wanyu/IteraTeR_full_sent huggingface dataset:
Original:  That both the D614G mutation and the K986P mutation increase the COVID-19 infectivity and why a large number of mutations, including those at interface residues, have not been selected in current SARS-CoV-2 pandemic are interpreted from the presented theory.
Revised:  That both the D614G mutation , the K986P mutation increase the COVID-19 infectivity and why a large number of mutations, including those at interface residues, have not been selected in current SARS-CoV-2 pandemic are interpreted from the presented theory.
Edit intent: fluency
Original:  That both the D614G mutation and the K986P mutation increase the COVID-19 infectivity and why a large number of mutations, including those at interface residues, have not been selected in current SARS-CoV-2 pandemic are interpreted from the presented theory.
Revised:  That both the D614G mutation and the K986P mutation and the new variants 501Y in current SARS-CoV-2 pandemic are interpreted from the presented theory.
Edit intent: meaning-changed
Original:  That both the D614G mutation and the K986P mutation increase the COVID-19 infectivity and why a large number of mutations, including those at interface residues, have not been selected in current SARS-CoV-2 pandemic are interpreted from the presented theory.
Revised:  That both the D614G mutation and the K986P mutation increase the COVID-19 infectivity and why a large number of mutations, including those at interface residues, have not been selected in current SARS-CoV-2 pandemic can be interpreted from the presented theory.
Edit intent: clarity
Original:  The evolution of coronavirus dependent on the alteration of conformation equilibrium is indicated. Finally, introduction of electric field to change the conformation potential barrier and how the conformation equilibriumdepends on temperature and humidity are briefly discussed .
Revised:  The comparison of the infectivity of SARS-CoV-2 with SARS-CoV-1 is made from the point of conformation equilibrium. Why the virus entrance takes priority in lower temperature and higher humidity is interpreted by the present theory. The conformational transition influenced by electromagnetic field is also discussed briefly .
Edit intent: meaning-changed
Original: This paper considers price volatility as the reason for description of the second-degree economic variables, trades and expectations } } aggregated during certain time interval {\Delta}.
Revised: This paper considers price volatility as the reason for description of the second-degree economic variables, trades and expectations }(p;t) that defines the mean price p(1;t), mean square price p(2;t), price volatility } aggregated during certain time interval {\Delta}.
Edit intent: meaning-changed
Original: This paper considers price volatility as the reason for description of the second-degree economic variables, trades and expectations } } aggregated during certain time interval {\Delta}.
Revised: This paper considers price volatility as the reason for description of the second-degree economic variables, trades and expectations } }p2(t)and all price n-th statistical moments p(n;t) as ratio of sums of n-th degree values C(n;t) and volumes U(n;t) of market trades aggregated during certain time interval {\Delta}.
Edit intent: meaning-changed
Original:  We call it - the second-order economic theory. The n-th degree products of costs and volumes of trades, performed by economic agents during interval { \Delta } determine price n-th statistical moments.
Revised:  The definition of the mean price p(1;t) coincides with definition of the volume weighted average price (VWAP) introduced at least 30 years ago. We show that price volatility { \Delta } determine price n-th statistical moments.
Edit intent: meaning-changed
Original:  The n-th degree products of costs and volumes of trades, performed by economic agents during interval { \Delta } determine price n-th statistical moments. First two price statistical moments define volatility . To model volatility one needs description of the squares of trades aggregated during interval \Delta%DIFDELCMD < }%%% . To describe price probability one needs all n-th statistical moments of price but that is almost impossible.
Revised:  The n-th degree products of costs and volumes of trades, performed by economic agents during interval { \Delta } \Delta%DIFDELCMD < }%%% . To describe price probability one needs all n-th statistical moments of price but that is almost impossible.
Edit intent: clarity
Original:  To model volatility one needs description of the squares of trades aggregated during interval \Delta%DIFDELCMD < }%%% . To describe price probability one needs all n-th statistical moments of price but that is almost impossible.
Revised:  To model volatility one needs description of the squares of trades aggregated during interval %DIFDELCMD < }%%% . To describe price probability one needs all n-th statistical moments of price but that is almost impossible.
Edit intent: coherence
Original:  To model volatility one needs description of the squares of trades aggregated during interval \Delta%DIFDELCMD < }%%% . To describe price probability one needs all n-th statistical moments of price but that is almost impossible. We define squares of agent's trades and macro expectations those approve the second-degree trades aggregated during interval {\Delta}.
