wanyu/IteraTeR_full_sent huggingface dataset:
Original:  On the other hand, much less is known in the multivariate case, where the domain of the filter is  in \mathbb R^d instead of \mathbb R. The only available result in this setting ~\mbox{%DIFAUXCMD Munch2016  topological spaces and cannot be used as is for finite metric spaces representing data, such as point clouds and distance matrices.
Revised:  On the other hand, much less is known in the multivariate case, where the domain of the filter is in this setting ~\mbox{%DIFAUXCMD Munch2016 topological spaces and cannot be used as is for finite metric spaces representing data, such as point clouds and distance matrices.
Edit intent: clarity
Original:  The only available result in this setting ~\mbox{%DIFAUXCMD Munch2016  topological spaces and cannot be used as is for finite metric spaces representing data, such as point clouds and distance matrices.
Revised:  The only available result in this setting DMW17, MW16 topological spaces and cannot be used as is for finite metric spaces representing data, such as point clouds and distance matrices.
Edit intent: meaning-changed
Original:  The only available result in this setting ~\mbox{%DIFAUXCMD Munch2016  topological spaces and cannot be used as is for finite metric spaces representing data, such as point clouds and distance matrices.
Revised:  The only available result in this setting ~\mbox{%DIFAUXCMD Munch2016 can only handle continuous topological spaces and cannot be used as is for finite metric spaces representing data, such as point clouds and distance matrices.
Edit intent: meaning-changed
Original:  In this article, we present an approximation result for the Reeb space in the multivariate case using a Mapper-based estimator, which is a slight modification of the usual Mapper construction . Moreover, our approximation is stated with respect to a pseudometric that is an extension of the usual%DIFDELCMD < {\em %%% interleaving distance between persistence modules~\mbox{%DIFAUXCMD \cite{Chazal2016}\hspace{0pt}%DIFAUXCMD . Finally, we apply our results to the case }  where the filter function used to compute the Mapper is estimated from the data.
Revised:  In this article, we present an approximation result for the Reeb space in the multivariate case using a Mapper-based estimator, which is a slight modification of the usual Mapper construction %DIFDELCMD < {\em %%% interleaving distance between persistence modules~\mbox{%DIFAUXCMD \cite{Chazal2016}\hspace{0pt}%DIFAUXCMD . Finally, we apply our results to the case } where the filter function used to compute the Mapper is estimated from the data.
Edit intent: coherence
Original:  Moreover, our approximation is stated with respect to a pseudometric that is an extension of the usual%DIFDELCMD < {\em %%% interleaving distance between persistence modules~\mbox{%DIFAUXCMD \cite{Chazal2016}\hspace{0pt}%DIFAUXCMD . Finally, we apply our results to the case }  where the filter function used to compute the Mapper is estimated from the data.
Revised:  Moreover, our approximation is stated with respect to a pseudometric that is an extension of the usual%DIFDELCMD < {\em %%% between persistence modules~\mbox{%DIFAUXCMD \cite{Chazal2016}\hspace{0pt}%DIFAUXCMD . Finally, we apply our results to the case } where the filter function used to compute the Mapper is estimated from the data.
Edit intent: clarity
Original:  Moreover, our approximation is stated with respect to a pseudometric that is an extension of the usual%DIFDELCMD < {\em %%% interleaving distance between persistence modules~\mbox{%DIFAUXCMD \cite{Chazal2016}\hspace{0pt}%DIFAUXCMD . Finally, we apply our results to the case }  where the filter function used to compute the Mapper is estimated from the data.
Revised:  Moreover, our approximation is stated with respect to a pseudometric that is an extension of the usual%DIFDELCMD < {\em %%% interleaving distance }\hspace{0pt}%DIFAUXCMD . Finally, we apply our results to the case } where the filter function used to compute the Mapper is estimated from the data.
