wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The introduction of separable functions also solves the issue of the domain allowing efficient vectorisation and load balancing. This method becomes unstable in certain cases and so we present a discussion of the optimisation of both approaches.
Revised:  The introduction of separable functions also solves the issue of the non-rectangular sparse grid. This separable method can become unstable in certain cases and so we present a discussion of the optimisation of both approaches.
Edit intent: meaning-changed
Original:  By making bispectrum calculations competitive with those for the power spectrum we are now able to consider joint analysis for cosmological science exploitation of new data. We demonstrate speed-ups of over 100x, arising from a combination of algorithmic improvements and architecture-aware optimizations targeted at improving thread and vectorization behaviour.
Revised:  We show significant speed-ups of over 100x, arising from a combination of algorithmic improvements and architecture-aware optimizations targeted at improving thread and vectorization behaviour.
Edit intent: clarity
Original:  The resulting MPI/OpenMP  code is capable of executing on clusters containing Intel(R) Xeon(R) processors and/or Intel(R) Xeon Phi(TM) coprocessors, with strong-scaling efficiency of 98.6\% on up to 16 nodes.
Revised:  The resulting MPI/OpenMP hybrid code is capable of executing on clusters containing Intel(R) Xeon(R) processors and/or Intel(R) Xeon Phi(TM) coprocessors, with strong-scaling efficiency of 98.6\% on up to 16 nodes.
Edit intent: meaning-changed
Original:  The resulting MPI/OpenMP  code is capable of executing on clusters containing Intel(R) Xeon(R) processors and/or Intel(R) Xeon Phi(TM) coprocessors, with strong-scaling efficiency of 98.6\% on up to 16 nodes.
Revised:  The resulting MPI/OpenMP code is capable of executing on clusters containing processors and/or Intel(R) Xeon Phi(TM) coprocessors, with strong-scaling efficiency of 98.6\% on up to 16 nodes.
Edit intent: fluency
Original:  The resulting MPI/OpenMP  code is capable of executing on clusters containing Intel(R) Xeon(R) processors and/or Intel(R) Xeon Phi(TM) coprocessors, with strong-scaling efficiency of 98.6\% on up to 16 nodes.
Revised:  The resulting MPI/OpenMP code is capable of executing on clusters containing Intel(R) Xeon(R) processors and/or coprocessors, with strong-scaling efficiency of 98.6\% on up to 16 nodes.
Edit intent: meaning-changed
Original:  We find that a single coprocessor outperforms two processor sockets by a factor of 1.3x and that running the same code across a combination of processors and coprocessors improves performance-per-node by a factor of 3.38x  .
Revised:  We find that a single coprocessor outperforms two processor sockets by a factor of 1.3x and that running the same code across a combination of both microarchitectures improves performance-per-node by a factor of 3.38x .
Edit intent: clarity
Original:  We find that a single coprocessor outperforms two processor sockets by a factor of 1.3x and that running the same code across a combination of processors and coprocessors improves performance-per-node by a factor of 3.38x  .
Revised:  We find that a single coprocessor outperforms two processor sockets by a factor of 1.3x and that running the same code across a combination of processors and coprocessors improves performance-per-node by a factor of 3.38x . By making bispectrum calculations competitive with those for the power spectrum (or two-point correlator) we are now able to consider joint analysis for cosmological science exploitation of new data .
Edit intent: meaning-changed
Original:  Aiming to handle the incompleteness problem as well as estimate travel speed accurately, in this paper, we propose an approach named STC in which spatial-temporal correlation among travel speeds of roads is leveraged.
Revised:  Aiming to handle the incompleteness problem as well as estimate travel speed accurately, in this paper, we propose an approach named STC that exploits the spatial-temporal correlation among travel speeds of roads is leveraged.
Edit intent: clarity
Original:  Aiming to handle the incompleteness problem as well as estimate travel speed accurately, in this paper, we propose an approach named STC in which spatial-temporal correlation among travel speeds of roads is leveraged. More specifically, in STC, we use cross correlation function to quantize the correlation among roads.
Revised:  Aiming to handle the incompleteness problem as well as estimate travel speed accurately, in this paper, we propose an approach named STC in which spatial-temporal correlation among travel speeds for roads by introducing the time-lagged cross correlation function to quantize the correlation among roads.
Edit intent: clarity
Original:  More specifically, in STC, we use cross correlation function to quantize the correlation among roads. Noticing the directional patterns of traffic feature diffusion  , we novelly make the determination of the time lagging factor in cross correlation calculation self-adaptive by using vehicle tracking .
Revised:  More specifically, in STC, we use cross correlation function . The time lagging factor describes the time consumption of traffic feature diffusion , we novelly make the determination of the time lagging factor in cross correlation calculation self-adaptive by using vehicle tracking .
Edit intent: meaning-changed
Original:  Elaborate experiments based on real taxi trace data show that STC can commendably settle the incompleteness problem of vehicle crowdsensing data based travel speed estimation and ensure the accuracy of estimated travel speed  , in comparison with representative existing methods  .
