wanyu/IteraTeR_v2 huggingface dataset:
Original:  There is a linear relationship between the presented flow cytometry method and agar plate counts for live (R2 = 0.99) and dead E. coli (R2 = 0.93) concentrations of ca. 104 to 108 bacteria mL-1 within mixtures of live and dead bacteria.
Revised:  There is a linear relationship between the presented flow cytometry method and agar plate counts for live (R2 = 0.99) and dead E. coli (R2 = 0.93) concentrations of ca. 104 to 108 bacteria ml-1 within mixtures of live and dead bacteria.
Edit intent: clarity
Original:  Conclusions: Reliable enumeration of live E. coli within a mixture of both live and dead was possible down to concentration ratio of 2.5\% live and for the enumeration of dead E. coli the lower limit was ca. 20\% dead.
Revised:  Conclusions: Reliable enumeration of live E. coli within a mixture of both live and dead was possible for concentration ratios of above 2.5\% live and for the enumeration of dead E. coli the lower limit was ca. 20\% dead.
Edit intent: clarity
Original:  Users derive utility from consuming electricity as well as consuming less energy than their neighbors , and disutility for consuming more than their neighbors.
Revised:  Users derive utility from consuming electricity as well as consuming less energy than their neighbors . However, a disutility is incurred for consuming more than their neighbors.
Edit intent: clarity
Original:  To maximize the profit of the load-serving entity providing electricity to users subject to such peer effects, we employ a two-stage game-theoretic model, where the entity sets the prices in the first stage , and the consumers decide on their demand in response to the observed price  so as to maximize their utility in the second stage.
Revised:  To maximize the profit of the load-serving entity that provides electricity to such users, we develop a two-stage game-theoretic model, where the entity sets the prices in the first stage , and the consumers decide on their demand in response to the observed price so as to maximize their utility in the second stage.
Edit intent: clarity
Original:  To maximize the profit of the load-serving entity providing electricity to users subject to such peer effects, we employ a two-stage game-theoretic model, where the entity sets the prices in the first stage , and the consumers decide on their demand in response to the observed price  so as to maximize their utility in the second stage.
Revised:  To maximize the profit of the load-serving entity providing electricity to users subject to such peer effects, we employ a two-stage game-theoretic model, where the entity sets the prices in the first stage . In the second stage, consumers decide on their demand in response to the observed price so as to maximize their utility in the second stage.
Edit intent: coherence
Original:  To maximize the profit of the load-serving entity providing electricity to users subject to such peer effects, we employ a two-stage game-theoretic model, where the entity sets the prices in the first stage , and the consumers decide on their demand in response to the observed price  so as to maximize their utility in the second stage.
Revised:  To maximize the profit of the load-serving entity providing electricity to users subject to such peer effects, we employ a two-stage game-theoretic model, where the entity sets the prices in the first stage , and the consumers decide on their demand in response to the observed price set in the first stage so as to maximize their utility in the second stage.
Edit intent: fluency
Original:  Further, we obtain closed-form solutions for the resulting electricity consumption and profit of the load serving entity for the case of perfect price discrimination and a single price under complete information, and approximations under incomplete information.
Revised:  Further, we obtain expressions for the resulting electricity consumption and profit of the load serving entity for the case of perfect price discrimination and a single price under complete information, and approximations under incomplete information.
Edit intent: clarity
Original:  Existing systems simply plot the raw data streams as they arrive, potentially obscuring large-scale deviations due to local variance and noise.
Revised:  Existing systems simply plot the raw data streams as they arrive, often obscuring large-scale deviations due to local variance and noise.
Edit intent: clarity
Original:  Existing systems simply plot the raw data streams as they arrive, potentially obscuring large-scale deviations due to local variance and noise.
Revised:  Existing systems simply plot the raw data streams as they arrive, potentially obscuring large-scale trends due to small-scale noise.
