wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this documentation, you will find the following . - A brief introduction to Conjure, - installation instructions, - a description of how to use Conjure through its command line user interface, - a list of Conjure's features, - a description of Conjure's input language Essence, and - a collection of simple demonstrations of Conjure's use.
Revised:  In this documentation, you will find the following . - A brief introduction to Conjure, installation instructions, - a description of how to use Conjure through its command line user interface, - a list of Conjure's features, - a description of Conjure's input language Essence, and - a collection of simple demonstrations of Conjure's use.
Edit intent: fluency
Original:  In this documentation, you will find the following . - A brief introduction to Conjure, - installation instructions, - a description of how to use Conjure through its command line user interface, - a list of Conjure's features, - a description of Conjure's input language Essence, and - a collection of simple demonstrations of Conjure's use.
Revised:  In this documentation, you will find the following . - A brief introduction to Conjure, - installation instructions, a description of how to use Conjure through its command line user interface, - a list of Conjure's features, - a description of Conjure's input language Essence, and - a collection of simple demonstrations of Conjure's use.
Edit intent: fluency
Original:  In this documentation, you will find the following . - A brief introduction to Conjure, - installation instructions, - a description of how to use Conjure through its command line user interface, - a list of Conjure's features, - a description of Conjure's input language Essence, and - a collection of simple demonstrations of Conjure's use.
Revised:  In this documentation, you will find the following . - A brief introduction to Conjure, - installation instructions, - a description of how to use Conjure through its command line user interface, a list of Conjure's features, - a description of Conjure's input language Essence, and - a collection of simple demonstrations of Conjure's use.
Edit intent: fluency
Original:  In this documentation, you will find the following . - A brief introduction to Conjure, - installation instructions, - a description of how to use Conjure through its command line user interface, - a list of Conjure's features, - a description of Conjure's input language Essence, and - a collection of simple demonstrations of Conjure's use.
Revised:  In this documentation, you will find the following . - A brief introduction to Conjure, - installation instructions, - a description of how to use Conjure through its command line user interface, - a list of Conjure's features, a description of Conjure's input language Essence, and - a collection of simple demonstrations of Conjure's use.
Edit intent: fluency
Original:  In this documentation, you will find the following . - A brief introduction to Conjure, - installation instructions, - a description of how to use Conjure through its command line user interface, - a list of Conjure's features, - a description of Conjure's input language Essence, and - a collection of simple demonstrations of Conjure's use.
Revised:  In this documentation, you will find the following . - A brief introduction to Conjure, - installation instructions, - a description of how to use Conjure through its command line user interface, - a list of Conjure's features, - a description of Conjure's input language Essence, and a collection of simple demonstrations of Conjure's use.
Edit intent: fluency
Original:  Here, using a simple mathematical model , we quantify the downstream impact of a contagion that mutates exactly once as it travels . Assuming that this mutation occurs at a random node in the contact network, we calculate the distribution of the number of "descendants," d, downstream from the initial "Patient Zero" mutant.
Revised:  Here, using a simple susceptible-infected (SI) model of contagion, we explore the downstream impact of a contagion that mutates exactly once as it travels . Assuming that this mutation occurs at a random node in the contact network, we calculate the distribution of the number of "descendants," d, downstream from the initial "Patient Zero" mutant.
Edit intent: meaning-changed
Original:  Here, using a simple mathematical model , we quantify the downstream impact of a contagion that mutates exactly once as it travels . Assuming that this mutation occurs at a random node in the contact network, we calculate the distribution of the number of "descendants," d, downstream from the initial "Patient Zero" mutant.
Revised:  Here, using a simple mathematical model , we quantify the downstream impact of a single mutation event . Assuming that this mutation occurs at a random node in the contact network, we calculate the distribution of the number of "descendants," d, downstream from the initial "Patient Zero" mutant.
Edit intent: clarity
Original:  For every discipline concerned with contagion and its prevention, we anticipate that contagion pathways  hold valuable lessons, given their role as the conduits through which single mutations, innovations, or failures can sweep through a network as a whole.
Revised:  We anticipate that contagion pathways hold valuable lessons, given their role as the conduits through which single mutations, innovations, or failures can sweep through a network as a whole.
Edit intent: clarity
Original:  For every discipline concerned with contagion and its prevention, we anticipate that contagion pathways  hold valuable lessons, given their role as the conduits through which single mutations, innovations, or failures can sweep through a network as a whole.
Revised:  For every discipline concerned with contagion and its prevention, we anticipate that contagion pathways will hold valuable lessons, given their role as the conduits through which single mutations, innovations, or failures can sweep through a network as a whole.
Edit intent: fluency
Original:  Then, we generalize our result to give a randomized \alpha-approximation algorithm for any \alpha\in [ 1, \sqrt{n, n / \log n} ], running in time  (n^2/\alpha^2  ).
Revised:  Then, we generalize our result to give a randomized \alpha-approximation algorithm for any \alpha\in [ \sqrt{\log n, n / \log n} ], running in time (n^2/\alpha^2 ).
Edit intent: meaning-changed
Original:  Then, we generalize our result to give a randomized \alpha-approximation algorithm for any \alpha\in [ 1, \sqrt{n, n / \log n} ], running in time  (n^2/\alpha^2  ).
Revised:  Then, we generalize our result to give a randomized \alpha-approximation algorithm for any \alpha\in [ 1, \sqrt{n, n / \log n} ], running in time O (n^2/\alpha^2 ).
Edit intent: meaning-changed
Original:  Then, we generalize our result to give a randomized \alpha-approximation algorithm for any \alpha\in [ 1, \sqrt{n, n / \log n} ], running in time  (n^2/\alpha^2  ).