Edit intent: clarity
Original:  Moreover, our approximation is stated with respect to a pseudometric that is an extension of the usual%DIFDELCMD < {\em %%% interleaving distance between persistence modules~\mbox{%DIFAUXCMD \cite{Chazal2016}\hspace{0pt}%DIFAUXCMD . Finally, we apply our results to the case }  where the filter function used to compute the Mapper is estimated from the data.
Revised:  Moreover, our approximation is stated with respect to a pseudometric that is an extension of the usual%DIFDELCMD < {\em %%% interleaving distance between persistence modules~\mbox{%DIFAUXCMD \cite{Chazal2016}\hspace{0pt}%DIFAUXCMD . Finally, we apply our results to the case } and we give risk bounds for estimating the Reeb space using this estimator. Our approach applies in particular to the setting where the filter function used to compute the Mapper is estimated from the data.
Edit intent: meaning-changed
Original:  Moreover, our approximation is stated with respect to a pseudometric that is an extension of the usual%DIFDELCMD < {\em %%% interleaving distance between persistence modules~\mbox{%DIFAUXCMD \cite{Chazal2016}\hspace{0pt}%DIFAUXCMD . Finally, we apply our results to the case }  where the filter function used to compute the Mapper is estimated from the data. We ] provide applications of this setting in statistics and machine learning and probability for different kinds of target filters, as well as numerical experiments that demonstrate the relevance of our approach .
Revised:  Moreover, our approximation is stated with respect to a pseudometric that is an extension of the usual%DIFDELCMD < {\em %%% interleaving distance between persistence modules~\mbox{%DIFAUXCMD \cite{Chazal2016}\hspace{0pt}%DIFAUXCMD . Finally, we apply our results to the case } where the filter function used to compute Mapper is also estimated from data, such as the eigenfunctions of PCA. Our results are given with respect to the Gromov-Hausdorff distance, computed with specific filter-based pseudometrics for Mappers and Reeb spaces defined in ] provide applications of this setting in statistics and machine learning and probability for different kinds of target filters, as well as numerical experiments that demonstrate the relevance of our approach .
Edit intent: meaning-changed
Original: The downward trend in Arctic sea ice is a key factor determining the pace and intensity of future global climate change; moreover, declines in sea ice can have a wide range of additional environmental and economic consequences  . Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century.
Revised: The downward trend in the amount of Arctic sea ice has a wide range of additional environmental and economic consequences . Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century.
Edit intent: clarity
Original:  moreover, declines in sea ice can have a wide range of additional environmental and economic consequences  . Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century.
Revised:  moreover, declines in sea ice can have a wide range of environmental and economic consequences . Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century.
Edit intent: clarity
Original:  moreover, declines in sea ice can have a wide range of additional environmental and economic consequences  . Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century.
Revised:  moreover, declines in sea ice can have a wide range of additional environmental and economic consequences including important effects on the pace and intensity of global climate change . Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century.
Edit intent: meaning-changed
Original: Aspect-term level sentiment analysis (ATSA) is a fine-grained task in sentiment classification. It aims at extracting and summarizing the sentiment polarity towards a given aspect phrase from a sentence. Most existing studies combined various neural network models with a delicately carved attention mechanism to generate refined representations of sentences for better predictions.
Revised: Aspect-term level sentiment analysis (ATSA) approaches combined various neural network models with a delicately carved attention mechanism to generate refined representations of sentences for better predictions.
Edit intent: clarity
Original:  However, they were inadequate to capture correlations between aspects and sentiments. Moreover , the annotated aspect term might be unavailable in real-world scenarios which may challenge the existing methods to give correct forecasting .
Revised:  In these methods, aspect terms are always provided in both training and testing process which may degrade aspect-level analysis into sentence-level prediction. However , the annotated aspect term might be unavailable in real-world scenarios which may challenge the existing methods to give correct forecasting .
Edit intent: meaning-changed
Original:  In this paper, we propose a capsule network based model named CAPSAR (CAPsule network with Sentiment-Aspect Reconstruction) to improve aspect-term level sentiment analysis.
