wanyu/IteraTeR_full_sent huggingface dataset:
Original:  From the obtained results , we observed that all the tested molecules showed better dock score in compared to the hydroxychloroquine claimed to be effective against COVID-19 . Combining the dock score and other medicinal properties, we believe the limonin can be further explored for potential use against COVID-19.
Revised:  From the obtained results , we observed that all the tested molecules showed better dock score in compared to the hydroxychloroquine claimed to be effective against COVID-19 . Combining the dock score and other medicinal properties, we believe the terpenoids based phytochemicals limonin and scopadulcic acid B can be further explored for potential use against COVID-19.
Edit intent: meaning-changed
Original:  This Hilbert geometry naturally yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein  model to differentiate it with the usual Siegel upper plane and Siegel disk domains.
Revised:  This Hilbert geometry yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein model to differentiate it with the usual Siegel upper plane and Siegel disk domains.
Edit intent: coherence
Original:  This Hilbert geometry naturally yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein  model to differentiate it with the usual Siegel upper plane and Siegel disk domains.
Revised:  This Hilbert geometry naturally yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein disk model to differentiate it with the usual Siegel upper plane and Siegel disk domains.
Edit intent: fluency
Original:  This Hilbert geometry naturally yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein  model to differentiate it with the usual Siegel upper plane and Siegel disk domains.
Revised:  This Hilbert geometry naturally yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein model to differentiate it with the classic Siegel upper plane and Siegel disk domains.
Edit intent: clarity
Original:  This Hilbert geometry naturally yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein  model to differentiate it with the usual Siegel upper plane and Siegel disk domains.
Revised:  This Hilbert geometry naturally yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein model to differentiate it with the usual Siegel upper plane and disk domains.
Edit intent: fluency
Original:  For example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex  matrices in the Siegel  domains: We compare two implementations of a generalization of the iterative  algorithm of [Badoiu and Clarkson, 2003] in the Siegel-Poincar\'e disk and in the Siegel-Klein disk.
Revised:  As an illustrating example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex matrices in the Siegel domains: We compare two implementations of a generalization of the iterative algorithm of [Badoiu and Clarkson, 2003] in the Siegel-Poincar\'e disk and in the Siegel-Klein disk.
Edit intent: coherence
Original:  For example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex  matrices in the Siegel  domains: We compare two implementations of a generalization of the iterative  algorithm of [Badoiu and Clarkson, 2003] in the Siegel-Poincar\'e disk and in the Siegel-Klein disk.
Revised:  For example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex square matrices in the Siegel domains: We compare two implementations of a generalization of the iterative algorithm of [Badoiu and Clarkson, 2003] in the Siegel-Poincar\'e disk and in the Siegel-Klein disk.
Edit intent: meaning-changed
Original:  For example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex  matrices in the Siegel  domains: We compare two implementations of a generalization of the iterative  algorithm of [Badoiu and Clarkson, 2003] in the Siegel-Poincar\'e disk and in the Siegel-Klein disk.
Revised:  For example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex matrices in the Siegel disk domains: We compare two implementations of a generalization of the iterative algorithm of [Badoiu and Clarkson, 2003] in the Siegel-Poincar\'e disk and in the Siegel-Klein disk.
Edit intent: fluency
Original:  For example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex  matrices in the Siegel  domains: We compare two implementations of a generalization of the iterative  algorithm of [Badoiu and Clarkson, 2003] in the Siegel-Poincar\'e disk and in the Siegel-Klein disk.
Revised:  For example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex matrices in the Siegel domains: We compare two implementations of a generalization of the iterative core-set algorithm of [Badoiu and Clarkson, 2003] in the Siegel-Poincar\'e disk and in the Siegel-Klein disk.
Edit intent: meaning-changed
Original:  We demonstrate that geometric computing in the Siegel-Klein disk allows one (i) to bypass the time-costly recentering operations to the origin (Siegel translations) required at each iteration of the SEB algorithm in the Siegel-Poincar\'e disk model, and (ii) to approximate numerically fast the Siegel distance with guaranteed lower and upper bounds  .
Revised:  We demonstrate that geometric computing in the Siegel-Klein disk allows one (i) to bypass the time-costly recentering operations to the disk origin required at each iteration of the SEB algorithm in the Siegel-Poincar\'e disk model, and (ii) to approximate numerically fast the Siegel distance with guaranteed lower and upper bounds .
