wanyu/IteraTeR_v2 huggingface dataset:
Original:  The most important in studying random walk on networks is to measure a structural parameter called mean first-passage time , denote by \mathcal{F} . As known, the commonly-utilized methods for determining%DIFDELCMD < \mathcal{F} %%%  are mainly based on Laplacian spectra on networks under consideration.
Revised:  The most important in studying random walk on networks is to measure a structural parameter called mean first-passage time , denote by \mathcal{F} . As known, the commonly-utilized methods for determining%DIFDELCMD < \mathcal{F} %%% for random walk on networks are mainly based on Laplacian spectra on networks under consideration.
Edit intent: clarity
Original:  On the other hand , methods of this type can become prohibitively complicated and even fail to work in the worst case where the corresponding Laplacian matrix is difficult to describe in the first place.
Revised:  On the other hand , methods of this type can become prohibitively complicated and even fail to work when the Laplacian matrix of network under consideration is difficult to describe in the first place.
Edit intent: clarity
Original:  In this paper, we will propose an effective approach to addressing this kind of issues on some tree networks, such as, Vicsek fractal, with intriguing structural properties, for instance, fractal feature.
Revised:  In this paper, we propose an effective approach to addressing this kind of issues on some tree networks, such as, Vicsek fractal, with intriguing structural properties, for instance, fractal feature.
Edit intent: coherence
Original: \mathcal{F}  To this end, we first build up a general formula between Wiener index , denoted by W , and \mathcal{F} on a tree.
Revised: \mathcal{F} To this end, we first build up a general formula between Wiener index \mathcal{W and \mathcal{F} on a tree.
Edit intent: clarity
Original:   As an immediate consequence, the previously published results in deterministic cases are easily covered by formulae established in this paper  . Most importantly, our approach is more manageable than many other methods including spectral technique in situations considered herein.
Revised:  As an immediate consequence, the previously published results upon deterministic cases are easily covered by formulae established in this paper . Most importantly, our approach is more manageable than many other methods including spectral technique in situations considered herein.
Edit intent: fluency
Original:   As an immediate consequence, the previously published results in deterministic cases are easily covered by formulae established in this paper  . Most importantly, our approach is more manageable than many other methods including spectral technique in situations considered herein.
Revised:  As an immediate consequence, the previously published results in deterministic cases are thoroughly covered by formulas established in this paper . Most importantly, our approach is more manageable than many other methods including spectral technique in situations considered herein.
Edit intent: clarity
Original: The ability to preserve local geometry of highly nonlinear manifolds in high dimensional spaces and properly unfold them into lower dimensional hyperplanes is the key to the success of manifold computing, nonlinear dimensionality reduction (NLDR) and visualization.
Revised: Nonlinear dimensionality reduction (NLDR) and visualization.
Edit intent: coherence
Original:  This paper proposes a novel method, called elastic locally isometric smoothness (ELIS {\em   ), to empower deep neural networks with such an ability.
Revised:  This paper proposes a novel method, called {\em ), to empower deep neural networks with such an ability.
Edit intent: clarity
Original: Nonlinear dimensionality reduction (NLDR) plays an important role in feature extraction and visualization of high dimensional data .
Revised: Nonlinear dimensionality reduction (NLDR) for data exploration. However, existing methods can often fail to preserve geometric, topological and/or distributional structures of data .
Edit intent: clarity
Original:  Extensive experiments , comparisons, and ablation studies demonstrate that DMT can deliver results superior to UMAP and t-SNE, and other leading manifold-based NLDR methods  .
Revised:  Extensive experiments , comparisons, and ablation studies demonstrate that DMT networks outperform existing leading manifold-based NLDR methods .
Edit intent: clarity
Original:  Our guarantees hold, in fact, for the celebrated Chow-Liu [1968] algorithm, using the plug-in estimator for  mutual information.
Revised:  Our guarantees hold, in fact, for the celebrated Chow-Liu [1968] algorithm, using the plug-in estimator for estimating mutual information.
Edit intent: clarity
Original: 3D free-form surfaces with high aesthetical and functional requirements are widely used in automotive and aerospace industry.
Edit intent: coherence
Original:  Geometric and dimensional variations of these free-form surfaces caused by inevitable uncertainties in the manufacturing process often leads to product quality issues.
Revised:  Geometric and dimensional variations in objects are caused by inevitable uncertainties in the manufacturing process often leads to product quality issues.
Edit intent: clarity
Original:  Geometric and dimensional variations of these free-form surfaces caused by inevitable uncertainties in the manufacturing process often leads to product quality issues.
Revised:  Geometric and dimensional variations of these free-form surfaces caused by inevitable uncertainties in manufacturing processes and often lead to product quality issues.
