wanyu/IteraTeR_v2 huggingface dataset:
Original:  Video information from the measurements is used to separate the line-of-sight (LOS) condition from the obstructed line-of-sight (OLOS) by vehicles  and non line-of-sight (NLOS) by buildings  .
Revised:  Video information from the measurements is used to separate the line-of-sight (LOS) , obstructed line-of-sight (OLOS) by vehicles and non line-of-sight (NLOS) by buildings .
Edit intent: clarity
Original:  Video information from the measurements is used to separate the line-of-sight (LOS) condition from the obstructed line-of-sight (OLOS) by vehicles  and non line-of-sight (NLOS) by buildings  .
Revised:  Video information from the measurements is used to separate the line-of-sight (LOS) condition from the obstructed line-of-sight (OLOS) by vehicles , and non line-of-sight (NLOS) by buildings .
Edit intent: fluency
Original:  Video information from the measurements is used to separate the line-of-sight (LOS) condition from the obstructed line-of-sight (OLOS) by vehicles  and non line-of-sight (NLOS) by buildings  .
Revised:  Video information from the measurements is used to separate the line-of-sight (LOS) condition from the obstructed line-of-sight (OLOS) by vehicles and non line-of-sight (NLOS) by buildings with the help of video information available during measurements .
Edit intent: coherence
Original:  It is observed that the vehicles obstructing LOS induce an additional attenuation of about 10 dB in the received signal power.
Revised:  It is observed that vehicles obstructing the LOS induce an additional attenuation of about 10 dB in the received signal power.
Edit intent: fluency
Original:  We also provide a simple recipe, how to incorporate our shadow fading model in VANET network simulators  .
Revised:  We also provide a simple recipe, to incorporate our shadow fading model in VANET network simulators .
Edit intent: clarity
Original: The Vehicle-to-Vehicle (V2V) propagation channel has significant implications on the design and performance of novel communication protocols for Vehicular Ad Hoc Networks (VANETs).
Revised: The vehicle-to-vehicle (V2V) propagation channel has significant implications on the design and performance of novel communication protocols for Vehicular Ad Hoc Networks (VANETs).
Edit intent: fluency
Original: The Vehicle-to-Vehicle (V2V) propagation channel has significant implications on the design and performance of novel communication protocols for Vehicular Ad Hoc Networks (VANETs).
Revised: The Vehicle-to-Vehicle (V2V) propagation channel has significant implications on the design and performance of novel communication protocols for vehicular ad hoc networks (VANETs).
Edit intent: fluency
Original:  In this paper we present a simple shadow fading model targeting system simulations based on real world measurements performed in urban and highway scenarios.
Revised:  In this paper we present a simple shadow fading model targeting system simulations based on real measurements performed in urban and highway scenarios.
Edit intent: clarity
Original:  The measurement data is separated for the situations line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight (NLOS) by buildings  with the help of video information available during measurements.
Revised:  The measurement data is separated into three categories, line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight (NLOS) by buildings with the help of video information available during measurements.
Edit intent: clarity
Original:  The measurement data is separated for the situations line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight (NLOS) by buildings  with the help of video information available during measurements.
Revised:  The measurement data is separated for the situations line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight (NLOS) by buildings , with the help of video information available during measurements.
Edit intent: fluency
Original:  The measurement data is separated for the situations line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight (NLOS) by buildings  with the help of video information available during measurements.
Revised:  The measurement data is separated for the situations line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight (NLOS) by buildings with the help of video information , recorded during the measurements.
Edit intent: clarity
Original:  In this paper we present a simple shadow fading model targeting system simulations based on real measurements performed in urban and highway scenarios.
Revised:  In this paper we present a shadow fading model targeting system simulations based on real measurements performed in urban and highway scenarios.
Edit intent: clarity
Original:  The measurement data is separated into three categories, line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight (NLOS) by buildings, with the help of video information , recorded during the measurements.
Revised:  The measurement data is separated into three categories, line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight due to buildings, with the help of video information , recorded during the measurements.
Edit intent: clarity
Original:  The measurement data is separated into three categories, line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight (NLOS) by buildings, with the help of video information , recorded during the measurements.
