wanyu/IteraTeR_v2 huggingface dataset:
Original:  The development of the free-hand sketch community in the deep learning era is reviewed , by surveying the existing datasets, research topics, and the state-of-the-art methods via a detailed taxonomy  . (iii) Moreover, the bottlenecks, open problems, and potential research directions of this communityhave also been discussed to promote the future works.
Revised:  The development of the free-hand sketch community in the deep learning era , by surveying the existing datasets, research topics, and the state-of-the-art methods via a detailed taxonomy . (iii) Moreover, the bottlenecks, open problems, and potential research directions of this communityhave also been discussed to promote the future works.
Edit intent: clarity
Original:  The development of the free-hand sketch community in the deep learning era is reviewed , by surveying the existing datasets, research topics, and the state-of-the-art methods via a detailed taxonomy  . (iii) Moreover, the bottlenecks, open problems, and potential research directions of this communityhave also been discussed to promote the future works.
Revised:  The development of the free-hand sketch community in the deep learning era is reviewed , by surveying existing datasets, research topics, and the state-of-the-art methods via a detailed taxonomy . (iii) Moreover, the bottlenecks, open problems, and potential research directions of this communityhave also been discussed to promote the future works.
Edit intent: fluency
Original:  The development of the free-hand sketch community in the deep learning era is reviewed , by surveying the existing datasets, research topics, and the state-of-the-art methods via a detailed taxonomy  . (iii) Moreover, the bottlenecks, open problems, and potential research directions of this communityhave also been discussed to promote the future works.
Revised:  The development of the free-hand sketch community in the deep learning era is reviewed , by surveying the existing datasets, research topics, and the state-of-the-art methods through a detailed taxonomy . (iii) Moreover, the bottlenecks, open problems, and potential research directions of this communityhave also been discussed to promote the future works.
Edit intent: fluency
Original:  The development of the free-hand sketch community in the deep learning era is reviewed , by surveying the existing datasets, research topics, and the state-of-the-art methods via a detailed taxonomy  . (iii) Moreover, the bottlenecks, open problems, and potential research directions of this communityhave also been discussed to promote the future works.
Revised:  The development of the free-hand sketch community in the deep learning era is reviewed , by surveying the existing datasets, research topics, and the state-of-the-art methods via a detailed taxonomy . (iii) Promotion of future work via a discussion of bottlenecks, open problems, and potential research directions of this communityhave also been discussed to promote the future works.
Edit intent: coherence
Original: This paper shows how to generate code that efficiently converts sparse tensors between disparate storage formats (data layouts) like CSR, DIA, ELL, and many others.
Revised: This paper shows how to generate code that efficiently converts sparse tensors between disparate storage formats (data layouts) such as CSR, DIA, ELL, and many others.
Edit intent: fluency
Original:  This enables a compiler to emit code that performs complex reorderings (remappings ) of nonzeros when converting between formats.
Revised:  This lets a compiler emit code that performs complex reorderings (remappings ) of nonzeros when converting between formats.
Edit intent: fluency
Original:  This enables a compiler to emit code that performs complex reorderings (remappings ) of nonzeros when converting between formats.
Revised:  This enables a compiler to emit code that performs complex remappings of nonzeros when converting between formats.
Edit intent: clarity
Original:  We additionally develop a query language that can extract complex statistics about sparse tensors, and we show how to emit efficient analysis code that computes such queries.
Revised:  We also develop a query language that can extract complex statistics about sparse tensors, and we show how to emit efficient analysis code that computes such queries.
Edit intent: fluency
Original:  We additionally develop a query language that can extract complex statistics about sparse tensors, and we show how to emit efficient analysis code that computes such queries.
Revised:  We additionally develop a query language that can extract statistics about sparse tensors, and we show how to emit efficient analysis code that computes such queries.
Edit intent: clarity
Original:  Disparate formats can implement this common interface, thus letting a compiler emit optimized sparse tensor conversion code for arbitrary combinations of a wide range of formats without hard-coding for any specific one .