Edit intent: clarity
Original:  We propose an alternative: to better prioritize attention in time series exploration and monitoring visualizations, smooth the time series as much as possible to remove noise while still retaining large-scale structure  .
Revised:  We propose an alternative: to better prioritize end users' attention, smooth time series visualizations as much as possible to remove noise while still retaining large-scale structure .
Edit intent: clarity
Original:  We propose an alternative: to better prioritize attention in time series exploration and monitoring visualizations, smooth the time series as much as possible to remove noise while still retaining large-scale structure  .
Revised:  We propose an alternative: to better prioritize attention in time series exploration and monitoring visualizations, smooth the time series as much as possible to remove noise , while retaining large-scale structure .
Edit intent: coherence
Original:  We develop a new technique for automatically smoothing streaming time series that adaptively optimizes this trade-off between noise reduction (i.e., variance) and outlier retention (i.e., kurtosis).
Revised:  We develop a new technique for automatically smoothing streaming time series by adaptively optimizing the trade-off between noise reduction (i.e., variance) and outlier retention (i.e., kurtosis).
Edit intent: coherence
Original:  We develop a new technique for automatically smoothing streaming time series that adaptively optimizes this trade-off between noise reduction (i.e., variance) and outlier retention (i.e., kurtosis).
Revised:  We develop a new technique for automatically smoothing streaming time series that adaptively optimizes this trade-off between noise reduction (i.e., variance) and trend retention (i.e., kurtosis).
Edit intent: clarity
Original:  We introduce metrics to quantitatively assess the quality of the choice of smoothing parameter and provide an efficient streaming analytics operator, ASAP, that optimizes these metrics by combining techniques from stream processing, user interface design, and signal processing via a novel autocorrelation-based pruning strategy and pixel-aware preaggregation  .
Revised:  We introduce metrics to quantitatively assess the quality of smoothed plots and provide an efficient streaming analytics operator, ASAP, that optimizes these metrics by combining techniques from stream processing, user interface design, and signal processing via a novel autocorrelation-based pruning strategy and pixel-aware preaggregation .
Edit intent: clarity
Original:  We introduce metrics to quantitatively assess the quality of the choice of smoothing parameter and provide an efficient streaming analytics operator, ASAP, that optimizes these metrics by combining techniques from stream processing, user interface design, and signal processing via a novel autocorrelation-based pruning strategy and pixel-aware preaggregation  .
Revised:  We introduce metrics to quantitatively assess the quality of the choice of smoothing parameter and provide an efficient search strategy for optimizing these metrics that combines techniques from stream processing, user interface design, and signal processing via a novel autocorrelation-based pruning strategy and pixel-aware preaggregation .
Edit intent: clarity
Original:  We introduce metrics to quantitatively assess the quality of the choice of smoothing parameter and provide an efficient streaming analytics operator, ASAP, that optimizes these metrics by combining techniques from stream processing, user interface design, and signal processing via a novel autocorrelation-based pruning strategy and pixel-aware preaggregation  .
Revised:  We introduce metrics to quantitatively assess the quality of the choice of smoothing parameter and provide an efficient streaming analytics operator, ASAP, that optimizes these metrics by combining techniques from stream processing, user interface design, and signal processing via autocorrelation-based pruning strategy and pixel-aware preaggregation .
Edit intent: clarity
Original:  We introduce metrics to quantitatively assess the quality of the choice of smoothing parameter and provide an efficient streaming analytics operator, ASAP, that optimizes these metrics by combining techniques from stream processing, user interface design, and signal processing via a novel autocorrelation-based pruning strategy and pixel-aware preaggregation  .
Revised:  We introduce metrics to quantitatively assess the quality of the choice of smoothing parameter and provide an efficient streaming analytics operator, ASAP, that optimizes these metrics by combining techniques from stream processing, user interface design, and signal processing via a novel autocorrelation-based pruning , pixel-aware preaggregation .
Edit intent: clarity
Original:  We demonstrate that ASAP is able to improve users' accuracy in identifying significant deviations in time series by up to 38.4\% while reducing response times by up to 44.3\%.