Edit intent: coherence
Original:  Failing to model the effect of non-ideal parts , i.e., parts with geometric and dimensional errors , during design inhibits the ability to predict such quality issues . A major challenge for accurate modelling of non-ideal parts during early design phase is the limited availability of data and the ability to effectively utilise the historical data from similar parts.
Revised:  Failing to model the effect of non-ideal parts , i.e., geometric and dimensional errors , during design inhibits the ability to predict such quality issues . A major challenge for accurate modelling of non-ideal parts during early design phase is the limited availability of data and the ability to effectively utilise the historical data from similar parts.
Edit intent: clarity
Original:  Failing to model the effect of non-ideal parts , i.e., parts with geometric and dimensional errors , during design inhibits the ability to predict such quality issues . A major challenge for accurate modelling of non-ideal parts during early design phase is the limited availability of data and the ability to effectively utilise the historical data from similar parts.
Revised:  Failing to model the effect of non-ideal parts , i.e., parts with geometric and dimensional errors of parts, early during design phase inhibits the ability to predict such quality issues . A major challenge for accurate modelling of non-ideal parts during early design phase is the limited availability of data and the ability to effectively utilise the historical data from similar parts.
Edit intent: clarity
Original:  Overcoming this challenge a novel morphing Gaussian Random Field ( mGRF ) methodology for  shape error modelling of 3D free-form surfaces during the early design stage is presented in this paper.
Revised:  Overcoming this challenge a novel morphing Gaussian Random Field ( MGRF ) methodology for shape error modelling of 3D free-form surfaces during the early design stage is presented in this paper.
Edit intent: fluency
Original:  The developed mGRF methodology is demonstrated, compared with state-of-art methodologies, and validated using a sport-utility-vehicle door part .
Revised:  Practical applications of developed MGRF methodology and its advantages are demonstrated using sport-utility-vehicle door part .
Edit intent: clarity
Original:  The developed mGRF methodology is demonstrated, compared with state-of-art methodologies, and validated using a sport-utility-vehicle door part .
Revised:  The developed mGRF methodology is demonstrated, compared with state-of-art methodologies, and validated using a sport-utility-vehicle door parts .
Edit intent: fluency
Original:  In this paper we will use properties of logarithmic functions to propose a pair of custom activation functions which can translate products into linear expression and learn using backpropagation.
Revised:  In this paper we will use properties of logarithmic functions to propose a pair of activation functions which can translate products into linear expression and learn using backpropagation.
Edit intent: clarity
Original:  Actris combines the power of modern concurrent separation logics with a first-class protocol mechanism---based on session types---for reasoning about message passing in the presence of other concurrency paradigms.
Revised:  Actris combines the power of modern concurrent separation logics with a first-class protocol mechanism -- based on session types -- for reasoning about message passing in the presence of other concurrency paradigms.
Edit intent: fluency
Original:  Moreover, it extends Actris to Actris 2.0 with a notion of subprotocols---based on session-type subtyping---that permits additional flexibility when composing channel endpoints, and that takes full advantage of the asynchronous semantics of message passing in Actris.
Revised:  Moreover, it extends Actris to Actris 2.0 with a notion of subprotocols -- based on session-type subtyping---that permits additional flexibility when composing channel endpoints, and that takes full advantage of the asynchronous semantics of message passing in Actris.
Edit intent: fluency
Original:  Moreover, it extends Actris to Actris 2.0 with a notion of subprotocols---based on session-type subtyping---that permits additional flexibility when composing channel endpoints, and that takes full advantage of the asynchronous semantics of message passing in Actris.
Revised:  Moreover, it extends Actris to Actris 2.0 with a notion of subprotocols---based on session-type subtyping -- that permits additional flexibility when composing channel endpoints, and that takes full advantage of the asynchronous semantics of message passing in Actris.
Edit intent: fluency
Original:  Soundness of Actris 2.0 is proved using a model of its protocol mechanism in the Iris framework.
Revised:  Soundness of Actris 2.0 is proven using a model of its protocol mechanism in the Iris framework.
Edit intent: fluency
Original:  For the first time, we develop a novel method for quantifying biased associations between representations of social concepts and attributes in images.
Revised:  We develop a novel method for quantifying biased associations between representations of social concepts and attributes in images.
Edit intent: clarity
Original:  We replicate 8 of 15 documented human biases from social psychology, from the innocuous, as with insects and flowers, to the potentially harmful, as with race and gender.
Revised:  We replicate 8 previously documented human biases from social psychology, from the innocuous, as with insects and flowers, to the potentially harmful, as with race and gender.
Edit intent: clarity
Original: Depression is a large-scale mental health problem and a challenging area for machine learning researchers in terms of the detection of depression.
Revised: Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression.
Edit intent: coherence
Original:  Datasets such as the Distress Analysis Interview Corpus - Wizard of Oz  have been created to aid research in this area.
Revised:  Datasets such as Distress Analysis Interview Corpus - Wizard of Oz have been created to aid research in this area.
