompt tuning approach enables strong few-shot and even zero-shot visual grounding capabilities of VL-PTMs. Comprehensive experimental results show that prompt tuned VL-PTMs outperform their fine-tuned counterparts by a large margin (e.g., 17.3\% absolute accuracy improvement, and 73.8\% relative standard deviation reduction on average with one shot in RefCOCO evaluation).
Revised:  In this way, CPT enables strong few-shot and even zero-shot visual grounding capabilities of VL-PTMs. Comprehensive experimental results show that prompt tuned VL-PTMs outperform their fine-tuned counterparts by a large margin (e.g., 17.3\% absolute accuracy improvement, and 73.8\% relative standard deviation reduction on average with one shot in RefCOCO evaluation).
Edit intent: clarity