Revised:  In this paper, we aim to improve ATSA by discovering the potential aspect terms of the predicted sentiment polarity when the aspect terms of a test sentence are unknown. We access this goal by proposing a capsule network based model named CAPSAR (CAPsule network with Sentiment-Aspect Reconstruction) to improve aspect-term level sentiment analysis.
Edit intent: meaning-changed
Original:  In this paper, we propose a capsule network based model named CAPSAR (CAPsule network with Sentiment-Aspect Reconstruction) to improve aspect-term level sentiment analysis. CAPSAR adopts a hierarchical structure of capsules and learns interactive patterns between aspects and sentiments through packaged sentiment-aspect reconstruction  . Capsules in CAPSAR are capable of communicating with other capsulesthrough a sharing-weight routing algorithm.
Revised:  In this paper, we propose a capsule network based model named CAPSAR . In CAPSAR, sentiment categories are denoted by capsules and aspect term information is injected into sentiment capsules through a sentiment-aspect reconstruction . Capsules in CAPSAR are capable of communicating with other capsulesthrough a sharing-weight routing algorithm.
Edit intent: clarity
Original:  CAPSAR adopts a hierarchical structure of capsules and learns interactive patterns between aspects and sentiments through packaged sentiment-aspect reconstruction  . Capsules in CAPSAR are capable of communicating with other capsulesthrough a sharing-weight routing algorithm.
Revised:  CAPSAR adopts a hierarchical structure of capsules and learns interactive patterns between aspects and sentiments through packaged sentiment-aspect reconstruction procedure during the training. As a result, coherent patterns between aspects and sentimental expressions are encapsulated by these sentiment capsules . Capsules in CAPSAR are capable of communicating with other capsulesthrough a sharing-weight routing algorithm.
Edit intent: meaning-changed
Original:  Experiments on three ATSA benchmarks demonstrate the superiority of our model, and CAPSAR can detect the potential aspect terms from sentences by de-capsulizing the vectors in capsules when aspect terms are unknown .
Revised:  Experiments on three ATSA benchmarks demonstrate the superiority of our model, and CAPSAR can detect the potential aspect terms from test sentence when only feeding the sentence to the model. Meanwhile, the proposed CAPSAR can clearly outperform SOTA methods in standard ATSA tasks .
Edit intent: meaning-changed
Original: This paper focuses on the development of a new class of diffusion processes that allows for direct and dynamic modelling of quantile diffusions.
Revised: We propose a novel approach for the construction of quantile processes governing the stochastic dynamics of quantiles in continuous time. Two classes of quantile diffusions are identified: The first, which we largely focus on, features a random quantile level and allows for direct and dynamic modelling of quantile diffusions.
Edit intent: meaning-changed
Original: This paper focuses on the development of a new class of diffusion processes that allows for direct and dynamic modelling of quantile diffusions. We constructed quantile diffusion processes by transforming each marginal of a given univariate diffusion process under a composite map consisting of a distribution function and quantile function, which in turn produces the marginals of the resulting quantile process.
Revised: This paper focuses on the development of a new class of diffusion processes that allows for direct interpretation of model parameters such as skewness and kurtosis. The second type are function-valued quantile diffusions and are driven by stochastic parameter processes, which determine the entire quantile function at each point in time. The quantile processes are obtained by transforming the marginals of a diffusion process under a composite map consisting of a distribution function and quantile function, which in turn produces the marginals of the resulting quantile process.
Edit intent: meaning-changed
Original:  We constructed quantile diffusion processes by transforming each marginal of a given univariate diffusion process under a composite map consisting of a distribution function and quantile function, which in turn produces the marginals of the resulting quantile process.
Revised:  We constructed quantile diffusion processes by transforming each marginal of a given univariate diffusion process under a composite map consisting of a distribution and a quantile function. Such maps, analogous to rank transmutation maps, produce the marginals of the resulting quantile process.