Edit intent: fluency
Original:  In this paper we examine gender bias in the DAIC-WOZ dataset using audio-based deep neural networks .
Revised:  In this paper we examine gender bias in the DAIC-WOZ dataset .
Edit intent: clarity
Original: Many different simulation methods for Stokes flow problems involve a common computationally intense task---the summation of a kernel function over O(N^2) pairs of points.
Revised: Many different simulation methods for Stokes flow problems involve a common computationally intense task -- the summation of a kernel function over O(N^2) pairs of points.
Edit intent: fluency
Original:  Here we present  the Kernel Aggregated Fast Multipole Method (KAFMM), which overcomes these drawbacks by allowing different kernel functions to be used for specific stages of octree traversal.
Revised:  Here we present the Kernel Aggregated Fast Multipole Method (KAFMM), because it uses different kernel functions to be used for specific stages of octree traversal.
Edit intent: coherence
Original:  Here we present  the Kernel Aggregated Fast Multipole Method (KAFMM), which overcomes these drawbacks by allowing different kernel functions to be used for specific stages of octree traversal.
Revised:  Here we present the Kernel Aggregated Fast Multipole Method (KAFMM), which overcomes these drawbacks by allowing different kernel functions at different stages of octree traversal.
Edit intent: clarity
Original:  The package is designed to be ready-to-use as well as being readily extensible to additional kernels . Massive parallelism is supported with mixed OpenMP and MPI .
Revised:  The package is designed to be ready-to-use as well as being readily extensible to additional kernels .
Edit intent: coherence
Original:  Next, we observed that the proposed PFP loss was more strongly correlated with the perceptual evaluation metrics than point-wise and signal-level losses, thus achieving higher scores for standardized quality and intelligibility evaluation metrics in the Voice Bank--DEMAND dataset.
Revised:  Next, we observed that the proposed PFP loss was more strongly correlated with the perceptual evaluation metrics than point-wise and signal-level losses, thus achieving higher scores for standardized quality and intelligibility evaluation metrics in the Voice Bank-DEMAND dataset.
Edit intent: fluency
Original:  Next, we observed that the proposed PFP loss was more strongly correlated with the perceptual evaluation metrics than point-wise and signal-level losses , thus achieving higher scores for standardized quality and intelligibility evaluation metrics in the Voice Bank-DEMAND dataset.
Revised:  Next, we observed that the proposed PFP loss was more strongly correlated with the perceptual evaluation metrics , as compared to signal-level losses , thus achieving higher scores for standardized quality and intelligibility evaluation metrics in the Voice Bank-DEMAND dataset.
Edit intent: clarity
Original:  Next, we observed that the proposed PFP loss was more strongly correlated with the perceptual evaluation metrics than point-wise and signal-level losses , thus achieving higher scores for standardized quality and intelligibility evaluation metrics in the Voice Bank-DEMAND dataset.
Revised:  Next, we observed that the proposed PFP loss was more strongly correlated with the perceptual evaluation metrics than point-wise and signal-level losses , thus achieving higher scores for standardized quality and intelligibility evaluations on the Voice Bank-DEMAND dataset.
Edit intent: clarity
Original: Despite the importance of short-term memory in cognitive function, how the input information is encoded and sustained in neural activity dynamics remains elusive.
Revised: Despite the significance of short-term memory in cognitive function, how the input information is encoded and sustained in neural activity dynamics remains elusive.
Edit intent: clarity
Original: Despite the importance of short-term memory in cognitive function, how the input information is encoded and sustained in neural activity dynamics remains elusive.
Revised: Despite the importance of short-term memory in cognitive function, the process of encoding and sustaining the input information in neural activity dynamics remains elusive.
Edit intent: clarity
Original:  Here, by training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristic of the short-term memory mechanism was obtained in which the input information was encoded in the amplitude of transient oscillation , rather than the stationary neural activities.
Revised:  Herein, we unveiled the significance of transient neural dynamics to short-term memory. By training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristic of the short-term memory mechanism was obtained in which the input information was encoded in the amplitude of transient oscillation , rather than the stationary neural activities.
Edit intent: clarity
Original:  Here, by training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristic of the short-term memory mechanism was obtained in which the input information was encoded in the amplitude of transient oscillation , rather than the stationary neural activities.
Revised:  Here, by training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristics of the short-term memory mechanism was obtained in which the input information was encoded in the amplitude of transient oscillation , rather than the stationary neural activities.
Edit intent: fluency
Original:  Here, by training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristic of the short-term memory mechanism was obtained in which the input information was encoded in the amplitude of transient oscillation , rather than the stationary neural activities.
Revised:  Here, by training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristic of the short-term memory mechanism were obtained in which the input information was encoded in the amplitude of transient oscillation , rather than the stationary neural activities.