Revised:  To model volatility one needs description of the squares of trades aggregated during interval \Delta%DIFDELCMD < }%%% p2(t) forecasting requires modeling evolution of the sums of second-degree values C(2;t) and volumes U(2;t). We call this model as second-order economic theory. We use numerical continuous risk ratings as ground for risk assessment of economic agents and distribute agents by risk ratings as coordinates. We introduce continuous economic media approximation of squares of values and volumes of agents trades and their flows aggregated during time interval {\Delta}.
Edit intent: meaning-changed
Original:  We derive equations  on the second-degree trades and expectations in economic space. As economic space we regard numerical continuous risk grades. Numerical risk grades are discussed at least for 80 years. We propose that econometrics permit accomplish risk assessment for almost all economic agents. Agents risk ratings distribute agents by economic space and define densities of macro second-degree trades and expectations.
Revised:  We derive equations on the second-degree trades .
Edit intent: coherence
Original:  In simple approximation numerous expectations and their perturbations can cause small harmonic oscillations of the second-degree trades disturbances and induce harmonic oscillations of price and volatility perturbations } .
Revised:  In simple approximation numerous expectations and their perturbations can cause small harmonic oscillations of the second-degree trades disturbances and induce harmonic oscillations of price and volatility perturbations \eta} .
Edit intent: meaning-changed
Original:  In simple approximation numerous expectations and their perturbations can cause small harmonic oscillations of the second-degree trades disturbances and induce harmonic oscillations of price and volatility perturbations } .
Revised:  In simple approximation numerous expectations and their perturbations can cause small harmonic oscillations of the second-degree trades disturbances and induce harmonic oscillations of price and volatility perturbations }(p;t) depends on sums of n-th degree values C(n;t) and volumes U(n;t) of market trades and hence requires development of the corresponding n-th order economic theory .
Edit intent: meaning-changed
Original:  Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset ( ViQuAD ), a new dataset for the low-resource language as Vietnamese to evaluate MRC models.
Revised:  Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset ( UIT-ViQuAD ), a new dataset for the low-resource language as Vietnamese to evaluate MRC models.
Edit intent: meaning-changed
Original:   The core of the proposed model is composed of self-attention and additive attention, where the former aims to capture the relatedness between input information , and the latter aims to learn the importance of inputs .
Revised:  A neural network is designed to maximize the similarity between the embedding of the three input (local context words, section and structural contexts) and the target citation appearing in the context. The core of the proposed model is composed of self-attention and additive attention, where the former aims to capture the relatedness between input information , and the latter aims to learn the importance of inputs .
Edit intent: meaning-changed
Original:   The core of the proposed model is composed of self-attention and additive attention, where the former aims to capture the relatedness between input information , and the latter aims to learn the importance of inputs .
Revised:  The core of the proposed model is composed of self-attention and additive attention, where the former aims to capture the relatedness between the contextual words and structural context , and the latter aims to learn the importance of inputs .
Edit intent: meaning-changed
Original:  Our method relies on 1 ) a cross-lingual auto-encoding , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality.
Revised:  Our method relies on (i ) a cross-lingual auto-encoding , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality.
Edit intent: meaning-changed
Original:  Our method relies on 1 ) a cross-lingual auto-encoding , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality.
Revised:  Our method relies on 1 ) a cross-lingual scene graph to sentence translation process , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality.
Edit intent: clarity
Original:  Our method relies on 1 ) a cross-lingual auto-encoding , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality.
Revised:  Our method relies on 1 ) a cross-lingual auto-encoding , which learns to decode sentences in the target language from a cross-lingual encoding space of scene graphs using a sentence parallel (bitext) corpus, and (ii ) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality.
Edit intent: meaning-changed
Original:  Our method relies on 1 ) a cross-lingual auto-encoding , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality.
Revised:  Our method relies on 1 ) a cross-lingual auto-encoding , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised cross-modal feature mapping which seeks to map the encoded scene graph features from image modality to sentence modality.
Edit intent: clarity
Original:  Our method relies on 1 ) a cross-lingual auto-encoding , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality.
Revised:  Our method relies on 1 ) a cross-lingual auto-encoding , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised feature mapping, which seeks to map an encoded scene graph features from image modality to sentence modality.
Edit intent: fluency
Original:  Our method relies on 1 ) a cross-lingual auto-encoding , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to sentence modality. By leveraging cross-lingual auto-encoding, cross-modal feature mapping, and adversarial learning, our method can learn an image captioner to generate captions in different languages.
