wanyu/IteraTeR_v2 huggingface dataset:
Original:  Assuming that the fire ant was introduced to a region , and monitoring was conducted immediately after a nest was detected in order to detect all other potentially established nests, we developed a mathematical model to investigate the detection rate .
Revised:  Assuming that the fire ant was introduced to a region , and monitoring was conducted immediately after a nest was detected in order to detect all other potentially established nests, we developed a mathematical model to investigate detection rates .
Edit intent: fluency
Original:  Setting the limit to three years, the detection rate is maximized when monitoring was conducted twice devising the location of traps while setting them at intervals of 30 m in each monitoring.
Revised:  Setting the monitoring limit to 3 years, the detection rate is maximized when monitoring was conducted twice devising the location of traps while setting them at intervals of 30 m in each monitoring.
Edit intent: fluency
Original:  Setting the limit to three years, the detection rate is maximized when monitoring was conducted twice devising the location of traps while setting them at intervals of 30 m in each monitoring.
Revised:  Setting the limit to three years, the detection rate was maximized when monitoring was conducted twice devising the location of traps while setting them at intervals of 30 m in each monitoring.
Edit intent: fluency
Original:  Setting the limit to three years, the detection rate is maximized when monitoring was conducted twice devising the location of traps while setting them at intervals of 30 m in each monitoring.
Revised:  Setting the limit to three years, the detection rate is maximized when monitoring was conducted shifting bait trap locations and setting them at intervals of 30 m in each monitoring.
Edit intent: clarity
Original:  Setting the limit to three years, the detection rate is maximized when monitoring was conducted twice devising the location of traps while setting them at intervals of 30 m in each monitoring.
Revised:  Setting the limit to three years, the detection rate is maximized when monitoring was conducted twice devising the location of traps while setting them at intervals of 30 m for each monitoring.
Edit intent: clarity
Original:  Monitoring should be conducted  at least 4 km round the source nest, and wider depending on how late a nest was found.
Revised:  Monitoring should be conducted in a radius of at least 4 km round the source nest, and wider depending on how late a nest was found.
Edit intent: clarity
Original:  Monitoring should be conducted  at least 4 km round the source nest, and wider depending on how late a nest was found.
Revised:  Monitoring should be conducted at least 4 km around the source nest, and wider depending on how late a nest was found.
Edit intent: fluency
Original:  Monitoring should be conducted  at least 4 km round the source nest, and wider depending on how late a nest was found.
Revised:  Monitoring should be conducted at least 4 km round the source nest, or wider---depending on how late a nest was found.
Edit intent: fluency
Original:  The minimum trap interval required in an arbitrary environment for thorough monitoring in the early stage of invasion is also shown .
Revised:  The minimum trap interval required in an arbitrary ant species for thorough monitoring in the early stage of invasion is also shown .
Edit intent: clarity
Original:  The minimum trap interval required in an arbitrary environment for thorough monitoring in the early stage of invasion is also shown .
Revised:  The minimum trap interval required in an arbitrary environment for thorough monitoring .
Edit intent: clarity
Original:  The key to successful eradication is thorough nest monitoring and destruction during early invasion to prevent range expansion.
Revised:  An important aspect of eradication is thorough nest monitoring and destruction during early invasion to prevent range expansion.
Edit intent: clarity
Original:  Setting the monitoring limit to 3 years, the detection rate was maximized when monitoring was conducted shifting bait trap locations and setting them at intervals of 30 m for each monitoring.
Revised:  Setting the monitoring limit to three years, the detection rate was maximized when monitoring was conducted shifting bait trap locations and setting them at intervals of 30 m for each monitoring.
Edit intent: fluency
Original:  Monitoring should be conducted in a radius of at least 4 km around the source nest, or wider---depending on how late a nest is found.
Revised:  Monitoring should be conducted in a radius of at least 4 km around the source nest, or wider --depending on how late a nest is found.
Edit intent: fluency
Original:  We build on the assumptions made in previous work, and propagate the muon flux from supernova-initiated cosmic rays from the surface to the depths of the ocean.
Revised:  We build on previous work, and propagate the muon flux from supernova-initiated cosmic rays from the surface to the depths of the ocean.
