wanyu/IteraTeR_full_sent huggingface dataset:
Original:  First, we use real trading records from  Shanghai Futures Exchange to construct futures trading networks, in which vertices are trading participants, and two vertices have a common edge if the two corresponding investors simultaneously appear in at least one trading record as a purchaser and a seller respectively.
Revised:  First, we use real trading records from Shanghai Futures Exchange to construct futures trading networks, in which vertices are trading participants, and two nodes have a common edge if the two corresponding investors simultaneously appear in at least one trading record as a purchaser and a seller respectively.
Edit intent: clarity
Original:  First, we use real trading records from  Shanghai Futures Exchange to construct futures trading networks, in which vertices are trading participants, and two vertices have a common edge if the two corresponding investors simultaneously appear in at least one trading record as a purchaser and a seller respectively.
Revised:  First, we use real trading records from Shanghai Futures Exchange to construct futures trading networks, in which vertices are trading participants, and two vertices have a common edge if the two corresponding investors appear simultaneously in at least one trading record as a purchaser and a seller respectively.
Edit intent: fluency
Original:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution,  and shrinkage of both  average path length and  diameter as network size increases.
Revised:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks . Empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution, and shrinkage of both average path length and diameter as network size increases.
Edit intent: coherence
Original:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution,  and shrinkage of both  average path length and  diameter as network size increases.
Revised:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit features such as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution, and shrinkage of both average path length and diameter as network size increases.
Edit intent: clarity
Original:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution,  and shrinkage of both  average path length and  diameter as network size increases.
Revised:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free behavior with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution, and shrinkage of both average path length and diameter as network size increases.
Edit intent: clarity
Original:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution,  and shrinkage of both  average path length and  diameter as network size increases.
Revised:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low-degree regions , small-world effect, URLanization, power-law betweenness distribution, and shrinkage of both average path length and diameter as network size increases.
Edit intent: fluency
Original:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution,  and shrinkage of both  average path length and  diameter as network size increases.
Revised:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution, disassortative mixing, and shrinkage of both average path length and diameter as network size increases.
Edit intent: clarity
Original:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution,  and shrinkage of both  average path length and  diameter as network size increases.
Revised:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution, and shrinkage of both the average path length and diameter as network size increases.
Edit intent: fluency
Original:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution,  and shrinkage of both  average path length and  diameter as network size increases.
Revised:  Then, we conduct a comprehensive statistical analysis on the constructed futures trading networks , and empirical results show that the futures trading networks exhibit such features as scale-free structure with interesting odd-even-degree divergence in low degree region , small-world effect, URLanization, power-law betweenness distribution, and shrinkage of both average path length and the diameter as network size increases.
Edit intent: fluency
Original:  We show that in the high dimensional case when the dimension p and the observation frequency n grow in the same rate, the limiting spectral distribution (LSD) of the RCV matrix depends on the covolatility process not only through the targeting ICV matrix, but also on how the covolatility process varies in time .
Revised:  We show that in the high dimensional case when the dimension p and the observation frequency n grow in the same rate, the limiting spectral distribution (LSD) of RCV depends on the covolatility process not only through the targeting ICV matrix, but also on how the covolatility process varies in time .
Edit intent: fluency
Original:  We show that in the high dimensional case when the dimension p and the observation frequency n grow in the same rate, the limiting spectral distribution (LSD) of the RCV matrix depends on the covolatility process not only through the targeting ICV matrix, but also on how the covolatility process varies in time .
Revised:  We show that in the high dimensional case when the dimension p and the observation frequency n grow in the same rate, the limiting spectral distribution (LSD) of the RCV matrix depends on the covolatility process not only through the targeting ICV .
Edit intent: coherence
Original:  We show that in the high dimensional case when the dimension p and the observation frequency n grow in the same rate, the limiting spectral distribution (LSD) of the RCV matrix depends on the covolatility process not only through the targeting ICV matrix, but also on how the covolatility process varies in time .
Revised:  We show that in the high dimensional case when the dimension p and the observation frequency n grow in the same rate, the limiting spectral distribution (LSD) of the RCV matrix depends on the covolatility process not only through the targeting ICV matrix, but also on how the covolatility process varies in time, .
Edit intent: fluency
Original:  We show that in the high dimensional case when the dimension p and the observation frequency n grow in the same rate, the limiting spectral distribution (LSD) of the RCV matrix depends on the covolatility process not only through the targeting ICV matrix, but also on how the covolatility process varies in time .
Revised:  We show that in the high dimensional case when the dimension p and the observation frequency n grow in the same rate, the limiting spectral distribution (LSD) of the RCV matrix depends on the covolatility process not only through the targeting ICV matrix, but also on how the covolatility process varies in timebut also on how the covolatility process varies in time .