Revised:  Our method relies on 1 ) a cross-lingual auto-encoding , which learns the scene graph mapping function along with the scene graph encoders and sentence decoders on machine translation parallel corpora, and 2 ) an unsupervised feature mapping, which seeks to map the encoded scene graph features from image modality to language modality.
Edit intent: coherence
Original:  The best strategy to date is to isolate diagnosed cases of infection, thus preventing further transmission. This technique consists on test the population for diagnosis, track the contacts of  infected, and treat by quarantine all these cases , namely Test-Track-Treat (TTT) .
Revised:  The best strategy up to date, popularly known as Test-Track-Treat (TTT), consist in testing the population for diagnosis, track the contacts of infected, and treat by quarantine all these cases , namely Test-Track-Treat (TTT) .
Edit intent: clarity
Original:  We develop an analytical expression for the effective case reproduction number %DIFDELCMD < {\cal %%% R _c(t) that reveals the role of contact tracing in the mitigation and suppression of the epidemics.
Revised:  We develop an analytical expression for the effective case reproduction number %DIFDELCMD < {\cal %%% R _c(t) that reveals the role of contact tracing in the mitigation and suppression of the epidemics.
Edit intent: fluency
Original:  However, when contact tracing is applied  , the strategy bends the curve leading to a fast suppression of the epidemic outbreak. We illustrate this qualitative difference between the two strategies and identify the factors that explain the effectiveness of contact tracing in sharply reversing the upward trend of the original epidemic curve, causing its bending.
Revised:  However, when contact tracing is applied , the strategy can bend the curve and suppress the epidemics. Quantitative results are dependent on the network topology. We quantify, the most important indicator of the effectiveness of contact tracing in sharply reversing the upward trend of the original epidemic curve, causing its bending.
Edit intent: meaning-changed
Original:  Classical results show that, when the dimension d\in N and \epsilon >0 are constant, every set S of n points in d-space admits an (1+ \epsilon )-spanners with O(n) edges and weight proportional to that of the Euclidean MST of S. Tight bounds on the dependence on \epsilon >0 for constant d\in N have been established only recently.
Revised:  Classical results show that, when the dimension d\in N and \varepsilon >0 are constant, every set S of n points in d-space admits an (1+ \epsilon )-spanners with O(n) edges and weight proportional to that of the Euclidean MST of S. Tight bounds on the dependence on \epsilon >0 for constant d\in N have been established only recently.
Edit intent: meaning-changed
Original:  Classical results show that, when the dimension d\in N and \epsilon >0 are constant, every set S of n points in d-space admits an (1+ \epsilon )-spanners with O(n) edges and weight proportional to that of the Euclidean MST of S. Tight bounds on the dependence on \epsilon >0 for constant d\in N have been established only recently.
Revised:  Classical results show that, when the dimension d\in N and \epsilon >0 are constant, every set S of n points in d-space admits an (1+ \varepsilon )-spanners with O(n) edges and weight proportional to that of the Euclidean MST of S. Tight bounds on the dependence on \epsilon >0 for constant d\in N have been established only recently.
Edit intent: meaning-changed
Original:  Classical results show that, when the dimension d\in N and \epsilon >0 are constant, every set S of n points in d-space admits an (1+ \epsilon )-spanners with O(n) edges and weight proportional to that of the Euclidean MST of S. Tight bounds on the dependence on \epsilon >0 for constant d\in N have been established only recently.
Revised:  Classical results show that, when the dimension d\in N and \epsilon >0 are constant, every set S of n points in d-space admits an (1+ \epsilon )-spanners with O(n) edges and weight proportional to that of the Euclidean MST of S. Tight bounds on the dependence on \varepsilon >0 for constant d\in N have been established only recently.
Edit intent: clarity
Original:  They gave upper bounds of O( \epsilon ^{-(d+1)/2}) for the minimum lightness in dimensions d\geq 3, and O( \epsilon ^{-(d-1))/2}) for the minimum sparsity in d-space for all d\geq 1.
Revised:  They gave upper bounds of O( \varepsilon ^{-(d+1)/2}) for the minimum lightness in dimensions d\geq 3, and O( \epsilon ^{-(d-1))/2}) for the minimum sparsity in d-space for all d\geq 1.
Edit intent: meaning-changed
Original:  They gave upper bounds of O( \epsilon ^{-(d+1)/2}) for the minimum lightness in dimensions d\geq 3, and O( \epsilon ^{-(d-1))/2}) for the minimum sparsity in d-space for all d\geq 1.
