wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In particular, we propose an explanation for the current credit crisis, as a fundamental, systemic failure in the system of rules governing the behaviour of the reserve based banking system, which have resulted in the effective removal of central bank control over money and loan supply expansion. This situation has arisen from the presence of two 'bugs' in the underlying regulations controlling the banking system: securitized loans, which allow the sale of loans issued against fractional reserve controlled deposits to entities outside of the commercial banking system; and allowing debt instruments , in particular securitized loans to be part of bank equity capital reserves which are used for the purpose of regulating the total quantity of credit created by the banking system.
Revised:  In particular, we propose an explanation for the current credit crisis, as a fundamental, systemic failure in the system of rules governing the behaviour of the reserve based banking system, and in particular its internal controls on the supply of credit. We suggest that the current credit crisis is the result of multiple failures in the Basel regulatory framework, including the removal of central bank reserve requirements from some classes of deposit accounts within the banking system, allowing financial instruments representing debt to be used as regulatory capital, and in particular the introduction of securitized lending which effectively removed a previously implicit control over the total quantity of credit created by the banking system.
Edit intent: clarity
Original:  We  argue that the interaction of these two problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending  from the commercial bank sector has increased at a faster rate than the accompanying money supply growth supporting it .
Revised:  We further argue that the interaction of these two problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending from the commercial bank sector has increased at a faster rate than the accompanying money supply growth supporting it .
Edit intent: clarity
Original:  We  argue that the interaction of these two problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending  from the commercial bank sector has increased at a faster rate than the accompanying money supply growth supporting it .
Revised:  We argue that the interaction of these problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending from the commercial bank sector has increased at a faster rate than the accompanying money supply growth supporting it .
Edit intent: clarity
Original:  We  argue that the interaction of these two problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending  from the commercial bank sector has increased at a faster rate than the accompanying money supply growth supporting it .
Revised:  We argue that the interaction of these two problems has led to a destabilising imbalance between total money and loan supply growth, in that total lending from the commercial bank sector has increased at a faster rate than the accompanying money supply growth supporting it .
Edit intent: clarity
Original:  We  argue that the interaction of these two problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending  from the commercial bank sector has increased at a faster rate than the accompanying money supply growth supporting it .
Revised:  We argue that the interaction of these two problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending sourced from the commercial bank sector has increased at a faster rate than the accompanying money supply growth supporting it .
Edit intent: clarity
Original:  We  argue that the interaction of these two problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending  from the commercial bank sector has increased at a faster rate than the accompanying money supply growth supporting it .
Revised:  We argue that the interaction of these two problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending from the commercial bank sector increased at a faster rate than the accompanying money supply growth supporting it .
Edit intent: fluency
Original:  We  argue that the interaction of these two problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending  from the commercial bank sector has increased at a faster rate than the accompanying money supply growth supporting it .
Revised:  We argue that the interaction of these two problems has led to unregulated growth in both the money and loan supplies, and to a dangerously destabilising imbalance between total money and loan supply growth, in that total lending from the commercial bank sector has increased at a faster rate than accompanying growth in the money supply. This not only created a multi-decade macro-economic debt spiral, but by increasing the ratio of debt to money within the monetary system acted to increase the risk of loan defaults, and consequentially reduce the overall stability of the banking system .
Edit intent: meaning-changed
Original:  We first %DIFDELCMD < {\it %%% establish that the so-called mean field technique, which spins off a fixed point equation, is mathematically valid for use in performance analysis of 802.11.
Revised:  We first %DIFDELCMD < {\it %%% that the so-called mean field technique, which spins off a fixed point equation, is mathematically valid for use in performance analysis of 802.11.
Edit intent: clarity
Original:  We first %DIFDELCMD < {\it %%% establish that the so-called mean field technique, which spins off a fixed point equation, is mathematically valid for use in performance analysis of 802.11. On the basis of this , succinct equations describing the backoff distribution as a function of the collision probability \gamma are derived .
Revised:  We first %DIFDELCMD < {\it %%% establish make a simplistic Palm interpretation of the Bianchi's formula and on the basis of which , succinct equations describing the backoff distribution as a function of the collision probability \gamma are derived .
Edit intent: meaning-changed
Original:  On the basis of this , succinct equations describing the backoff distribution as a function of the collision probability \gamma are derived . In addition, the observation that the %DIFDELCMD < {\it %%% entropy of the backoff process in 802.11 increases with the number of nodes leads us to see through a Poissonian character inherent in 802.11.