Revised:  The measurement data is separated into three categories, line-of-sight (LOS), obstructed line-of-sight (OLOS) by vehicles, and non line-of-sight (NLOS) by buildings, with the help of video information recorded during the measurements.
Edit intent: fluency
Original:  Finally, system level VANET simulation results are presented, which show the difference between the LOS/OLOS model and a channel model based on Nakagami-m fading.
Revised:  Finally, system level VANET simulation results are presented, showing the difference between the LOS/OLOS model and a channel model based on Nakagami-m fading.
Edit intent: coherence
Original: In complex systems, the interplay between nonlinear and stochastic dynamics  gives rise to an evolution process in Darwinian sense  with punctuated equilibrium,  random "mutations" and "adaptations".
Revised: In complex systems, the interplay between nonlinear and stochastic dynamics gives rise to an evolutionary process in Darwinian sense with punctuated equilibrium, random "mutations" and "adaptations".
Edit intent: fluency
Original:  Epigenetic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a framework .
Revised:  Phenotypic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a framework .
Edit intent: clarity
Original:  Epigenetic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a framework .
Revised:  Epigenetic states of a biological cell, a mesoscopic nonlinear stochastic open biochemical system, could be understood through such a perspective .
Edit intent: clarity
Original: In this letter, we propose a novel network traffic matrix decomposition method named as Stable Principal Component Pursuit with Frequency Domain Regularization (SPCP-FDR) . SPCP-FDR improves the Stable Principal Component Pursuit (SPCP) method by using a new noise regularization function defined in frequency domain.
Revised: We propose a novel network traffic matrix decomposition method named as Stable Principal Component Pursuit with Frequency Domain Regularization (SPCP-FDR) . SPCP-FDR improves the Stable Principal Component Pursuit (SPCP) method by using a new noise regularization function defined in frequency domain.
Edit intent: coherence
Original: In this letter, we propose a novel network traffic matrix decomposition method named as Stable Principal Component Pursuit with Frequency Domain Regularization (SPCP-FDR) . SPCP-FDR improves the Stable Principal Component Pursuit (SPCP) method by using a new noise regularization function defined in frequency domain.
Revised: In this letter, we propose a novel network traffic matrix decomposition method named Stable Principal Component Pursuit with Frequency Domain Regularization (SPCP-FDR) . SPCP-FDR improves the Stable Principal Component Pursuit (SPCP) method by using a new noise regularization function defined in frequency domain.
Edit intent: fluency
Original: In this letter, we propose a novel network traffic matrix decomposition method named as Stable Principal Component Pursuit with Frequency Domain Regularization (SPCP-FDR) . SPCP-FDR improves the Stable Principal Component Pursuit (SPCP) method by using a new noise regularization function defined in frequency domain.
Revised: In this letter, we propose a novel network traffic matrix decomposition method named as Stable Principal Component Pursuit with Frequency-Domain Regularization (SPCP-FDR) . SPCP-FDR improves the Stable Principal Component Pursuit (SPCP) method by using a new noise regularization function defined in frequency domain.
Edit intent: clarity
Original: In this letter, we propose a novel network traffic matrix decomposition method named as Stable Principal Component Pursuit with Frequency Domain Regularization (SPCP-FDR) . SPCP-FDR improves the Stable Principal Component Pursuit (SPCP) method by using a new noise regularization function defined in frequency domain.
Revised: In this letter, we propose a novel network traffic matrix decomposition method named as Stable Principal Component Pursuit with Frequency Domain Regularization (SPCP-FDR) , which improves the Stable Principal Component Pursuit (SPCP) method by using a new noise regularization function defined in frequency domain.
Edit intent: coherence
Original: In this letter, we propose a novel network traffic matrix decomposition method named as Stable Principal Component Pursuit with Frequency Domain Regularization (SPCP-FDR) . SPCP-FDR improves the Stable Principal Component Pursuit (SPCP) method by using a new noise regularization function defined in frequency domain.
Revised: In this letter, we propose a novel network traffic matrix decomposition method named as Stable Principal Component Pursuit with Frequency Domain Regularization (SPCP-FDR) . SPCP-FDR improves the Stable Principal Component Pursuit (SPCP) method by using a frequency-domain noise regularization function defined in frequency domain.