Edit intent: clarity
Original:  In our proposed scheme the features of both 2D-video and depth-map components are first extracted . Next, master shares and ownership shares are generated from these features and their relevant copyright information based on (2,2) visual secret sharing scheme.
Revised:  In our proposed scheme the features of both 2D-video and ownership shares are generated from these features and their relevant copyright information based on (2,2) visual secret sharing scheme.
Edit intent: clarity
Original:  Different with traditional zero-watermark schemes, both the features and ownership shares are stored in relevant databases.
Revised:  Different with traditional zero-watermark schemes, features and ownership shares are stored in relevant databases.
Edit intent: clarity
Original:  which outperforms the traditional zero-watermark schemes, but also identifies the copyrights of 2D-video and depth-map components of 3D videos reliably, independently and simultaneously without any content distortion or watermark-embedding limitation, which outperforms existing 2D-video based and depth-map based watermark schemes for protecting 3D video .
Revised:  which outperforms the traditional zero-watermark schemes, but also identifies the copyrights of 2D-video and depth-map components of 3D videos .
Edit intent: clarity
Original:  (2) We show a "parameterized complexity" type result by showing that for k-QSAT instances from a certain non-trivial class, computing explicit solutions reduces to solving for a single root of a single univariate polynomial , where the degree of the polynomial depends on the "parameters" . (3) We conduct a structural graph theoretic study of 3-QSAT interaction graphs which have a "matching".
Revised:  (2) We give a parameterized algorithm for k-QSAT instances from a certain non-trivial class, computing explicit solutions reduces to solving for a single root of a single univariate polynomial , where the degree of the polynomial depends on the "parameters" . (3) We conduct a structural graph theoretic study of 3-QSAT interaction graphs which have a "matching".
Edit intent: clarity
Original:  (2) We show a "parameterized complexity" type result by showing that for k-QSAT instances from a certain non-trivial class, computing explicit solutions reduces to solving for a single root of a single univariate polynomial , where the degree of the polynomial depends on the "parameters" . (3) We conduct a structural graph theoretic study of 3-QSAT interaction graphs which have a "matching".
Revised:  (2) We show a "parameterized complexity" type result by showing that for k-QSAT instances from a certain non-trivial class, computing explicit solutions reduces to solving for a single root of a single univariate polynomial . (3) We conduct a structural graph theoretic study of 3-QSAT interaction graphs which have a "matching".
Edit intent: coherence
Original: We study tensor networks as a model of arithmetic computation for evaluating multilinear maps.
Revised: We study tensor networks as a model of arithmetic computation for evaluating multilinear maps.
Edit intent: fluency
Original:  This model turns out to be strong enough to capture the current best algorithms for a variety of problems, e.g., O(n^{\omega + \epsilon}) time matrix multiplication, O(n \log n) time discrete Fourier transform, O(n^{(\omega+\epsilon)t + j }) time (3t+j)-clique counting and O^*(2^n) time for computing the permanent of a matrix.
Revised:  This model turns out to be strong enough to capture the current best algorithms for a variety of problems, e.g., O(n^{\omega + \epsilon}) time matrix multiplication, O(n \log n) time discrete Fourier transform, O(n^{(\omega+\epsilon)t }) time (3t+j)-clique counting and O^*(2^n) time for computing the permanent of a matrix.
Edit intent: clarity
Original:  This model turns out to be strong enough to capture the current best algorithms for a variety of problems, e.g., O(n^{\omega + \epsilon}) time matrix multiplication, O(n \log n) time discrete Fourier transform, O(n^{(\omega+\epsilon)t + j }) time (3t+j)-clique counting and O^*(2^n) time for computing the permanent of a matrix.
Revised:  This model turns out to be strong enough to capture the current best algorithms for a variety of problems, e.g., O(n^{\omega + \epsilon}) time matrix multiplication, O(n \log n) time discrete Fourier transform, O(n^{(\omega+\epsilon)t + j }) time 3t-clique counting and O^*(2^n) time for computing the permanent of a matrix.
Edit intent: clarity
Original:    While powerful, the model still has limitations, and we are able to show a number of unconditional lower bounds for various linear maps, including: (a) an \Omega(n^{ %DIFDELCMD < \lceil %%% 2v/3%DIFDELCMD < \rceil%%%  }) time lower bound for v-clique , matching the upper bound if \omega = 2.  (b) an \Omega(2^{0.918n}) time lower bound for the permanent of an n \times n matrix.