Revised:  We demonstrate that ASAP can improve users' accuracy in identifying significant deviations in time series by up to 38.4\% while reducing response times by up to 44.3\%.
Edit intent: clarity
Original:  Moreover, ASAP delivers these results several orders of magnitude faster than alternative optimization strategies.
Revised:  Moreover, ASAP delivers these results several orders of magnitude faster than alternative search strategies.
Edit intent: clarity
Original: Modeling the popularity dynamics of an online item is an important open problem in social media analysis and computational social science.
Revised: Modeling the popularity dynamics of an online item is an important open problem in computational social science.
Edit intent: clarity
Original:  The recently-proposed Hawkes Intensity Process (HIP) models popularity as a non-linear interplay between exogenous stimuli and the endogenous reaction.
Revised:  The recently proposed Hawkes Intensity Process (HIP) models popularity as a non-linear interplay between exogenous stimuli and the endogenous reaction.
Edit intent: fluency
Original:  We propose two novel metrics based on HIP: to describe popularity gain per unit of promotion , and to quantify the time it takes for such effects to unfold.
Revised:  We propose two novel metrics based on HIP: to describe popularity gain per unit of promo- tion , and to quantify the time it takes for such effects to unfold.
Edit intent: fluency
Original:  Here, we propose two novel metrics based on HIP: to describe popularity gain per unit of promo- tion , and to quantify the time it takes for such effects to unfold.
Revised:  Here, we propose two novel metrics based on HIP: to describe popularity gain per unit of promotion , and to quantify the time it takes for such effects to unfold.
Edit intent: fluency
Original: The use of a necessity-like modality in a typed \lambda-calculus can be used as a device for separating the calculus in two separate regions.
Revised: The use of a necessity modality in a typed \lambda-calculus can be used as a device for separating the calculus in two separate regions.
Edit intent: clarity
Original: The use of a necessity-like modality in a typed \lambda-calculus can be used as a device for separating the calculus in two separate regions.
Revised: The use of a necessity-like modality in a typed \lambda-calculus can be used to separate it into two regions.
Edit intent: clarity
Original:  These can be thought of as intensional vs. extensional data: data in the first region, the modal one, are available as code, and their description can be examined , whereas data in the second region are only available as values up to ordinary equality.
Revised:  These can be thought of as intensional vs. extensional data: data in the first region, the modal one, are available as code, and their description can be examined . In contrast, data in the second region are only available as values up to ordinary equality.
Edit intent: coherence
Original:  This allows us to add seemingly non-functional operations at modal types , whilst maintaining consistency.
Revised:  This allows us to add non-functional operations at modal types , whilst maintaining consistency.
Edit intent: clarity
Original:  This allows us to add seemingly non-functional operations at modal types , whilst maintaining consistency.
Revised:  This allows us to add seemingly non-functional operations at modal types whilst maintaining consistency.
Edit intent: fluency
Original:  In this setting  the G\"odel-L\"ob axiom acquires a novel constructive reading: it affords the programmer the possibility of a very strong kind of recursion , by enabling him to write programs that have access to their own code.
Revised:  In this setting , the G\"odel-L\"ob axiom acquires a novel constructive reading: it affords the programmer the possibility of a very strong kind of recursion , by enabling him to write programs that have access to their own code.
Edit intent: fluency
Original:  In this setting  the G\"odel-L\"ob axiom acquires a novel constructive reading: it affords the programmer the possibility of a very strong kind of recursion , by enabling him to write programs that have access to their own code.
Revised:  In this setting the G\"odel-L\"ob axiom acquires a novel constructive reading: it affords the programmer the possibility of a very strong kind of recursion which enables them to write programs that have access to their own code.
Edit intent: clarity
Original:  Our construction(X) under the probability P. }\newline  is inspired by a new interpretation of the quasiconvex duality and naturally leads to the introduction of the general class of Value\&Risk measures .