Edit intent: clarity
Original:  The technique is based on  iterative replacement of pairs of letters appearing in the equation by a `fresh' letter and local modification of variables (replacing X by aX or Xa).
Revised:  The technique is based on local modification of variables (replacing X by aX or Xa) and iterative replacement of pairs of letters appearing in the equation by a `fresh' letter and local modification of variables (replacing X by aX or Xa).
Edit intent: fluency
Original:  Using this technique we give a new, independent and completely self-contained proofs of most of the known results for  equations.
Revised:  Using this technique we give a new, independent and self-contained proofs of most of the known results for equations.
Edit intent: coherence
Original:  Both upper bounds were previously attained, though by separate and completely different algorithms.
Edit intent: coherence
Original:  Furthermore, a simple analysis of the algorithm yields a doubly exponential upper bound on the size of the length-minimal solution.
Revised:  Furthermore, a further analysis of the algorithm yields a doubly exponential upper bound on the size of the length-minimal solution.
Edit intent: clarity
Original:  The presented algorithm does not use exponential bound on the exponent of periodicity , only its weaker version which is concerned with blocks of one letter only (a relatively short and simple proof is supplied), conversely, the analysis of the algorithm yields an independent proof of the exponential bound on exponent of periodicity.
Revised:  The presented algorithm does not use exponential bound on the exponent of periodicity . Conversely, the analysis of the algorithm yields an independent proof of the exponential bound on exponent of periodicity.
Edit intent: coherence
Original:  The objective is to decide if there exists a ranking ? of the vertices satisfying all but at most k constraints.
Revised:  The objective is to decide if there exists a ranking of the vertices satisfying all but at most k constraints.
Edit intent: fluency
Original:  Finally, we consider another generalization of Feedback Arc Set in Tournaments, which does not fi?t the aforementioned framework.
Revised:  Finally, we consider another generalization of Feedback Arc Set in Tournaments, which does not fit the aforementioned framework.
Edit intent: fluency
Original:  We prove that so-called l_r-simply characterized ranking r-CSP admit linear vertex-kernels whenever they admit constant-factor approximation algorithms.
Revised:  We prove that so-called l_r-simply characterized ranking r-CSPs admit linear vertex-kernels whenever they admit constant-factor approximation algorithms.
Edit intent: fluency
Original:  Finally, we consider another generalization of Feedback Arc Set in Tournaments, which does not fit the aforementioned framework.
Revised:  Moreover, we introduce another generalization of Feedback Arc Set in Tournaments, which does not fit the aforementioned framework.
Edit intent: clarity
Original: The postive semidefinite rank of a nonnegative (m\times n)-matrix S is the minimum number q such that there exist real positive semidefinite (q\times q)-matrices A_1,...,A_m, B_1,...,B_n such that S(k,\ell) = \tr A_k^*B_\ell.
Revised: The postive semidefinite rank of a nonnegative (m\times n)-matrix S is the minimum number q such that there exist positive semidefinite (q\times q)-matrices A_1,...,A_m, B_1,...,B_n such that S(k,\ell) = \tr A_k^*B_\ell.
Edit intent: clarity
Original:  It characterizes the minimum "size " of "formulations " of  combinatorial optimization problems as Semidefinite Programming problems, and is closely related to the well-known nonnegative rank .
Revised:  Just as the nonnegative rank characterizes the minimum "size " of "formulations " of combinatorial optimization problems as Semidefinite Programming problems, and is closely related to the well-known nonnegative rank .
Edit intent: clarity
Original:  It characterizes the minimum "size " of "formulations " of  combinatorial optimization problems as Semidefinite Programming problems, and is closely related to the well-known nonnegative rank .
Revised:  It characterizes the minimum size of combinatorial optimization problems as Semidefinite Programming problems, and is closely related to the well-known nonnegative rank .
Edit intent: clarity
Original:  For nonnegative rank, the most important lower bound (the Boolean rank) is entirely based on the support of S, i.e., its zero/non-zero pattern .
Revised:  For nonnegative rank, the most important lower bound technique on nonnegative rank only uses the zero/non-zero pattern .