Edit intent: clarity
Original:  We demonstrate that geometric computing in the Siegel-Klein disk allows one (i) to bypass the time-costly recentering operations to the origin (Siegel translations) required at each iteration of the SEB algorithm in the Siegel-Poincar\'e disk model, and (ii) to approximate numerically fast the Siegel distance with guaranteed lower and upper bounds  .
Revised:  We demonstrate that geometric computing in the Siegel-Klein disk allows one (i) to bypass the time-costly recentering operations to the origin (Siegel translations) required at each iteration of the SEB algorithm in the Siegel-Poincar\'e disk model, and (ii) to approximate fast and numerically the Siegel-Klein distance with guaranteed lower and upper bounds .
Edit intent: fluency
Original:  We demonstrate that geometric computing in the Siegel-Klein disk allows one (i) to bypass the time-costly recentering operations to the origin (Siegel translations) required at each iteration of the SEB algorithm in the Siegel-Poincar\'e disk model, and (ii) to approximate numerically fast the Siegel distance with guaranteed lower and upper bounds  .
Revised:  We demonstrate that geometric computing in the Siegel-Klein disk allows one (i) to bypass the time-costly recentering operations to the origin (Siegel translations) required at each iteration of the SEB algorithm in the Siegel-Poincar\'e disk model, and (ii) to approximate numerically fast the Siegel distance with guaranteed lower and upper bounds derived from nested Hilbert geometries .
Edit intent: meaning-changed
Original:  This Hilbert geometry yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein disk model to differentiate it with the classic Siegel upper plane and disk domains.
Revised:  This Hilbert geometry yields a generalization of the Klein disk model of hyperbolic geometry, henceforth called the Siegel-Klein disk model to differentiate it with the classic Siegel upper plane and disk domains.
Edit intent: clarity
Original:  This Hilbert geometry yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein disk model to differentiate it with the classic Siegel upper plane and disk domains.
Revised:  This Hilbert geometry yields a generalization of the Klein disk model of hyperbolic geometry, which we term the Siegel-Klein disk model to differentiate it with the classical Siegel upper plane and disk domains.
Edit intent: fluency
Original:  As an illustrating example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex square matrices in the Siegel disk domains: We compare two implementations of a generalization of the iterative core-set algorithm of Badoiu and Clarkson , 2003 in the Siegel-Poincar\'e disk and in the Siegel-Klein disk .
Revised:  For example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex square matrices in the Siegel disk domains: We compare two implementations of a generalization of the iterative core-set algorithm of Badoiu and Clarkson , 2003 in the Siegel-Poincar\'e disk and in the Siegel-Klein disk .
Edit intent: coherence
Original:  As an illustrating example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex square matrices in the Siegel disk domains: We compare two implementations of a generalization of the iterative core-set algorithm of Badoiu and Clarkson , 2003 in the Siegel-Poincar\'e disk and in the Siegel-Klein disk .
Revised:  As an illustrating example, we show how to approximate the smallest enclosing ball of a set of complex square matrices in the Siegel disk domains: We compare two implementations of a generalization of the iterative core-set algorithm of Badoiu and Clarkson , 2003 in the Siegel-Poincar\'e disk and in the Siegel-Klein disk .
Edit intent: clarity
Original:  As an illustrating example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex square matrices in the Siegel disk domains: We compare two implementations of a generalization of the iterative core-set algorithm of Badoiu and Clarkson , 2003 in the Siegel-Poincar\'e disk and in the Siegel-Klein disk .
Revised:  As an illustrating example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex square matrices in the Siegel disk domains: We compare two generalizations of the iterative core-set algorithm of Badoiu and Clarkson , 2003 in the Siegel-Poincar\'e disk and in the Siegel-Klein disk .
Edit intent: clarity
Original:  As an illustrating example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex square matrices in the Siegel disk domains: We compare two implementations of a generalization of the iterative core-set algorithm of Badoiu and Clarkson , 2003 in the Siegel-Poincar\'e disk and in the Siegel-Klein disk .
Revised:  As an illustrating example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex square matrices in the Siegel disk domains: We compare two implementations of a generalization of the iterative core-set algorithm of Badoiu and Clarkson (BC) in the Siegel-Poincar\'e disk and in the Siegel-Klein disk .