Revised:  Our construction(X) under the probability P. }\newline Our approach is inspired by a new interpretation of the quasiconvex duality and naturally leads to the introduction of the general class of Value\&Risk measures .
Edit intent: clarity
Original:  We introduce a general class of Value\&Risk (V\&R) measures as a function of (p,X,P), where p is the observed initial value of X or is assigned by \pi _{P(X) under the probability P. }%DIFDELCMD < \newline %%% Our approach is inspired by a new interpretation of the quasiconvex duality in a Knightian setting, where a family of probability measures replaces the single reference probability and is then applied to value financial positions.
Revised:  (X) under the probability P. }%DIFDELCMD < \newline %%% Our approach is inspired by a new interpretation of the quasiconvex duality in a Knightian setting, where a family of probability measures replaces the single reference probability and is then applied to value financial positions.
Edit intent: coherence
Original:  Diametrically, our construction of V\&R measures is based on the selection of a basket of claims to test the reliability of models  .
Revised:  Diametrically, our construction of Value\&Risk measures is based on the selection of a basket of claims to test the reliability of models .
Edit intent: clarity
Original:  For example, by knowing when will users follow back, service providers can focus on the users with  potential long reciprocal delay for effective targeted marketing.
Revised:  For example, by knowing when will users follow back, service providers can focus on the users with a potential long reciprocal delay for effective targeted marketing.
Edit intent: fluency
Original:  In addition we propose some extensions of obtained results that can be applicable for pair trading algorithmic strategies.
Revised:  In addition we propose some extensions of obtained results that can be applicable for algorithmic strategies.
Edit intent: clarity
Original:  We propose a novel framework that reduces the management and integration overheads of the traditional approaches by leveraging the capabilities (especially global visibility and management) of Software-defined networking (SDN) architectures.
Revised:  We propose a novel framework that reduces the management and integration overheads of the traditional approaches by leveraging the capabilities (especially global visibility and management) of software-defined networking (SDN) architectures.
Edit intent: fluency
Original:  Given the specifications of flows requiring real-time guarantees , our framework synthesizes paths through the network and associated switch configurations that can meet the requisite delay requirementsfor real-time flows.
Revised:  Given the specifications of flows that must meet hard real-time guarantees , our framework synthesizes paths through the network and associated switch configurations that can meet the requisite delay requirementsfor real-time flows.
Edit intent: clarity
Original:  Given the specifications of flows requiring real-time guarantees , our framework synthesizes paths through the network and associated switch configurations that can meet the requisite delay requirementsfor real-time flows.
Revised:  Given the specifications of flows requiring real-time requirements , our framework synthesizes paths through the network and associated switch configurations that can meet the requisite delay requirementsfor real-time flows.
Edit intent: clarity
Original:  Using exhaustive simulations as well as emulations with real switch software, we show the feasibility of our approach  .
Revised:  Using exhaustive simulations as well as the feasibility of our approach .
Edit intent: clarity
Original: We define the  visual complexity of a plane graph drawing to be the number of  geometric objects needed to represent all its edges.
Revised: We define the emph visual complexity of a plane graph drawing to be the number of geometric objects needed to represent all its edges.
Edit intent: fluency
Original: We define the emph visual complexity of a plane graph drawing to be the number of basic geometric objects needed to represent all its edges.
Revised: We define the visual complexity of a plane graph drawing to be the number of basic geometric objects needed to represent all its edges.
Edit intent: coherence
Original: We define the emph visual complexity of a plane graph drawing to be the number of basic geometric objects needed to represent all its edges.
Revised: We define the emph visual complexity of a plane graph drawing to be the number of basic geometric objects needed to represent all its edges.
Edit intent: fluency
Original:  This provides a significant improvement over the lower bound of 2n for line segments for a nontrivial graph class.
Revised:  This is significantly smaller than the lower bound of 2n for line segments for a nontrivial graph class.