Edit intent: clarity
Original: The postive semidefinite rank of a nonnegative (m\times n)-matrix  S is the minimum number  q such that there exist positive semidefinite (q\times q)-matrices A_1, ... \dots ,A_m, B_1, ... \dots ,B_n such that S(k,\ell) = %DIFDELCMD < \tr %%%  A_k^* B_\ell . Just as the nonnegative rank characterizes the minimum size of formulations of combinatorial optimization problems as linear programs, the positive semidefinite rank characterizes their minimum size as positive semidefinite programs.
Revised: The positive semidefinite rank of a nonnegative (m\times n)-matrix S is the minimum number q such that there exist positive semidefinite (q\times q)-matrices A_1, ... \dots ,A_m, B_1, ... \dots ,B_n such that S(k,\ell) = %DIFDELCMD < \tr %%% A_k^* B_\ell . Just as the nonnegative rank characterizes the minimum size of formulations of combinatorial optimization problems as linear programs, the positive semidefinite rank characterizes their minimum size as positive semidefinite programs.
Edit intent: clarity
Original: The postive semidefinite rank of a nonnegative (m\times n)-matrix  S is the minimum number  q such that there exist positive semidefinite (q\times q)-matrices A_1, ... \dots ,A_m, B_1, ... \dots ,B_n such that S(k,\ell) = %DIFDELCMD < \tr %%%  A_k^* B_\ell . Just as the nonnegative rank characterizes the minimum size of formulations of combinatorial optimization problems as linear programs, the positive semidefinite rank characterizes their minimum size as positive semidefinite programs.
Revised: The postive semidefinite rank of a nonnegative (m\times n)-matrix ~ S is the minimum number q such that there exist positive semidefinite (q\times q)-matrices A_1, ... \dots ,A_m, B_1, ... \dots ,B_n such that S(k,\ell) = %DIFDELCMD < \tr %%% A_k^* B_\ell . Just as the nonnegative rank characterizes the minimum size of formulations of combinatorial optimization problems as linear programs, the positive semidefinite rank characterizes their minimum size as positive semidefinite programs.
Edit intent: fluency
Original: The postive semidefinite rank of a nonnegative (m\times n)-matrix  S is the minimum number  q such that there exist positive semidefinite (q\times q)-matrices A_1, ... \dots ,A_m, B_1, ... \dots ,B_n such that S(k,\ell) = %DIFDELCMD < \tr %%%  A_k^* B_\ell . Just as the nonnegative rank characterizes the minimum size of formulations of combinatorial optimization problems as linear programs, the positive semidefinite rank characterizes their minimum size as positive semidefinite programs.
Revised: The postive semidefinite rank of a nonnegative (m\times n)-matrix S is the minimum number ~ q such that there exist positive semidefinite (q\times q)-matrices A_1, ... \dots ,A_m, B_1, ... \dots ,B_n such that S(k,\ell) = %DIFDELCMD < \tr %%% A_k^* B_\ell . Just as the nonnegative rank characterizes the minimum size of formulations of combinatorial optimization problems as linear programs, the positive semidefinite rank characterizes their minimum size as positive semidefinite programs.
Edit intent: fluency
Original: The postive semidefinite rank of a nonnegative (m\times n)-matrix  S is the minimum number  q such that there exist positive semidefinite (q\times q)-matrices A_1, ... \dots ,A_m, B_1, ... \dots ,B_n such that S(k,\ell) = %DIFDELCMD < \tr %%%  A_k^* B_\ell . Just as the nonnegative rank characterizes the minimum size of formulations of combinatorial optimization problems as linear programs, the positive semidefinite rank characterizes their minimum size as positive semidefinite programs.
Revised: The postive semidefinite rank of a nonnegative (m\times n)-matrix S is the minimum number q such that there exist positive semidefinite (q\times q)-matrices A_1, \dots ,A_m, B_1, ... \dots ,B_n such that S(k,\ell) = %DIFDELCMD < \tr %%% A_k^* B_\ell . Just as the nonnegative rank characterizes the minimum size of formulations of combinatorial optimization problems as linear programs, the positive semidefinite rank characterizes their minimum size as positive semidefinite programs.