Revised:  While powerful, the model still has limitations, and we are able to show a number of unconditional lower bounds for various linear maps, including: (a) an \Omega(n^{ %DIFDELCMD < \lceil %%% %DIFDELCMD < \rceil%%% }) time lower bound for v-clique , matching the upper bound if \omega = 2. (b) an \Omega(2^{0.918n}) time lower bound for the permanent of an n \times n matrix.
Edit intent: coherence
Original:  This model turns out to be strong enough to capture the current best algorithms for a variety of problems, e.g., O(n^{\omega+\epsilon}) time matrix multiplication,  O(n \log n) time discrete Fourier transform , O(n^{(\omega+\epsilon)t and O^*(2^n) time for computing the permanent of a matrix.
Revised:  This model turns out to be strong enough to capture the current best algorithms for a variety of problems, e.g., O(n^{\omega+\epsilon}) time matrix multiplication, O(n \log n) time discrete Fourier transform and O^*(2^n) time for computing the permanent of a matrix.
Edit intent: coherence
Original:  While powerful, the model still has limitations, and we are able to show a number of unconditional lower bounds for various linear maps, including: (a) an \Omega(n^{bw(P)}) time lower bound for counting homomorphisms from P to an n-vertex graph, matching the upper bound if \omega = 2. In particular for P a v-clique this yields an \Omega(n^{\lceil 2v/3 \rceil}) time lower bound for counting v-cliques . (b) an \Omega(2^{0.918n) time lower bound for the permanent of an n \times n matrix.
Revised:  While powerful, the model still has limitations, and we are able to show a number of unconditional lower bounds for various multilinear maps, including: (a) an \Omega(n^{bw(P)}) time lower bound for counting homomorphisms from P to an n-vertex graph, matching the upper bound if \omega = 2. In particular for P a v-clique this yields an \Omega(n^{\lceil 2v/3 \rceil}) time lower bound for counting v-cliques . (b) an \Omega(2^{0.918n) time lower bound for the permanent of an n \times n matrix.
Edit intent: clarity
Original:  (c) an }  \Omega(n ^4 ) time lower bound for the trilinear map D_{ijk = l A_{ijl}B_{ikl} C_{jkl} generalizing matrix multiplication, taking three n \times n \times n tensors A, B, C and producing an n \times n \times n tensor D, } \ge  ruling out tensor networks as an approach to obtaining non-trivial algorithms for hyperclique counting and the Max-3-CSP problem ) time lower bound for the permanent of an n \times n matrix} .
Revised:  (c) an } \Omega(n ^v ) time lower bound for the trilinear map D_{ijk = l A_{ijl}B_{ikl} C_{jkl} generalizing matrix multiplication, taking three n \times n \times n tensors A, B, C and producing an n \times n \times n tensor D, } \ge ruling out tensor networks as an approach to obtaining non-trivial algorithms for hyperclique counting and the Max-3-CSP problem ) time lower bound for the permanent of an n \times n matrix} .
Edit intent: clarity
Original:  (c) an }  \Omega(n ^4 ) time lower bound for the trilinear map D_{ijk = l A_{ijl}B_{ikl} C_{jkl} generalizing matrix multiplication, taking three n \times n \times n tensors A, B, C and producing an n \times n \times n tensor D, } \ge  ruling out tensor networks as an approach to obtaining non-trivial algorithms for hyperclique counting and the Max-3-CSP problem ) time lower bound for the permanent of an n \times n matrix} .
Revised:  (c) an } \Omega(n ^4 ) time lower bound for = l A_{ijl}B_{ikl} C_{jkl} generalizing matrix multiplication, taking three n \times n \times n tensors A, B, C and producing an n \times n \times n tensor D, } \ge ruling out tensor networks as an approach to obtaining non-trivial algorithms for hyperclique counting and the Max-3-CSP problem ) time lower bound for the permanent of an n \times n matrix} .