Revised:  Disparate formats can implement this common interface, thus letting a compiler emit optimized sparse tensor conversion code for arbitrary combinations of many formats without hard-coding for any specific one .
Edit intent: clarity
Original:  Disparate formats can implement this common interface, thus letting a compiler emit optimized sparse tensor conversion code for arbitrary combinations of a wide range of formats without hard-coding for any specific one .
Revised:  Disparate formats can implement this common interface, thus letting a compiler emit optimized sparse tensor conversion code for arbitrary combinations of a wide range of formats without hard-coding for any specific combination .
Edit intent: clarity
Original:  Our evaluation shows that our technique generates sparse tensor conversion routines with performance between 0.99 and 2.2 \times that of hand-optimized implementations in two widely used sparse linear algebra libraries , SPARSKIT and Intel MKL. By emitting code that avoids materializing temporaries, our technique also outperforms both libraries by between 1.4 and 3.4 \times for CSC/COO to DIA/ELL conversion.
Revised:  Our evaluation shows that the technique generates sparse tensor conversion routines with performance between 0.99 and 2.2 \times that of hand-optimized implementations in two widely used sparse linear algebra libraries , SPARSKIT and Intel MKL. By emitting code that avoids materializing temporaries, our technique also outperforms both libraries by between 1.4 and 3.4 \times for CSC/COO to DIA/ELL conversion.
Edit intent: fluency
Original:  Our evaluation shows that our technique generates sparse tensor conversion routines with performance between 0.99 and 2.2 \times that of hand-optimized implementations in two widely used sparse linear algebra libraries , SPARSKIT and Intel MKL. By emitting code that avoids materializing temporaries, our technique also outperforms both libraries by between 1.4 and 3.4 \times for CSC/COO to DIA/ELL conversion.
Revised:  Our evaluation shows that our technique generates sparse tensor conversion routines with performance between 0.99 and 2.2 \times that of hand-optimized versions in SPARSKIT and Intel MKL, two popular sparse linear algebra libraries , SPARSKIT and Intel MKL. By emitting code that avoids materializing temporaries, our technique also outperforms both libraries by between 1.4 and 3.4 \times for CSC/COO to DIA/ELL conversion.
Edit intent: clarity
Original:  Our evaluation shows that our technique generates sparse tensor conversion routines with performance between 0.99 and 2.2 \times that of hand-optimized implementations in two widely used sparse linear algebra libraries , SPARSKIT and Intel MKL. By emitting code that avoids materializing temporaries, our technique also outperforms both libraries by between 1.4 and 3.4 \times for CSC/COO to DIA/ELL conversion.
Revised:  Our evaluation shows that our technique generates sparse tensor conversion routines with performance between 0.99 and 2.2 \times that of hand-optimized implementations in two widely used sparse linear algebra libraries . And by emitting code that avoids materializing temporaries, our technique also outperforms both libraries by between 1.4 and 3.4 \times for CSC/COO to DIA/ELL conversion.
Edit intent: coherence
Original:  The optimal quadrature formulas of the cases m=2 and m=3 are applied for reconstruction of Computed Tomography (CT) images by approximating Fourier transforms in the filtered back-projection formula .
Revised:  The optimal quadrature formulas of the cases m=2 and m=3 are applied for reconstruction of Computed Tomography (CT) . By approximating Fourier transforms in the filtered back-projection formula .
Edit intent: clarity
Original:  Our proposed streaming transformer architecture achieves 2.7\% and 7.0\% WER for the ``clean'' and ``other'' test data of LibriSpeech, which to the best of our knowledge is the best published streaming end-to-end ASR result for this task.
Revised:  Our proposed streaming transformer architecture achieves 2.7\% and 7.0\% WER for the "clean" and "other" test data of LibriSpeech, which to the best of our knowledge is the best published streaming end-to-end ASR result for this task.
Edit intent: fluency
Original:  Our proposed streaming transformer architecture achieves 2.7\% and 7.0\% WER for the ``clean'' and ``other'' test data of LibriSpeech, which to the best of our knowledge is the best published streaming end-to-end ASR result for this task.