Edit intent: fluency
Original: The postive semidefinite rank of a nonnegative (m\times n)-matrix  S is the minimum number  q such that there exist positive semidefinite (q\times q)-matrices A_1, ... \dots ,A_m, B_1, ... \dots ,B_n such that S(k,\ell) = %DIFDELCMD < \tr %%%  A_k^* B_\ell . Just as the nonnegative rank characterizes the minimum size of formulations of combinatorial optimization problems as linear programs, the positive semidefinite rank characterizes their minimum size as positive semidefinite programs.
Revised: The postive semidefinite rank of a nonnegative (m\times n)-matrix S is the minimum number q such that there exist positive semidefinite (q\times q)-matrices A_1, ... \dots ,A_m, B_1, \dots ,B_n such that S(k,\ell) = %DIFDELCMD < \tr %%% A_k^* B_\ell . Just as the nonnegative rank characterizes the minimum size of formulations of combinatorial optimization problems as linear programs, the positive semidefinite rank characterizes their minimum size as positive semidefinite programs.
Edit intent: fluency
Original:  The most important  lower bound technique on nonnegative rank only uses the zero/non-zero pattern of the matrix.
Revised:  The most important , lower bound technique on nonnegative rank only uses the zero/non-zero pattern of the matrix.
Edit intent: fluency
Original:  Such an experimental approach is sorely missing for NP-hard control problems, where control refers to attempts to tamper with the outcome of elections by adding/ delet-ing /partitioning either voters or candidates.
Revised:  Such an experimental approach is sorely missing for NP-hard control problems, where control refers to attempts to tamper with the outcome of elections by adding/ deleting /partitioning either voters or candidates.
Edit intent: fluency
Original:  We experimentally tackle NP-hard control problems for Bucklin and fallback voting , which among natural voting systems with efficient winner determination are the systems currently known to display the broadest resistance to control in terms of NP-hardness  [ER10, EPR11  ].
Revised:  We experimentally tackle NP-hard control problems for Bucklin and fallback voting . Among natural voting systems with efficient winner determination are the systems currently known to display the broadest resistance to control in terms of NP-hardness [ER10, EPR11 ].
Edit intent: coherence
Original:  We experimentally tackle NP-hard control problems for Bucklin and fallback voting , which among natural voting systems with efficient winner determination are the systems currently known to display the broadest resistance to control in terms of NP-hardness  [ER10, EPR11  ].
Revised:  We experimentally tackle NP-hard control problems for Bucklin and fallback voting , which among natural voting systems with efficient winner determination , fallback voting is currently known to display the broadest resistance to control in terms of NP-hardness [ER10, EPR11 ].
Edit intent: clarity
Original:  In this paper, we present an approach to predict the total CPU utilization in terms of CPU clock tick of applications when running on MapReduce framework.
Revised:  In this paper, we present an approach to provision the total CPU utilization in terms of CPU clock tick of applications when running on MapReduce framework.
Edit intent: clarity
Original:  The outcomes of the measurements  follow a distribution which is predicted by quantum mechanics .
Revised:  The outcomes of the measurements follow a distribution predicted by quantum mechanics .
Edit intent: clarity
Original:  We also give a lower bound on quantum communication complexity which can be viewed as a quantum extension of the rectangle bound, effectively overcoming the necessity of a quantum minmax theorem.
Edit intent: coherence
Original:  To this end, we present and analyze a new set of sparse and low-rank recovery algorithms  within the class of hard thresholding methods.
Revised:  To this end, we present and analyze a new set of low-rank recovery algorithms within the class of hard thresholding methods.
Edit intent: clarity
Original:  We provide strategies on how to set up these algorithms via basic "ingredients " for different configurations to achieve complexity vs. accuracy tradeoffs.
Revised:  We provide strategies on how to set up these algorithms via basic ingredients for different configurations to achieve complexity vs. accuracy tradeoffs.
Edit intent: fluency
Original:  Moreover, we propose acceleration schemes by utilizing memory-based techniques and randomized, \epsilon-approximate , low-rank projections to speed-up the convergence as well as decrease the computational costs in the recovery process.
Revised:  Moreover, we study acceleration schemes via memory-based techniques and randomized, \epsilon-approximate , low-rank projections to speed-up the convergence as well as decrease the computational costs in the recovery process.
Edit intent: clarity