Edit intent: clarity
Original:  (c) an }  \Omega(n ^4 ) time lower bound for the trilinear map D_{ijk = l A_{ijl}B_{ikl} C_{jkl} generalizing matrix multiplication, taking three n \times n \times n tensors A, B, C and producing an n \times n \times n tensor D, } \ge  ruling out tensor networks as an approach to obtaining non-trivial algorithms for hyperclique counting and the Max-3-CSP problem ) time lower bound for the permanent of an n \times n matrix} .
Revised:  (c) an } \Omega(n ^4 ) time lower bound for the trilinear map D_{ijk = l A_{ijl}B_{ikl} C_{jkl} generalizing matrix multiplication, taking three n \times n \times n tensors A, B, C and producing an n \times n \times n tensor D, } k\ge ruling out tensor networks as an approach to obtaining non-trivial algorithms for hyperclique counting and the Max-3-CSP problem ) time lower bound for the permanent of an n \times n matrix} .
Edit intent: fluency
Original:  the source code is hosted on GitHub at URL  Comprehensive and continually-updated documentation is available at URL  The pyphi-users mailing list can be joined at URL  A web-based graphical interface to the software is available at URL 
Revised:  the source code is hosted on GitHub at URL . Comprehensive and continually-updated documentation is available at URL The pyphi-users mailing list can be joined at URL A web-based graphical interface to the software is available at URL 
Edit intent: fluency
Original:  the source code is hosted on GitHub at URL  Comprehensive and continually-updated documentation is available at URL  The pyphi-users mailing list can be joined at URL  A web-based graphical interface to the software is available at URL 
Revised:  the source code is hosted on GitHub at URL Comprehensive and continually-updated documentation is available at URL . The pyphi-users mailing list can be joined at URL A web-based graphical interface to the software is available at URL 
Edit intent: fluency
Original:  the source code is hosted on GitHub at URL  Comprehensive and continually-updated documentation is available at URL  The pyphi-users mailing list can be joined at URL  A web-based graphical interface to the software is available at URL 
Revised:  the source code is hosted on GitHub at URL Comprehensive and continually-updated documentation is available at URL The pyphi-users mailing list can be joined at URL . A web-based graphical interface to the software is available at URL 
Edit intent: fluency
Original:  the source code is hosted on GitHub at URL  Comprehensive and continually-updated documentation is available at URL  The pyphi-users mailing list can be joined at URL  A web-based graphical interface to the software is available at URL 
Revised:  the source code is hosted on GitHub at URL Comprehensive and continually-updated documentation is available at URL The pyphi-users mailing list can be joined at URL A web-based graphical interface to the software is available at URL .
Edit intent: fluency
Original: A major branch of anomaly detection methods rely on dynamic networks: raw sequential data is first converted to a series of networks, then critical change points are identified in the evolving network structure.
Revised: A major branch of anomaly detection methods relies on dynamic networks: raw sequential data is first converted to a series of networks, then critical change points are identified in the evolving network structure.
Edit intent: fluency
Original: A major branch of anomaly detection methods rely on dynamic networks: raw sequential data is first converted to a series of networks, then critical change points are identified in the evolving network structure.
Revised: A major branch of anomaly detection methods rely on dynamic networks: raw sequence data is first converted to a series of networks, then critical change points are identified in the evolving network structure.
Edit intent: fluency
Original:  However, existing approaches use the first-order network (FON ) to represent the underlying raw data, which may lose important higher-order sequence patterns, making higher-order anomalies undetectable in subsequent analysis.
Revised:  However, existing approaches use first-order network (FON ) to represent the underlying raw data, which may lose important higher-order sequence patterns, making higher-order anomalies undetectable in subsequent analysis.
Edit intent: fluency
Original:  However, existing approaches use the first-order network (FON ) to represent the underlying raw data, which may lose important higher-order sequence patterns, making higher-order anomalies undetectable in subsequent analysis.
Revised:  However, existing approaches use the first-order networks (FONs ) to represent the underlying raw data, which may lose important higher-order sequence patterns, making higher-order anomalies undetectable in subsequent analysis.
Edit intent: clarity
Original:  A natural problem is to find the number of characters that need to be queried (i.e. the decision tree complexity) in a string in order to determine whether this string contains a certain pattern.
Revised:  A natural problem is to determine the number of characters that need to be queried (i.e. the decision tree complexity) in a string in order to determine whether this string contains a certain pattern.