Edit intent: fluency
Original:  Here, by training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristic of the short-term memory mechanism was obtained in which the input information was encoded in the amplitude of transient oscillation , rather than the stationary neural activities.
Revised:  Here, by training recurrent neural networks to short-term memory tasks and analyzing the dynamics, the characteristic of the short-term memory mechanism was obtained in which the input information was encoded in the amplitude of transient oscillations , rather than the stationary neural activities.
Edit intent: fluency
Original:  This transient orbit was attracted to a slow manifold, which allowed for the discarding of irrelevant information.
Revised:  This transient trajectory was attracted to a slow manifold, which allowed for the discarding of irrelevant information.
Edit intent: clarity
Original:  This transient orbit was attracted to a slow manifold, which allowed for the discarding of irrelevant information.
Revised:  This transient orbit was attracted to a slow manifold, which permitted the discarding of irrelevant information.
Edit intent: fluency
Original:  The generality of the result and its relevance to neural information processing were discussed .
Revised:  The generality of the result and its relevance to neural information processing in general .
Edit intent: clarity
Original:  In this paper, we utilize self-supervised pre-training to stabilize the label permutations.
Revised:  In this paper, we propose to perform self-supervised pre-training to stabilize the label permutations.
Edit intent: clarity
Original:  In this work, we propose a novel approximation scheme of the Laplace--Beltrami operator on point clouds and utilize it for developing a free-boundary conformal parameterization method for disk-type point clouds  .
Revised:  In this work, we develop a free-boundary conformal parameterization method for disk-type point clouds .
Edit intent: clarity
Original:  In this paper , we focus on the case where the discontinuities arise from distinct sub-patterns, each defined on different parts of the input space.
Revised:  This paper focuses on the case where the discontinuities arise from distinct sub-patterns, each defined on different parts of the input space.
Edit intent: coherence
Original:  Learning such a function involves identifying the partition of the input space, where each part describes a single continuous sub-pattern of the target function, and then uniformly approximating each of these sub-patterns individually.
Edit intent: coherence
Original:  These include a guarantee that its partition component can approximate any partition of the input space in the upper-Kuratowski sense and a guarantee that our architecture is dense in a large non-separable space of discontinuous functions . Quantitative approximation rates and guarantees for the learnability of a performance-optimizing partition are provided.
Revised:  These include a guarantee that its partition component can approximate any partition of the input space in the upper-Kuratowski sense and a guarantee that our architecture is dense in a large non-separable space of discontinuous functions . 
Edit intent: coherence
Original:  In this paper, we propose a generalized form of the connectionist temporal classification (CTC) objective that accepts a graph representation of the training targets .
Revised:  In this paper, we propose a generalized form of the connectionist temporal classification (CTC) objective that accepts a graph representation of the training labels .
Edit intent: clarity
Original: The use of mobile applications apps and GPS service on smartphones for transportation management applications has enabled the new " on-demand mobility" service, where the transportation supply is following the users' schedule and routes.
Revised: In recent years, with the advancements in information and communication technology, different emerging on-demand mobility" service, where the transportation supply is following the users' schedule and routes.
Edit intent: clarity
Original:  In this study,  Random Forest (RF), Bagging, Artificial Neural Network (ANN) , and Deep Neural Network (DNN) machine learning algorithms were adopted to develop a pickup demand model (trip generation) and a trip demand model (trip distribution model) for BellevilleODT service based on the dissemination areas'demographic characteristics and the existing trip characteristics.
Revised:  In this study, Random Forest (RF), Bagging, Artificial Neural Network (ANN) and Deep Neural Network (DNN) machine learning algorithms were adopted to develop a pickup demand model (trip generation) and a trip demand model (trip distribution model) for BellevilleODT service based on the dissemination areas'demographic characteristics and the existing trip characteristics.
Edit intent: fluency
Original: In recent years, with the advancements in information and communication technology, different emerging on-demand shared mobility services have been introduced as innovative solutions in the low-density areas, including on-demand transit (ODT), mobility-on-demand (MOD) transit, and crowdsourced mobility services.
Revised: In recent years, with the advancements in information and communication technology, different emerging on-demand shared mobility services have been introduced as innovative solutions in the low-density areas, including on-demand transit (ODT), mobility on-demand (MOD) transit, and crowdsourced mobility services.
Edit intent: fluency
Original:  In this study, we developed trip production and distribution models for ODT services at Dissemination Areas (DA) level using four machine learning algorithms: Random Forest (RF), Bagging, Artificial Neural Network (ANN) and Deep Neural Network (DNN).
Revised:  In this study, we developed trip production and distribution models for ODT services at Dissemination areas (DA) level using four machine learning algorithms: Random Forest (RF), Bagging, Artificial Neural Network (ANN) and Deep Neural Network (DNN).
Edit intent: fluency