Edit intent: fluency
Original:  We establish a Marcenko-Pastur type theorem for weighted sample covariance matrices, based on which we further establish a Marcenko-Pastur type theorem for RCV matrices for a class \mathcal{C \sC of diffusion processes.
Revised:  We establish a Marcenko-Pastur type theorem for weighted sample covariance matrices, based on which we obtain a Marcenko-Pastur type theorem for RCV matrices for a class \mathcal{C \sC of diffusion processes.
Edit intent: clarity
Original:  We establish a Marcenko-Pastur type theorem for weighted sample covariance matrices, based on which we further establish a Marcenko-Pastur type theorem for RCV matrices for a class \mathcal{C \sC of diffusion processes.
Revised:  We establish a Marcenko-Pastur type theorem for weighted sample covariance matrices, based on which we further establish a Marcenko-Pastur type theorem for RCV for a class \mathcal{C \sC of diffusion processes.
Edit intent: fluency
Original:  We establish a Marcenko-Pastur type theorem for weighted sample covariance matrices, based on which we further establish a Marcenko-Pastur type theorem for RCV matrices for a class \mathcal{C \sC of diffusion processes.
Revised:  We establish a Marcenko-Pastur type theorem for weighted sample covariance matrices, based on which we further establish a Marcenko-Pastur type theorem for RCV matrices for a class \sC of diffusion processes.
Edit intent: meaning-changed
Original:  The results explicitly demonstrate how the time-variability of the covolatility process affects the LSD of RCV matrix. We then propose an alternative estimator, the {\it time-variation adjusted realized covariance} (TVARCV) matrix.
Revised:  The results explicitly demonstrate how the time-variability of the covolatility process affects the LSD of RCV . We further propose an alternative estimator, the {\it time-variation adjusted realized covariance} (TVARCV) matrix.
Edit intent: clarity
Original:  We show that for diffusion processes in class \mathcal{C \sC , the TVARCV matrix possesses the desirable property that its LSD depends solely on that of the targeting ICV matrix through a Mar\v{c}enko-Pastur equation  .
Revised:  We show that for processes in class \mathcal{C \sC , the TVARCV matrix possesses the desirable property that its LSD depends solely on that of the targeting ICV matrix through a Mar\v{c}enko-Pastur equation .
Edit intent: clarity
Original:  We show that for diffusion processes in class \mathcal{C \sC , the TVARCV matrix possesses the desirable property that its LSD depends solely on that of the targeting ICV matrix through a Mar\v{c}enko-Pastur equation  .
Revised:  We show that for diffusion processes in class \sC , the TVARCV matrix possesses the desirable property that its LSD depends solely on that of the targeting ICV matrix through a Mar\v{c}enko-Pastur equation .
Edit intent: meaning-changed
Original:  We show that for diffusion processes in class \mathcal{C \sC , the TVARCV matrix possesses the desirable property that its LSD depends solely on that of the targeting ICV matrix through a Mar\v{c}enko-Pastur equation  .
Revised:  We show that for diffusion processes in class \mathcal{C \sC , the TVARCV possesses the desirable property that its LSD depends solely on that of the targeting ICV matrix through a Mar\v{c}enko-Pastur equation .
Edit intent: clarity
Original:  We show that for diffusion processes in class \mathcal{C \sC , the TVARCV matrix possesses the desirable property that its LSD depends solely on that of the targeting ICV matrix through a Mar\v{c}enko-Pastur equation  .
Revised:  We show that for diffusion processes in class \mathcal{C \sC , the TVARCV matrix possesses the desirable property that its LSD depends solely on that of the targeting ICV through the Mar\v{c}enko-Pastur equation .
Edit intent: fluency
Original:  We show that for diffusion processes in class \mathcal{C \sC , the TVARCV matrix possesses the desirable property that its LSD depends solely on that of the targeting ICV matrix through a Mar\v{c}enko-Pastur equation  .
Revised:  We show that for diffusion processes in class \mathcal{C \sC , the TVARCV matrix possesses the desirable property that its LSD depends solely on that of the targeting ICV matrix through a Mar\v{c}enko-Pastur equation , and hence, in particular, the TVARCV can be used to recover the empirical spectral distribution of the ICV by using existing algorithms .
Edit intent: meaning-changed
Original:  We extend the notion of a discounted moment generation function of the log stock price to the case where the underlying can default and show how to calculate it in terms of a coupled system of generalized Riccati equations.
Revised:  We extend the notion of a discounted moment generation function of the log stock price to the case where the underlying can default and show how to calculate discounted complex moments by solving a coupled system of generalized Riccati equations.
Edit intent: meaning-changed
Original:  This yields an efficient method to compute prices of power payoffs and Fourier transforms . European calls and puts as well as binaries and asset-or-nothing options can then be priced with the fast Fourier transform methods of Carr and Madan (1999) and Lee (2005).
Revised:  This yields an efficient method to compute prices of power payoffs . European calls and puts as well as binaries and asset-or-nothing options can then be priced with the fast Fourier transform methods of Carr and Madan (1999) and Lee (2005).