Revised:  On the basis of this , succinct equations describing the backoff distribution as a function of the collision probability \gamma are derived , which also correct a possible misunderstanding in the field. The observation that the %DIFDELCMD < {\it %%% entropy of the backoff process in 802.11 increases with the number of nodes leads us to see through a Poissonian character inherent in 802.11.
Edit intent: meaning-changed
Original:  In addition, the observation that the %DIFDELCMD < {\it %%% entropy of the backoff process in 802.11 increases with the number of nodes leads us to see through a Poissonian character inherent in 802.11.
Revised:  In addition, the observation that the %DIFDELCMD < {\it %%% entropy of the backoff process in 802.11 increases with the number of nodes leads us to see through a Poissonian character inherent in 802.11.
Edit intent: fluency
Original:  However, it is also found that the %DIFDELCMD < {\it %%% collision effect between nodes prevails over the %DIFDELCMD < {\it %%% Poissonian aggregation effect in spite of its tendency to increase with the number of nodes.
Revised:  However, it is also found that the %DIFDELCMD < {\it %%% collision effect between nodes prevails over the %DIFDELCMD < {\it %%% Poissonian aggregation effect in spite of its tendency to increase with the number of nodes.
Edit intent: fluency
Original:  Another principle discovered upon regular variation theory is that the per-packet backoff has a truncated%DIFDELCMD < {\it %%%  Pareto-type tail distribution with an exponent of (\log \gamma)/\log m (m is the multiplicative factor).
Revised:  Another principle discovered upon regular variation theory is that the %DIFDELCMD < {\it %%% Pareto-type tail distribution with an exponent of (\log \gamma)/\log m (m is the multiplicative factor).
Edit intent: clarity
Original:  Another principle discovered upon regular variation theory is that the per-packet backoff has a truncated%DIFDELCMD < {\it %%%  Pareto-type tail distribution with an exponent of (\log \gamma)/\log m (m is the multiplicative factor).
Revised:  Another principle discovered upon regular variation theory is that the per-packet backoff has a truncated%DIFDELCMD < {\it %%% backoff times have a truncated Pareto-type tail distribution with an exponent of (\log \gamma)/\log m (m is the multiplicative factor).
Edit intent: meaning-changed
Original:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Revised:  Moreover, we identify %DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Edit intent: fluency
Original:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Revised:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 through both of mathematical and empirical wavelet-based analyses and answer a riddle: the absence of long range dependence in aggregate total load. We also show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Edit intent: meaning-changed
Original:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Revised:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) %DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Edit intent: clarity
Original:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Revised:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Edit intent: fluency
Original:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Revised:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning %DIFDELCMD < {\it %%% directional unfairness .
Edit intent: clarity
Original:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Revised:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% unfairness .
Edit intent: clarity
Original:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional unfairness .
Revised:  Moreover, we identify the%DIFDELCMD < {\it %%% long-range dependence in 802.11 and show that the inter-transmission probability undergoes a dramatic change at \gamma_0=1/m^2 and falls into two qualitatively distinct categories: either approximately Gaussian or %DIFDELCMD < {\it %%% L\'evy \alpha-stable distribution with \alpha \in (1,2) entailing infinite variances,%DIFDELCMD < {\it %%% leaning tendency, and%DIFDELCMD < {\it %%% directional .
Edit intent: clarity
Original:  Second, we show that the modularity function Q exhibits extreme degeneracies: that is, the modularity landscape admits an exponential number of distinct high-scoring solutions and does not typically exhibit a clear global maximum.
Revised:  Second, we show that the modularity function Q exhibits extreme degeneracies: that is, the modularity landscape admits an exponential number of distinct high-scoring solutions and typically lacks a clear global maximum.
Edit intent: clarity
Original:  Third, we derive the limiting behavior of the maximum modularity Q_max for  infinitely modular networks, showing that it depends strongly  on the size of the network and  the number of module-like subgraphs it contains.
Revised:  Third, we derive the limiting behavior of the maximum modularity Q_max for one model of infinitely modular networks, showing that it depends strongly on the size of the network and the number of module-like subgraphs it contains.
Edit intent: clarity
Original:  Third, we derive the limiting behavior of the maximum modularity Q_max for  infinitely modular networks, showing that it depends strongly  on the size of the network and  the number of module-like subgraphs it contains.