Revised:  Our proposed streaming transformer architecture achieves 2.7\% and 7.0\% WER for the ``clean'' and ``other'' test data of LibriSpeech, which to our knowledge is the best published streaming end-to-end ASR result for this task.
Edit intent: fluency
Original:  Our proposed streaming transformer architecture achieves 2.7\% and 7.0 \% WER for the "clean " and "other " test data of LibriSpeech, which to our knowledge is the best published streaming end-to-end ASR result for this task.
Revised:  Our proposed streaming transformer architecture achieves 2.7\% and 7.0 \% WER for the clean and other test data of LibriSpeech, which to our knowledge is the best published streaming end-to-end ASR result for this task.
Edit intent: fluency
Original:  Our proposed streaming transformer architecture achieves 2.8\% and 7.3 \% WER for the clean and other test data of LibriSpeech, which to our knowledge is the best published streaming end-to-end ASR result for this task.
Revised:  Our proposed streaming transformer architecture achieves 2.8\% and 7.3 \% WER for the "clean" and "other" test data of LibriSpeech, which to our knowledge is the best published streaming end-to-end ASR result for this task.
Edit intent: fluency
Original:  In particular, we derive supercloseness estimates on the velocity and a priori error estimates under unstructured grids .
Revised:  In particular, we derive superconvergence estimates for the velocity and a priori error estimates under unstructured grids .
Edit intent: clarity
Original:  As applications, we use monotone recursive types to generically derive two recursive representations of data in the lambda calculus, the Parigot and Scott encoding  , together with constant-time destructors, a recursion scheme, and the standard induction principle .
Revised:  As applications, we use monotone recursive types to generically derive two recursive representations of data in lambda calculus, the Parigot and Scott encoding , together with constant-time destructors, a recursion scheme, and the standard induction principle .
Edit intent: fluency
Original:  When Kriging is used as the surrogate model of choice (also called Bayesian optimization), then one of the most frequently chosen criteria is expected improvement.
Revised:  When Kriging is used as the surrogate model of choice (also called Bayesian optimization), one of the most frequently chosen criteria is expected improvement.
Edit intent: coherence
Original:  Yet, we argue that the popularity of expected improvement largely relies on its theoretical properties rather than empirically validated performance.
Revised:  We argue that the popularity of expected improvement largely relies on its theoretical properties rather than empirically validated performance.
Edit intent: coherence
Original:  A few select results from the literature show evidence, that under certain conditions, expected improvement may perform worse than something as simple as the predicted value of a surrogate model.
Revised:  Few results from the literature show evidence, that under certain conditions, expected improvement may perform worse than something as simple as the predicted value of a surrogate model.
Edit intent: clarity
Original:  A few select results from the literature show evidence, that under certain conditions, expected improvement may perform worse than something as simple as the predicted value of a surrogate model.
Revised:  A few select results from the literature show evidence, that under certain conditions, expected improvement may perform worse than something as simple as the predicted value of the surrogate model.
Edit intent: fluency
Original:  Both infill criteria are benchmarked in an extensive empirical study on the `BBOB' function set.
Revised:  We benchmark both infill criteria in an extensive empirical study on the `BBOB' function set.
Edit intent: clarity
Original:  Importantly, this investigation includes a detailed study of the impact of problem dimensionality on algorithm performance.
Revised:  This investigation includes a detailed study of the impact of problem dimensionality on algorithm performance.
Edit intent: coherence
Original:  A statistical analysis reveals that the purely exploitative search with the predicted value criterion performs better on most problems of five or more dimensions.
Revised:  A statistical analysis reveals that the purely exploitative search with the predicted value criterion performs better on most problems of five or higher dimensions.
Edit intent: clarity
Original:  We give an in-depth guide for choosing between both infill criteria based on a priori knowledge about a given problem , its dimensionality, and the available evaluation budget.
Revised:  In addition, we give an in-depth guide for choosing between both infill criteria based on a priori knowledge about a given problem , its dimensionality, and the available evaluation budget.
Edit intent: coherence
Original:  We give an in-depth guide for choosing between both infill criteria based on a priori knowledge about a given problem , its dimensionality, and the available evaluation budget.