Edit intent: clarity
Original:  This yields an efficient method to compute prices of power payoffs and Fourier transforms . European calls and puts as well as binaries and asset-or-nothing options can then be priced with the fast Fourier transform methods of Carr and Madan (1999) and Lee (2005).
Revised:  This yields an efficient method to compute prices of power payoffs and Fourier transforms . European calls and puts as well as binaries and asset-or-nothing options can be priced with the fast Fourier transform methods of Carr and Madan (1999) and Lee (2005).
Edit intent: coherence
Original:  Spectra of random directed networks, where entries are completely un-correlated (\tau=0) show circular distribution with delocalized eigenvectors, where non-random networks with correlated entries have localized eigenvectors.
Revised:  Spectra of random networks, with completely uncorrelated entries show a circular distribution with delocalized eigenvectors, where non-random networks with correlated entries have localized eigenvectors.
Edit intent: clarity
Original:  Spectra of random directed networks, where entries are completely un-correlated (\tau=0) show circular distribution with delocalized eigenvectors, where non-random networks with correlated entries have localized eigenvectors.
Revised:  Spectra of random directed networks, where entries are completely un-correlated (\tau=0) show circular distribution with delocalized eigenvectors, where as networks with correlated entries have localized eigenvectors.
Edit intent: clarity
Original:  In order to understand the origin of localization we track the spectra as a function of connection probability and directionality both. The kind of inhibitory and excitatory connections we are considering, low connection probability leads to localized eigenstates near boundary of the circular region, whereas large connection probabilities give rise to isolated delocalized eigenstates.
Revised:  In order to understand the origin of localization we track the spectra as a function of connection probability and directionality .
Edit intent: coherence
Original:  Spectra of networks having modular structure show distinguishable different features than the random networks. For a very well distinguished community structure (rewiring probability p_r \sim 0) , the whole spectra is localized except some of eigenstates at boundary .
Revised:  Moreover, for a very well distinguished community structure (rewiring probability p_r \sim 0) , the whole spectra is localized except some of eigenstates at boundary .
Edit intent: coherence
Original:  For a very well distinguished community structure (rewiring probability p_r \sim 0) , the whole spectra is localized except some of eigenstates at boundary . As p_r is increased and network deviates from  community structure there is a sudden change in the localization property for  very small value of deformation from the perfect community structure.
Revised:  For a very well distinguished community structure (rewiring probability p_r \sim 0) , the whole spectra is localized except some of eigenstates at boundary of the circular distribution. As the network deviates from community structure there is a sudden change in the localization property for very small value of deformation from the perfect community structure.
Edit intent: clarity
Original: We study the asymptotic behaviour of the difference between the Value at Risks VaR(L) and VaR(L+S) for heavy tailed random variables L and S as an application to the sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II . Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor.
Revised: We study the asymptotic behavior of the difference between the Value at Risks VaR(L) and VaR(L+S) for heavy tailed random variables L and S as an application to the sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II . Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor.
Edit intent: fluency
Original: We study the asymptotic behaviour of the difference between the Value at Risks VaR(L) and VaR(L+S) for heavy tailed random variables L and S as an application to the sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II . Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor.
Revised: We study the asymptotic behaviour of the difference between the values at risk VaR(L) and VaR(L+S) for heavy tailed random variables L and S as an application to the sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II . Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor.
Edit intent: clarity
Original: We study the asymptotic behaviour of the difference between the Value at Risks VaR(L) and VaR(L+S) for heavy tailed random variables L and S as an application to the sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II . Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor.
Revised: We study the asymptotic behaviour of the difference between the Value at Risks VaR(L) and VaR(L+S) for heavy tailed random variables L and S for application in sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II . Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor.
Edit intent: clarity
Original: We study the asymptotic behaviour of the difference between the Value at Risks VaR(L) and VaR(L+S) for heavy tailed random variables L and S as an application to the sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II . Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor.
Revised: We study the asymptotic behaviour of the difference between the Value at Risks VaR(L) and VaR(L+S) for heavy tailed random variables L and S as an application to the sensitivity analysis of quantitative operational risk management within the framework of an advanced measurement approach (AMA) of Basel II . Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor.
Edit intent: fluency
Original: We study the asymptotic behaviour of the difference between the Value at Risks VaR(L) and VaR(L+S) for heavy tailed random variables L and S as an application to the sensitivity analysis of quantitative operational risk management in the framework of an advanced measurement approach (AMA) of Basel II . Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor.
Revised: We study the asymptotic behaviour of the difference between the Value at Risks VaR(L) and VaR(L+S) for heavy tailed random variables L and S as an application to the sensitivity analysis of quantitative operational risk management in the framework of the advanced measurement approach (AMA) of Basel II . Here the variable L describes the loss amount of the present risk profile and S means the loss amount caused by an additional loss factor.
Edit intent: fluency