Revised:  Third, we derive the limiting behavior of the maximum modularity Q_max for infinitely modular networks, showing that it depends strongly both on the size of the network and the number of module-like subgraphs it contains.
Edit intent: clarity
Original:  Third, we derive the limiting behavior of the maximum modularity Q_max for  infinitely modular networks, showing that it depends strongly  on the size of the network and  the number of module-like subgraphs it contains.
Revised:  Third, we derive the limiting behavior of the maximum modularity Q_max for infinitely modular networks, showing that it depends strongly on the size of the network and on the number of module-like subgraphs it contains.
Edit intent: fluency
Original:  Third, we derive the limiting behavior of the maximum modularity Q_max for  infinitely modular networks, showing that it depends strongly  on the size of the network and  the number of module-like subgraphs it contains.
Revised:  Third, we derive the limiting behavior of the maximum modularity Q_max for infinitely modular networks, showing that it depends strongly on the size of the network and the number of modules it contains.
Edit intent: clarity
Original:  Finally, using three real-world examples of metabolic networks , we show that the degenerate solutions can fundamentally disagree on the composition of even the largest modules. Together, these results significantly extend and clarify our understanding of this popular method. In particular, they explain why so manyheuristics perform well in practice at finding high-scoring partitions, why these heuristics can disagree on the composition of the identified modules , and how the estimated value of Q_max should be interpreted.
Revised:  Finally, using three real-world examples of metabolic networks , we show that the degenerate solutions can fundamentally disagree on many, but not all, partition properties such as the composition of the identified modules , and how the estimated value of Q_max should be interpreted.
Edit intent: clarity
Original:  In particular, they explain why so manyheuristics perform well in practice at finding high-scoring partitions, why these heuristics can disagree on the composition of the identified modules , and how the estimated value of Q_max should be interpreted. Further, they imply that the output of any modularity maximization procedure should be interpreted cautiously in scientific contexts.
Revised:  In particular, they explain why so manyheuristics perform well in practice at finding high-scoring partitions, why these heuristics can disagree on the composition of the largest modules and the distribution of module sizes. These results imply that the output of any modularity maximization procedure should be interpreted cautiously in scientific contexts.
Edit intent: meaning-changed
Original:   We conclude by discussing avenues for mitigating  these behaviors, such as combining information from many degenerate solutions or using generative models.
Revised:  They also explain why many heuristics are often successful at finding high-scoring partitions in practice and why different heuristics can disagree on the modular structure of the same network. We conclude by discussing avenues for mitigating these behaviors, such as combining information from many degenerate solutions or using generative models.
Edit intent: meaning-changed
Original: We develop a theoretical trading conditioning model subject to price volatility and return  in terms of market psychological behavior, based on a volume-price probability wave distribution in which we use transaction volume probability to describe price volatility uncertainty and intensity.
Revised: We develop a theoretical trading conditioning model subject to price volatility and return information in terms of market psychological behavior, based on a volume-price probability wave distribution in which we use transaction volume probability to describe price volatility uncertainty and intensity.
Edit intent: meaning-changed
Original: We develop a theoretical trading conditioning model subject to price volatility and return  in terms of market psychological behavior, based on a volume-price probability wave distribution in which we use transaction volume probability to describe price volatility uncertainty and intensity.
Revised: We develop a theoretical trading conditioning model subject to price volatility and return in terms of market psychological behavior, based on analytical transaction volume-price probability wave distribution in which we use transaction volume probability to describe price volatility uncertainty and intensity.
Edit intent: meaning-changed
Original: We develop a theoretical trading conditioning model subject to price volatility and return  in terms of market psychological behavior, based on a volume-price probability wave distribution in which we use transaction volume probability to describe price volatility uncertainty and intensity.
Revised: We develop a theoretical trading conditioning model subject to price volatility and return in terms of market psychological behavior, based on a volume-price probability wave distributions in which we use transaction volume probability to describe price volatility uncertainty and intensity.
Edit intent: fluency
Original:  Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there are , in general, significant positive correlations between the rate of mean return and that of change in both trading conditioning intensity and the amount of transaction ;
Revised:  Applying the model to high frequent data test in China stock market, we have main findings as follows: 1) there is , in general, significant positive correlations between the rate of mean return and that of change in both trading conditioning intensity and the amount of transaction ;
Edit intent: fluency