Revised:  Elaborate experiments based on real taxi trace data show that STC can settle the incompleteness problem of vehicle crowdsensing data based travel speed estimation and ensure the accuracy of estimated travel speed , in comparison with representative existing methods .
Edit intent: clarity
Original:  Elaborate experiments based on real taxi trace data show that STC can commendably settle the incompleteness problem of vehicle crowdsensing data based travel speed estimation and ensure the accuracy of estimated travel speed  , in comparison with representative existing methods  .
Revised:  Elaborate experiments based on real taxi trace data show that STC can commendably settle the incompleteness problem of vehicle crowdsensing data based travel speed estimation and ensure the accuracy of estimated travel speed better , in comparison with representative existing methods .
Edit intent: meaning-changed
Original:  Elaborate experiments based on real taxi trace data show that STC can commendably settle the incompleteness problem of vehicle crowdsensing data based travel speed estimation and ensure the accuracy of estimated travel speed  , in comparison with representative existing methods  .
Revised:  Elaborate experiments based on real taxi trace data show that STC can commendably settle the incompleteness problem of vehicle crowdsensing data based travel speed estimation and ensure the accuracy of estimated travel speed , in comparison with representative existing methods such as KNN, Kriging and ARIMA .
Edit intent: meaning-changed
Original: Network biology approaches have over the last decade proven to be very useful for the integration and generation of functional hypothesis by providing a context for specific molecular components and processes. Recent experimental and computational techniques yield networksof increased size and sophistication. The study of these complex cellular networks is emerging as a new challenge in biology. A number of dimensionality reduction techniques for graphs have been developed to cope with complexity of networks .
Revised: To cope with the complexity of large networks, a number of dimensionality reduction techniques for graphs have been developed to cope with complexity of networks .
Edit intent: clarity
Original:  Here we therefore develop a rigorous framework, based on algorithmic information theory, to quantify the capability to preserve information when network motif analysis, graph spectra and sparsification methods respectively, are applied to over twenty different well-established networks.
Revised:  Here we develop a framework, based on algorithmic information theory, to quantify the capability to preserve information when network motif analysis, graph spectra and sparsification methods respectively, are applied to over twenty different well-established networks.
Edit intent: clarity
Original:  Here we therefore develop a rigorous framework, based on algorithmic information theory, to quantify the capability to preserve information when network motif analysis, graph spectra and sparsification methods respectively, are applied to over twenty different well-established networks.
Revised:  Here we therefore develop a rigorous framework, based on algorithmic information theory, to quantify the extent to which information is preserved when network motif analysis, graph spectra and sparsification methods respectively, are applied to over twenty different well-established networks.
Edit intent: clarity
Original:  Here we therefore develop a rigorous framework, based on algorithmic information theory, to quantify the capability to preserve information when network motif analysis, graph spectra and sparsification methods respectively, are applied to over twenty different well-established networks.
Revised:  Here we therefore develop a rigorous framework, based on algorithmic information theory, to quantify the capability to preserve information when network motif analysis, graph spectra and spectral sparsification methods are applied to over twenty different well-established networks.
Edit intent: clarity
Original:  Here we therefore develop a rigorous framework, based on algorithmic information theory, to quantify the capability to preserve information when network motif analysis, graph spectra and sparsification methods respectively, are applied to over twenty different well-established networks.
Revised:  Here we therefore develop a rigorous framework, based on algorithmic information theory, to quantify the capability to preserve information when network motif analysis, graph spectra and sparsification methods respectively, are applied to over twenty different biological and artificial networks.
Edit intent: clarity
Original:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Revised:  We find that the spectral sparsification is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Edit intent: clarity
Original:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Revised:  We find that the sparsification method is highly sensitive to high number of edge deletion, leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Edit intent: meaning-changed
Original:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Revised:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies , and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Edit intent: coherence
Original:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Revised:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods are the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Edit intent: fluency
Original:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Revised:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular , capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Edit intent: clarity
Original:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but in that process largely lost the information content of the original networks.
Revised:  We find that the sparsification method is highly sensitive to deletion of edges leading to significant inconsistencies with respect to the loss of information and that graph spectral methods were the most irregular measure only capturing algebraic information in a condensed fashion but largely losing most of the information content of the original networks.
Edit intent: clarity
Original:   Our algorithmic information methodology therefore provides a rigorous framework enabling  fundamental assessment and comparison between different methods for reducing the complexity of networks while preserving key structures in the networks thereby facilitating the identification of such core processes .
Revised:  However, the approach shows that network motif analysis excels at preserving the relative algorithmic information content of a network, hence validating and generalizing the remarkable fact that despite their inherent combinatorial possibilities, local regularities preserve information to such an extent that essential properties are fully recoverable across different networks to determine their family group to which they belong to (eg genetic vs social network). Our algorithmic information methodology therefore provides a rigorous framework enabling fundamental assessment and comparison between different methods for reducing the complexity of networks while preserving key structures in the networks thereby facilitating the identification of such core processes .