Edit intent: meaning-changed
Original:  As an illustrating example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex square matrices in the Siegel disk domains: We compare two implementations of a generalization of the iterative core-set algorithm of Badoiu and Clarkson , 2003 in the Siegel-Poincar\'e disk and in the Siegel-Klein disk .
Revised:  As an illustrating example, we show how to approximate the Smallest Enclosing Ball (SEB) of a set of complex square matrices in the Siegel disk domains: We compare two implementations of a generalization of the iterative core-set algorithm of Badoiu and Clarkson , 2003 in the Siegel-Poincar\'e disk and in the Siegel-Klein disk :
Edit intent: fluency
Original:  We demonstrate that geometric computing in the Siegel-Klein disk allows one (i) to bypass the time-costly recentering operations to the disk origin required at each iteration of the SEB algorithm in the Siegel-Poincar\'e disk model, and (ii) to approximate fast and numerically the Siegel-Klein distance with guaranteed lower and upper bounds derived from nested Hilbert geometries.
Revised:  We demonstrate that geometric computing in the Siegel-Klein disk allows one (i) to bypass the time-costly recentering operations to the disk origin required at each iteration of the BC algorithm in the Siegel-Poincar\'e disk model, and (ii) to approximate fast and numerically the Siegel-Klein distance with guaranteed lower and upper bounds derived from nested Hilbert geometries.
Edit intent: clarity
Original: Designing visual crowd experiments requires both control and versatility. While behavioural and computer sciences have produced a fair number of tools facilitating this process, a gap remains when it comes to the combination of accessibility and versatility. The Processing language is widely used by artist and designers of varying levels of expertise and thus fulfils these conditions. Here, we investigated whether the Processing inspired JavaScript library p5. js can be used for visual crowd research. We specifically explored how to use p5.js in combination with MTurk and report a simple way of using p5.js 'sketches' to perform online tests .
Revised: Relating human judgements to pictures is central to a wide variety of scientific disciplines. Pictures are used to evoke and study faculties of the human mind, while humans input is used to label, understand and model pictorial representations. Human input is often collected through online crowdsourcing experiments. This paper discusses the usage of crowdsourcing in two major branches of picture-centered research, human and computer vision, and identifies novel directions such as art history and design. We demonstrate that a wide variety of experiments can be conducted by using p5.js 'sketches' to perform online tests .
Edit intent: meaning-changed
Original:  We report four complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition . Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved .
Revised:  We report five complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition . Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved .
Edit intent: meaning-changed
Original:  We report four complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition . Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved .
Revised:  We report four complementary experimental paradigms to illustrated the accessibility and versatility of p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition . Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved .
Edit intent: fluency
Original:  We report four complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition . Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved .
Revised:  We report four complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, 3D shape perception, Composition, and Perspective reconstruction . Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved .
Edit intent: meaning-changed
Original:  We report four complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition . Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved .
Revised:  We report four complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition . Results reveal that literature findings can be reproduced and novel insights can easily be achieved .
Edit intent: clarity
Original:  We report four complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition . Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved .
Revised:  We report four complementary experimental paradigms to illustrated the accessibility and versatility pf p5.js: Change blindness, BubbleView, Gauge figure (attitude probe) and Composition . Results reveal that previous literature findings can be reproduced and novel insights can easily be achieved with the p5.js library.
Edit intent: meaning-changed
Original:  The control of the epidemic is induced by the aggregation of individuals' decisions to limit their social interactions: on one side, when the epidemic is ongoing, an individual is encouraged to diminish his/her contact rate in order to avoid getting infected, but , on the other side, this effort comes at a social cost.
Revised:  This control is induced by the aggregation of individuals' decisions to limit their social interactions: on one side, when the epidemic is ongoing, an individual is encouraged to diminish his/her contact rate in order to avoid getting infected, but , on the other side, this effort comes at a social cost.
Edit intent: clarity
Original:  The control of the epidemic is induced by the aggregation of individuals' decisions to limit their social interactions: on one side, when the epidemic is ongoing, an individual is encouraged to diminish his/her contact rate in order to avoid getting infected, but , on the other side, this effort comes at a social cost.
Revised:  The control of the epidemic is induced by the aggregation of individuals' decisions to limit their social interactions: when the epidemic is ongoing, an individual is encouraged to diminish his/her contact rate in order to avoid getting infected, but , on the other side, this effort comes at a social cost.