Revised:  They gave upper bounds of O( \epsilon ^{-(d+1)/2}) for the minimum lightness in dimensions d\geq 3, and O( \varepsilon ^{-(d-1))/2}) for the minimum sparsity in d-space for all d\geq 1.
Edit intent: meaning-changed
Original:  Le and Solomon (ESA 2020) also constructed Steiner (1+ \epsilon )-spanners of lightness O( \epsilon ^{-1}\log\Delta) in the plane, where \Delta\in \Omega(\log n) is the \emph{spread} of S, defined as the ratio between the maximum and minimum distance between a pair of points.
Revised:  Le and Solomon (ESA 2020) also constructed Steiner (1+ \varepsilon )-spanners of lightness O( \epsilon ^{-1}\log\Delta) in the plane, where \Delta\in \Omega(\log n) is the \emph{spread} of S, defined as the ratio between the maximum and minimum distance between a pair of points.
Edit intent: meaning-changed
Original:  Le and Solomon (ESA 2020) also constructed Steiner (1+ \epsilon )-spanners of lightness O( \epsilon ^{-1}\log\Delta) in the plane, where \Delta\in \Omega(\log n) is the \emph{spread} of S, defined as the ratio between the maximum and minimum distance between a pair of points.
Revised:  Le and Solomon (ESA 2020) also constructed Steiner (1+ \epsilon )-spanners of lightness O( \varepsilon ^{-1}\log\Delta) in the plane, where \Delta\in \Omega(\log n) is the \emph{spread} of S, defined as the ratio between the maximum and minimum distance between a pair of points.
Edit intent: meaning-changed
Original:  Using a new geometric analysis, we establish lower bounds of \Omega( \epsilon ^{-d/2}) for the lightness and \Omega( \epsilon ^{-(d-1)/2}) for the sparsity of such spanners in Euclidean d-space for all d\geq 2.
Revised:  Using a new geometric analysis, we establish lower bounds of \Omega( \varepsilon ^{-d/2}) for the lightness and \Omega( \epsilon ^{-(d-1)/2}) for the sparsity of such spanners in Euclidean d-space for all d\geq 2.
Edit intent: meaning-changed
Original:  Using a new geometric analysis, we establish lower bounds of \Omega( \epsilon ^{-d/2}) for the lightness and \Omega( \epsilon ^{-(d-1)/2}) for the sparsity of such spanners in Euclidean d-space for all d\geq 2.
Revised:  Using a new geometric analysis, we establish lower bounds of \Omega( \epsilon ^{-d/2}) for the lightness and \Omega( \varepsilon ^{-(d-1)/2}) for the sparsity of such spanners in Euclidean d-space for all d\geq 2.
Edit intent: meaning-changed
Original:  We use the geometric insight from our lower bound analysis to construct Steiner (1+ \epsilon )-spanners of lightness O( \epsilon ^{-1}\log n) for n points in Euclidean plane.
Revised:  We use the geometric insight from our lower bound analysis to construct Steiner (1+ \varepsilon )-spanners of lightness O( \epsilon ^{-1}\log n) for n points in Euclidean plane.
Edit intent: meaning-changed
Original:  We use the geometric insight from our lower bound analysis to construct Steiner (1+ \epsilon )-spanners of lightness O( \epsilon ^{-1}\log n) for n points in Euclidean plane.
Revised:  We use the geometric insight from our lower bound analysis to construct Steiner (1+ \epsilon )-spanners of lightness O( \varepsilon ^{-1}\log n) for n points in Euclidean plane.
Edit intent: meaning-changed
Original:  To address this challenge , we propose the use of topologically persistent features for object recognition. We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features,  by applying persistent homology to  filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
Revised:  To address this challenge , we propose the use of topologically persistent features , which rely on the shape information of the objects, to address this challenge. In particular, we extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features, by applying persistent homology to filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
Edit intent: meaning-changed
Original:  We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features,  by applying persistent homology to  filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
Revised:  We extract two kinds of features, by applying persistent homology to filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
Edit intent: clarity
Original:  We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features,  by applying persistent homology to  filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
Revised:  We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features, namely, sparse persistence image (PI) and amplitude, by applying persistent homology to filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
Edit intent: meaning-changed
Original:  We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features,  by applying persistent homology to  filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
Revised:  We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features, by applying persistent homology to multi-directional height function-based filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
Edit intent: meaning-changed