Revised:  We give an in-depth guide for choosing the infill criteria based on a priori knowledge about a given problem , its dimensionality, and the available evaluation budget.
Edit intent: clarity
Original:  We give an in-depth guide for choosing between both infill criteria based on a priori knowledge about a given problem , its dimensionality, and the available evaluation budget.
Revised:  We give an in-depth guide for choosing between both infill criteria based on prior knowledge about the problem at hand , its dimensionality, and the available evaluation budget.
Edit intent: clarity
Original:  We give an in-depth guide for choosing between both infill criteria based on a priori knowledge about a given problem , its dimensionality, and the available evaluation budget.
Revised:  We give an in-depth guide for choosing between both infill criteria based on a priori knowledge about a given problem , its dimensionality, and the available budget.
Edit intent: clarity
Original:  We study the parameterized complexity of Geodetic Set with respect to structural parameters and show dichotomy results: We develop fixed-parameter algorithms with respect to the feedback edge numberand with respect to the tree-depth ] . On the negative side, we prove that the problem is W 1%DIFDELCMD < ]%%% -hard with respect to the feedback vertex number and the path-width .
Revised:  We study the parameterized complexity of Geodetic Set and show positive and negative results: We develop fixed-parameter algorithms with respect to the feedback edge numberand with respect to the tree-depth ] . On the negative side, we prove that the problem is W 1%DIFDELCMD < ]%%% -hard with respect to the feedback vertex number and the path-width .
Edit intent: clarity
Original:  We study the parameterized complexity of Geodetic Set with respect to structural parameters and show dichotomy results: We develop fixed-parameter algorithms with respect to the feedback edge numberand with respect to the tree-depth ] . On the negative side, we prove that the problem is W 1%DIFDELCMD < ]%%% -hard with respect to the feedback vertex number and the path-width .
Revised:  We study the parameterized complexity of Geodetic Set with respect to structural parameters and show dichotomy results: We develop fixed-parameter algorithms with respect to the feedback edge numberand with respect to the tree-depth ] . On the positive side, we prove that the problem is W 1%DIFDELCMD < ]%%% -hard with respect to the feedback vertex number and the path-width .
Edit intent: fluency
Original:  We study the parameterized complexity of Geodetic Set with respect to structural parameters and show dichotomy results: We develop fixed-parameter algorithms with respect to the feedback edge numberand with respect to the tree-depth ] . On the negative side, we prove that the problem is W 1%DIFDELCMD < ]%%% -hard with respect to the feedback vertex number and the path-width .
Revised:  We study the parameterized complexity of Geodetic Set with respect to structural parameters and show dichotomy results: We develop fixed-parameter algorithms with respect to the feedback edge numberand with respect to the tree-depth ] . On the negative side, we 1%DIFDELCMD < ]%%% -hard with respect to the feedback vertex number and the path-width .
Edit intent: coherence
Original:  We study the parameterized complexity of Geodetic Set with respect to structural parameters and show dichotomy results: We develop fixed-parameter algorithms with respect to the feedback edge numberand with respect to the tree-depth ] . On the negative side, we prove that the problem is W 1%DIFDELCMD < ]%%% -hard with respect to the feedback vertex number and the path-width .
Revised:  We study the parameterized complexity of Geodetic Set with respect to structural parameters and show dichotomy results: We develop fixed-parameter algorithms with respect to the feedback edge numberand with respect to the tree-depth ] . On the negative side, we prove that the problem is W 1%DIFDELCMD < ]%%% develop fixed-parameter algorithms with respect to the feedback vertex number and the path-width .
Edit intent: coherence
Original:  We study the parameterized complexity of Geodetic Set with respect to structural parameters and show dichotomy results: We develop fixed-parameter algorithms with respect to the feedback edge numberand with respect to the tree-depth ] . On the negative side, we prove that the problem is W 1%DIFDELCMD < ]%%% -hard with respect to the feedback vertex number and the path-width .