Edit intent: fluency
Original:  The control of the epidemic is induced by the aggregation of individuals' decisions to limit their social interactions: on one side, when the epidemic is ongoing, an individual is encouraged to diminish his/her contact rate in order to avoid getting infected, but , on the other side, this effort comes at a social cost.
Revised:  The control of the epidemic is induced by the aggregation of individuals' decisions to limit their social interactions: on one side, when the epidemic is ongoing, an individual can diminish his/her contact rate in order to avoid getting infected, but , on the other side, this effort comes at a social cost.
Edit intent: clarity
Original:  The control of the epidemic is induced by the aggregation of individuals' decisions to limit their social interactions: on one side, when the epidemic is ongoing, an individual is encouraged to diminish his/her contact rate in order to avoid getting infected, but , on the other side, this effort comes at a social cost.
Revised:  The control of the epidemic is induced by the aggregation of individuals' decisions to limit their social interactions: on one side, when the epidemic is ongoing, an individual is encouraged to diminish his/her contact rate in order to avoid getting infected, but this effort comes at a social cost.
Edit intent: coherence
Original:  We provide numerical examples and a sensitivity analysis . We show that the divergence between the individual and societal strategies happens after the epidemic peak but while significant propagation is still underway.
Revised:  We provide numerical examples and a sensitivity analysis , as well as an extension to a SEIR compartmental model to account for the relatively long latent phase of the COVID-19 disease. In all the scenarii considered, the divergence between the individual and societal strategies happens after the epidemic peak but while significant propagation is still underway.
Edit intent: meaning-changed
Original: Quantitative risk management, particularly volatility forecasting, is critically important to traders, portfolio managers as well as policy makers. In this paper, we applied quantum reservoir computing for forecasting VIX ( the CBOE volatility index ), a highly non-linear and memory intensive `real-life' signal that is driven by market dynamics and trader psychology and cannot be expressed by a deterministic equation.
Revised: Forecasting the CBOE volatility index ), a highly non-linear and memory intensive `real-life' signal that is driven by market dynamics and trader psychology and cannot be expressed by a deterministic equation.
Edit intent: coherence
Original:  In this paper, we applied quantum reservoir computing for forecasting VIX ( the CBOE volatility index ), a highly non-linear and memory intensive `real-life' signal that is driven by market dynamics and trader psychology and cannot be expressed by a deterministic equation.
Revised:  In this paper, we applied quantum reservoir computing for forecasting VIX ( the CBOE volatility index (VIX) is a highly non-linear and memory intensive `real-life' signal that is driven by market dynamics and trader psychology and cannot be expressed by a deterministic equation.
Edit intent: coherence
Original:  In this paper, we applied quantum reservoir computing for forecasting VIX ( the CBOE volatility index ), a highly non-linear and memory intensive `real-life' signal that is driven by market dynamics and trader psychology and cannot be expressed by a deterministic equation. As a first step, we lay out the systematic design considerations for using a NISQ reservoir as a computing engine (which should be useful for practitioners) . We then show how to experimentally evaluate the memory capacity of various reservoir topologies (using IBM-Q's Rochester device) to identify the configuration with maximum memory capacity. Once the optimal design is selected, the forecast is produced by a linear combination of  the average spin of a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards.
Revised:  In this paper, we applied quantum reservoir computing for forecasting VIX ( the CBOE volatility index ), a highly non-linear and memory-intensive task. In this paper, we use quantum reservoir computing to forecast the VIX using S the average spin of a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards.
Edit intent: coherence
Original:  Once the optimal design is selected, the forecast is produced by a linear combination of  the average spin of a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards.
Revised:  Once the optimal design is selected, the forecast is produced by a linear combination of P500 (SPX) time-series. Our reservoir is a hybrid quantum-classical system executed on IBM's 53-qubit Rochester chip. We encode the SPX values in the rotation angles and linearly combine the average spin of a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards.
Edit intent: meaning-changed
Original:  Once the optimal design is selected, the forecast is produced by a linear combination of  the average spin of a 6-qubit quantum register trained using VIX and SPX data from year 1990 onwards. We test the forecast performance over the sub-prime mortgage crisis period (Dec 2007 - Jun 2009) .
Revised:  Once the optimal design is selected, the forecast is produced by a linear combination of the average spin of the six-qubit register to predict the value of VIX at the next time step .
Edit intent: meaning-changed