Edit intent: meaning-changed
Original:   Our algorithmic information methodology therefore provides a rigorous framework enabling  fundamental assessment and comparison between different methods for reducing the complexity of networks while preserving key structures in the networks thereby facilitating the identification of such core processes .
Revised:  Our algorithmic information methodology thus provides a rigorous framework enabling fundamental assessment and comparison between different methods for reducing the complexity of networks while preserving key structures in the networks thereby facilitating the identification of such core processes .
Edit intent: fluency
Original:   Our algorithmic information methodology therefore provides a rigorous framework enabling  fundamental assessment and comparison between different methods for reducing the complexity of networks while preserving key structures in the networks thereby facilitating the identification of such core processes .
Revised:  Our algorithmic information methodology therefore provides a rigorous framework enabling a fundamental assessment and comparison between different methods for reducing the complexity of networks while preserving key structures in the networks thereby facilitating the identification of such core processes .
Edit intent: fluency
Original:   Our algorithmic information methodology therefore provides a rigorous framework enabling  fundamental assessment and comparison between different methods for reducing the complexity of networks while preserving key structures in the networks thereby facilitating the identification of such core processes .
Revised:  Our algorithmic information methodology therefore provides a rigorous framework enabling fundamental assessment and comparison between different methods for reducing the complexity of networks while preserving key structures in the networks thereby facilitating the identification and evaluation of the capabilities of old and new methods .
Edit intent: meaning-changed
Original:  A complex topology of channels in proteins leads often to difficulties in modeling ligand escape pathways by classical molecular dynamics simulations. In this paper two novel memetic methods for searching the exit paths and cavity space exploration are proposed: Memory Enhanced Random Acceleration (MERA)Molecular Dynamics and Immune Algorithm (IA ).
Revised:  A complex topology of channels in proteins leads often to difficulties in modeling ligand escape pathways by standard molecular dynamics (MD) simulations. Here, two novel methods for searching the exit paths and cavity space exploration are proposed: Memory Enhanced Random Acceleration (MERA)Molecular Dynamics and Immune Algorithm (IA ).
Edit intent: clarity
Original:  In this paper two novel memetic methods for searching the exit paths and cavity space exploration are proposed: Memory Enhanced Random Acceleration (MERA)Molecular Dynamics and Immune Algorithm (IA ).
Revised:  In this paper two novel memetic methods for searching of ligand exit pathways and cavity exploration are proposed: Memory Enhanced Random Acceleration (MERA)Molecular Dynamics and Immune Algorithm (IA ).
Edit intent: meaning-changed
Original:  They are tested on three protein channels with increasing complexity  : M2 muscarinic GPCR receptor, enzyme nitrile hydrataseand heme-protein cytochrome P450cam.
Revised:  They are tested on three protein channels with increasing complexity of tunnels : M2 muscarinic GPCR receptor, enzyme nitrile hydrataseand heme-protein cytochrome P450cam.
Edit intent: clarity
Original:  The rationale behind this choice is that gene coexpression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. . To extract the relevant biological information from the multiplex we studied its partition into communities.
Revised:  The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. . To extract the relevant biological information from the multiplex we studied its partition into communities.
Edit intent: fluency
Original:  The rationale behind this choice is that gene coexpression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. . To extract the relevant biological information from the multiplex we studied its partition into communities.
Revised:  The rationale behind this choice is that gene coexpression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multiplex we studied its partition into communities.
Edit intent: fluency
Original:  The rationale behind this choice is that gene coexpression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. . To extract the relevant biological information from the multiplex we studied its partition into communities.
Revised:  The rationale behind this choice is that gene coexpression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. . To extract the relevant biological information from the multi-network we studied its partition into communities.
Edit intent: clarity
Original:  Even if our procedure is valid in principle for any pathology in this work we concentrate in particular on the gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multiplex communities a set of candidate driver cancer genes.
Revised:  Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multiplex communities a set of candidate driver cancer genes.
Edit intent: clarity
Original:  Even if our procedure is valid in principle for any pathology in this work we concentrate in particular on the gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multiplex communities a set of candidate driver cancer genes.
Revised:  Even if our procedure is valid in principle for any pathology in this work we concentrate in particular on the gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes.
Edit intent: clarity
Original: In a great many of the contexts in which groups of cilia or flagella are found they exhibit synchronized behavior . This includes phase-locking , as seen in Chlamydomonas flagella {\it  , and metachronal wave formation in the ciliary arrays of Paramecium or in the respiratory cilia of URLanisms.
Revised: In a great many of the contexts in which groups of cilia or flagella are found they exhibit synchronized behavior ; this includes phase locking , as seen in Chlamydomonas flagella {\it , and metachronal wave formation in the ciliary arrays of Paramecium or in the respiratory cilia of URLanisms.
Edit intent: fluency
Original:  This includes phase-locking , as seen in Chlamydomonas flagella {\it  , and metachronal wave formation in the ciliary arrays of Paramecium or in the respiratory cilia of URLanisms.
Revised:  This includes phase-locking , as seen in {\it , and metachronal wave formation in the ciliary arrays of Paramecium or in the respiratory cilia of URLanisms.
Edit intent: clarity