Edit intent: meaning-changed
Original:  The transformation allows for the moments of the underlying process to be directly interpreted with regard to parameters of the transformation. For instance, skewness or kurtosis may be introduced to enable more realistic modelling of data such as financial asset returns, as well as the recycling of samples of the underlying process to make simulation of the transformed quantile process easier. We derive the stochastic differential equation satisfied by the quantile diffusion, and characterise the conditions under which strong and weak solutions exist, both in the general case and for the more specific Tukey g-h, g-transform and h-transform families of quantile diffusions .
Revised:  We discuss the relationship and differences between our approach and existing methods and characterisations of quantile processes in discrete and continuous time. As an example of an application of quantile diffusions, we show how measure distortions, often found in financial mathematics and actuarial science, may be induced .
Edit intent: clarity
Original:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and online datasets.
Revised:  Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and online datasets.
Edit intent: clarity
Original:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and online datasets.
Revised:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN) . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and online datasets.
Edit intent: clarity
Original:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and online datasets.
Revised:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state-of-the-art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and online datasets.
Edit intent: fluency
Original:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and online datasets.
Revised:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and two state-of-the-art self-calibrating algorithm on both offline and online datasets.
Edit intent: clarity
Original:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and online datasets.
Revised:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithms developed specifically for EMG-based deep network adaptation on both offline and online datasets.
Edit intent: meaning-changed
Original:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and online datasets.
Revised:  Consequently, this paper proposes tackling the especially challenging task of adapting to sEMG signals when multiple days have elapsed between each recording , by presenting SCADANN, a new, deep learning-based, self-calibrating algorithm . SCADANN is ranked against three state of the art domain adversarial algorithms and a multiple-vote self-calibrating algorithm on both offline and dynamic datasets.
Edit intent: clarity
Original:  Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals  when multiple days have elapsed between each recording  by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN).
Revised:  Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals , when multiple days have elapsed between each recording by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN).
Edit intent: fluency
Original:  Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals  when multiple days have elapsed between each recording  by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN).
Revised:  Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals when multiple days have elapsed between each recording , by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN).
Edit intent: fluency
Original:  SCADANN is ranked against three state-of-the-art domain adversarial algorithms and two state-of-the-art self-calibrating algorithms developed specifically for EMG-based deep network adaptation on both offline and dynamic datasets .
Revised:  SCADANN is compared with two state-of-the-art self-calibrating algorithms developed specifically for EMG-based deep network adaptation on both offline and dynamic datasets .
Edit intent: clarity
Original:  SCADANN is ranked against three state-of-the-art domain adversarial algorithms and two state-of-the-art self-calibrating algorithms developed specifically for EMG-based deep network adaptation on both offline and dynamic datasets .
Revised:  SCADANN is ranked against three state-of-the-art domain adversarial algorithms and two state-of-the-art self-calibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms) .
Edit intent: meaning-changed
Original:  Natural Language Processing (NLP) tools and frameworks have significantly contributed with solutions to the problems of extracting entities and relations and linking them to the related knowledge graphs. Albeit effective, the majority of existing tools are available for only one knowledge graph .
Revised:  Natural Language Processing (NLP) tools and frameworks have significantly contributed with solutions to the problems of extracting entities and relations and linking them to proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, still, there are limited tools to link knowledge within the text to Wikidata .
Edit intent: meaning-changed
Original:  In this paper, we present Falcon 2.0,  a rule-based tool capable of accurately mapping entities and relations in short texts to resources in both DBpedia and Wikidatafollowing the same approach in both cases.
Revised:  In this paper, we present Falcon 2.0, first joint entity, and relation linking tool over Wikidata. It receives a rule-based tool capable of accurately mapping entities and relations in short texts to resources in both DBpedia and Wikidatafollowing the same approach in both cases.
Edit intent: meaning-changed