Edit intent: coherence
Original:  A natural problem is to find the number of characters that need to be queried (i.e. the decision tree complexity) in a string in order to determine whether this string contains a certain pattern.
Revised:  A natural problem is to find the number of characters that need to be queried (i.e. the decision tree complexity) in a string in order to decide whether this string contains a certain pattern.
Edit intent: fluency
Original:  He further conjectured that these bounds are tight.
Revised:  He further conjectured that this bound is tight.
Edit intent: fluency
Original:  By using adversary methods , Tuza disproved this conjecture and showed that more than  half of binary patterns are evasive {\em  , i.e. any algorithm needs to query all the characters  .
Revised:  By using the adversary method , Tuza disproved this conjecture and showed that more than half of binary patterns are evasive {\em , i.e. any algorithm needs to query all the characters .
Edit intent: fluency
Original:  By using adversary methods , Tuza disproved this conjecture and showed that more than  half of binary patterns are evasive {\em  , i.e. any algorithm needs to query all the characters  .
Revised:  By using adversary methods , Tuza disproved this conjecture and showed that more than one half of binary patterns are evasive {\em , i.e. any algorithm needs to query all the characters .
Edit intent: clarity
Original:  By using adversary methods , Tuza disproved this conjecture and showed that more than  half of binary patterns are evasive {\em  , i.e. any algorithm needs to query all the characters  .
Revised:  By using adversary methods , Tuza disproved this conjecture and showed that more than half of binary patterns are {\em , i.e. any algorithm needs to query all the characters .
Edit intent: clarity
Original:  Using the algebraic approach of Rivest and Vuillemin we give a new sufficient condition for the evasiveness of patterns, which  reveals an interesting connection to Skolem's Problem  .
Revised:  Using the algebraic approach of Rivest and Vuillemin , we also give a new sufficient condition for the evasiveness of patterns, which reveals an interesting connection to Skolem's Problem .
Edit intent: coherence
Original:  Using the algebraic approach of Rivest and Vuillemin we give a new sufficient condition for the evasiveness of patterns, which  reveals an interesting connection to Skolem's Problem  .
Revised:  Using the algebraic approach of Rivest and Vuillemin we give a new sufficient condition for the evasiveness of patterns, which reveals an interesting connection to Skolem's Problem .
Edit intent: fluency
Original:  Often, a user may have  flexibility to accept combinations that weight these criteria differently, within limits.
Revised:  A user may have flexibility to accept combinations that weight these criteria differently, within limits.
Edit intent: coherence
Original:  Often, a user may have  flexibility to accept combinations that weight these criteria differently, within limits.
Revised:  Often, a user may have the flexibility to accept combinations that weight these criteria differently, within limits.
Edit intent: fluency
Original:  Often, a user may have  flexibility to accept combinations that weight these criteria differently, within limits.
Revised:  Often, a user may have flexibility to accept combinations that weigh these criteria differently, within limits.
Edit intent: fluency
Original:  On the other hand, this choice of weights can greatly affect the fairness of the ranking produced .
Revised:  On the other hand, this choice of weights can greatly affect the fairness of the produced ranking .
Edit intent: fluency
Original:  Each ranking function can then be expressed as a vector of weights, or  a point in a multi-dimensional space.
Revised:  Each ranking function can be expressed as a vector of weights, or a point in a multi-dimensional space.
Edit intent: coherence
Original:  Each ranking function can then be expressed as a vector of weights, or  a point in a multi-dimensional space.
Revised:  Each ranking function can then be expressed as a vector of weights, or as a point in a multi-dimensional space.
Edit intent: fluency
Original:  Using this identification  , our system is able to tell users whether their proposed ranking function satisfies the desired fairness criteria and  if it does not, to suggest the smallest modification which does .
Revised:  Using this identification method , our system is able to tell users whether their proposed ranking function satisfies the desired fairness criteria and if it does not, to suggest the smallest modification which does .
Edit intent: clarity
Original:  Using this identification  , our system is able to tell users whether their proposed ranking function satisfies the desired fairness criteria and  if it does not, to suggest the smallest modification which does .
Revised:  Using this identification , our system is able to tell users whether their proposed ranking function satisfies the desired fairness criteria and , if it does not, to suggest the smallest modification which does .
Edit intent: fluency