Edit intent: clarity
Original: Both feedback of ratings and trust relationships can be used to reveal user preference to improve recommendation performance, especially for cold users.
Revised: Both feedback of ratings and trust relationships can be used to reveal users' tastes for improving recommendation performance, especially for cold users.
Edit intent: clarity
Original:  Towards this problem , we propose a Correlative Denoising Autoencoder (CoDAE) model to learn correlations\textit{  from both rating and trust data for Top-N recommendation.
Revised:  Towards this problem , we propose to utilize the idea of Denoising Auto-Encoders (DAE) to tackle this problem. Specially, we propose a novel deep learning model, the\textit{ from both rating and trust data for Top-N recommendation.
Edit intent: clarity
Original:  Comprehensive experiments on two public datasets  demonstrate that the CoDAE significantly outperforms other state-of-the-art approaches in top-N recommendation task.
Revised:  Comprehensive experiments on two public datasets demonstrate that the proposed method significantly outperforms other state-of-the-art approaches in top-N recommendation task.
Edit intent: clarity
Original:  Comprehensive experiments on two public datasets  demonstrate that the CoDAE significantly outperforms other state-of-the-art approaches in top-N recommendation task.
Revised:  Comprehensive experiments on two public datasets demonstrate that the CoDAE significantly outperforms other comparisons for top-N recommendation task.
Edit intent: clarity
Original:  Specially , we propose a novel deep learning model, the\textit{Trust-aware Collaborative Denoising Auto-Encoder (TDAE) , to learn compact and effective representations from both rating and trust data for top-N recommendation.
Revised:  Specially , we propose a novel \textit{Trust-aware Collaborative Denoising Auto-Encoder (TDAE) , to learn compact and effective representations from both rating and trust data for top-N recommendation.
Edit intent: clarity
Original:  Specially , we propose a novel deep learning model, the\textit{Trust-aware Collaborative Denoising Auto-Encoder (TDAE) , to learn compact and effective representations from both rating and trust data for top-N recommendation.
Revised:  Specially , we propose a novel deep learning model, the\textit{ (TDAE) , to learn compact and effective representations from both rating and trust data for top-N recommendation.
Edit intent: coherence
Original: The recently proposed temporal ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks.
Revised: The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks.
Edit intent: fluency
Original:  However, because the targets change only once per epoch, temporal ensembling becomes unwieldy when using large datasets.
Revised:  However, because the targets change only once per epoch, Temporal Ensembling becomes unwieldy when using large datasets.
Edit intent: coherence
Original:  However, because the targets change only once per epoch, temporal ensembling becomes unwieldy when using large datasets.
Revised:  However, because the targets change only once per epoch, temporal ensembling becomes unwieldy when learning large datasets.
Edit intent: coherence
Original:  As an additional benefit, the method improves test accuracy and enables training with fewer labels than earlier methods.
Revised:  As an additional benefit, Mean Teacher improves test accuracy and enables training with fewer labels than earlier methods.
Edit intent: clarity
Original:   Mean Teacher achieves error rate 4.35\% on SVHN with 250 labels, better than Temporal Ensembling does with 1000 labels  .
Revised:  Mean Teacher achieves an error rate of 4.35\% on SVHN with 250 labels, better than Temporal Ensembling does with 1000 labels .
Edit intent: fluency
Original:   Mean Teacher achieves error rate 4.35\% on SVHN with 250 labels, better than Temporal Ensembling does with 1000 labels  .
Revised:  Mean Teacher achieves error rate 4.35\% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels .
Edit intent: clarity
Original:  We show how deep architectures with sample-level filters improve the accuracy in music auto-tagging and they provide results that are com- parable to previous state-of-the-art performances for the Magnatagatune dataset and Million song dataset .
Revised:  We show how deep architectures with sample-level filters improve the accuracy in music auto-tagging and they provide results comparable to previous state-of-the-art performances for the Magnatagatune dataset and Million song dataset .
Edit intent: clarity