Revised:  We study the parameterized complexity of Geodetic Set with respect to structural parameters and show dichotomy results: We develop fixed-parameter algorithms with respect to the feedback edge numberand with respect to the tree-depth ] . On the negative side, we prove that the problem is W 1%DIFDELCMD < ]%%% -hard with respect to the feedback edge number and with respect to the tree-depth .
Edit intent: clarity
Original:  On the positive side, we develop fixed-parameter algorithms with respect to the feedback edge number and with respect to the tree-depth  .
Revised:  On the positive side, we develop fixed-parameter algorithms with respect to the feedback edge number , the tree-depth .
Edit intent: coherence
Original:  Given a graph G, terminal vertices s and t, and an integer k, the Tracking Paths problem asks whether there exists at most k vertices, which if marked as trackers, would ensure that the sequence of trackers encountered in each s-t path is unique.
Revised:  Given a graph G, terminal vertices s and t, and an integer k, the Tracking Paths problem asks whether there exists at most k vertices, which if marked as trackers, would ensure that the sequence of trackers encountered in each s-t path is unique.
Edit intent: fluency
Original:  The problem was first proposed by Banik et al. in \mbox{ DBLP:conf/ciac/BanikKPS170pt , where they gave results for some restricted versions of the problem .
Revised:  0pt , where they gave results for some restricted versions of the problem .
Edit intent: coherence
Original: The emergence of autonomous vehicles ( AVs) is anticipated to influence the public transportation (PT) system .
Revised: The emerging autonomous vehicles ( AVs) is anticipated to influence the public transportation (PT) system .
Edit intent: fluency
Original: The emergence of autonomous vehicles ( AVs) is anticipated to influence the public transportation (PT) system .
Revised: The emergence of autonomous vehicles ( AV) can either supplement the public transportation (PT) system .
Edit intent: clarity
Original: The emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or be a competitor with it.
Revised: Emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or be a competitor with it.
Edit intent: clarity
Original: The emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or be a competitor with it.
Revised: The emerging autonomous vehicles (AV) can either supplement the public transportation (PT) system or compete with it.
Edit intent: clarity
Original:  For PT, after supply updates, spatially the services are concentrated to short feeder routes directly to the subway station , and temporally concentrated to peak hours.
Revised:  For PT, after supply updates, the PT services are spatially concentrated to shorter routes feeding directly to the subway station , and temporally concentrated to peak hours.
Edit intent: fluency
Original:  For PT, after supply updates, spatially the services are concentrated to short feeder routes directly to the subway station , and temporally concentrated to peak hours.
Revised:  For PT, after supply updates, spatially the services are concentrated to short feeder routes directly to the subway station and temporally concentrated to peak hours.
Edit intent: fluency
Original:  For passengers , the competition reduces their travel time but increases their travel costs.
Revised:  On average , the competition reduces their travel time but increases their travel costs.
Edit intent: clarity
Original:  For passengers , the competition reduces their travel time but increases their travel costs.
Revised:  For passengers , the competition reduces the travel time of passengers but increases their travel costs.
Edit intent: clarity
Original:  Nonetheless, the generalized travel cost is still reduced when counting the value of time.
Revised:  Nonetheless, the generalized travel cost is reduced when incorporating the value of time.
Edit intent: clarity
Original:  For system efficiencyand sustainability, bus supply adjustment can increase the bus average load and reduce total PCE , while the AV supply adjustment shows the oppositeeffect.
Revised:  With respect to the system efficiency, the bus supply adjustment can increase the bus average load and reduce total PCE , while the AV supply adjustment shows the oppositeeffect.
Edit intent: clarity
Original:  For system efficiencyand sustainability, bus supply adjustment can increase the bus average load and reduce total PCE , while the AV supply adjustment shows the oppositeeffect.
Revised:  For system efficiencyand sustainability, bus supply adjustment increases the average vehicle load and reduces the total vehicle kilometer traveled measured by the passenger car equivalent (PCE) , while the AV supply adjustment shows the oppositeeffect.
Edit intent: clarity
Original: Networks are natural geometric objects .
Revised: Real networks are finite metric spaces .
Edit intent: clarity