Revised:  Then, we generalize our result to give a randomized \alpha-approximation algorithm for any \alpha\in [ 1, \sqrt{n, n / \log n} ], running in time (n^2/\alpha^2 \log n ).
Edit intent: meaning-changed
Original:  Both of them are non-stationary time series. This means that their statistical distribution change over time . Consequently a detrended process is made to neutralize the non-stationary effects. The detrended process is obtained by decomposing the financial time series into a deterministic trend and random fluctuations. We present an alternative method on detrending time series based on the classical moving average (MA) models, where Kurtosis is used to determine the windows size. Then, the dentrending fluctuation analysis (DFA) is use to show that the detrended part is stationary .
Revised:  Both of them are non-stationary effects. This is a linear Fokker-Planck equation (FPE) that describes the time evolution of the probability distribution function (PDF) of the price return. By applying Ito's lemma, this FPE is associated with a stochastic differential equation (SDE) that models the time evolution of the price return in a fashion different from the classical Black-Scholes equation. Both FPE and SDE equations account for a deterministic part or trend, and a stationary, stochastic part as a q-Gaussian noise. The model is validated using the S\&P500 index's data. After removing the trend from the index, we show that the detrended part is stationary .
Edit intent: meaning-changed
Original:  Then, the dentrending fluctuation analysis (DFA) is use to show that the detrended part is stationary . This is done by considering the autocorrelation of detrended price return and the power spectrum analysis of detrended price .
Revised:  Then, the dentrending fluctuation analysis (DFA) is use to show that the detrended part is stationary by evaluating the Hurst exponent of the multifractal time series, its power spectrum, and its autocorrelation .
Edit intent: meaning-changed
Original: We show that, except for a special family of polynomials -that we call \lambda -reducible-, a natural  generalization of Schwartz-Zippel-DeMillo-Lipton lemma holds.
Revised: We show that, except for a special family of polynomials -that we call \lambda -reducible-, a natural generalization of Schwartz-Zippel-DeMillo-Lipton lemma holds.
Edit intent: fluency
Original: We show that, except for a special family of polynomials -that we call \lambda -reducible-, a natural  generalization of Schwartz-Zippel-DeMillo-Lipton lemma holds.
Revised: We show that, except for a special family of polynomials -that we call \lambda -reducible-, a natural multivariate generalization of Schwartz-Zippel-DeMillo-Lipton lemma holds.
Edit intent: meaning-changed
Original: We show that^{\lambda_i} be finite sets, and let S:=S_1 \times S_2 \times \ldots \times S_m \subset C^n be the multi-grid defined by S_i. Suppose p is an n-variate degree d polynomial.
Revised: Motivated by applications in combinatorial geometry, we consider the following question: Let \lambda=(\lambda_1,\lambda_2,\ldots,\lambda_m) be an m-partition of a positive integer n, S_i \subseteq \mathbb{C^{\lambda_i} be finite sets, and let S:=S_1 \times S_2 \times \ldots \times S_m \subset C^n be the multi-grid defined by S_i. Suppose p is an n-variate degree d polynomial.
Edit intent: meaning-changed
Original:  Then we show that a natural multivariate generalization of the DeMillo-Lipton-Schwartz-Zippel lemma holds} , except for a special family of polynomials -that we call \lambda -reducible-, a natural multivariate generalization of Schwartz-Zippel-DeMillo-Lipton lemma holds.
Revised:  Then we show that a natural multivariate generalization of the DeMillo-Lipton-Schwartz-Zippel lemma holds} , except for a special family of polynomials that we call \lambda -reducible-, a natural multivariate generalization of Schwartz-Zippel-DeMillo-Lipton lemma holds.
Edit intent: fluency
Original:  Then we show that a natural multivariate generalization of the DeMillo-Lipton-Schwartz-Zippel lemma holds} , except for a special family of polynomials -that we call \lambda -reducible-, a natural multivariate generalization of Schwartz-Zippel-DeMillo-Lipton lemma holds. Moreover , we develop a symbolic algorithm to detect \lambda-reducibility.
Revised:  Then we show that a natural multivariate generalization of the DeMillo-Lipton-Schwartz-Zippel lemma holds} , except for a special family of polynomials -that we call \lambda -reducible. This yields a simultaneous generalization of Szemer\'edi-Trotter theorem and Schwartz-Zippel lemma into higher dimensions, and has applications in incidence geometry. Finally , we develop a symbolic algorithm to detect \lambda-reducibility.
Edit intent: meaning-changed
Original:  Moreover , we develop a symbolic algorithm to detect \lambda-reducibility. Our work is motivated by and has applications in combinatorial geometry. Along the way we also present a multivariate generalization of Combinatorial Nullstellensatz, which might be of independent interest .
Revised:  Moreover , we develop a symbolic algorithm that identifies certain \lambda-reducible polynomials. More precisely, our symbolic algorithm detects polynomials that include a cartesian product of hypersurfaces in their zero set. It is likely that using Chow forms the algorithm can be generalized to handle arbitrary \lambda-reducible polynomials, which we leave as an open problem .
Edit intent: meaning-changed
Original:  We discuss how the design choices have been driven by the system's goals: blockchain's goal is security , whereas the distributed database's goal is performance . We then conduct an extensive and in-depth performance study on two blockchains, namely Quorum and Hyperledger Fabric, and three distributed databases, namely CockroachDB, TiDB and etcd.
Revised:  We discuss how the design choices are driven by two goals: security for blockchains, and performance for distributed databases. To expose the impact of different design choices on the overall performance, we conduct an in-depth performance study on two blockchains, namely Quorum and Hyperledger Fabric, and three distributed databases, namely CockroachDB, TiDB and etcd.
Edit intent: clarity
Original:  We then conduct an extensive and in-depth performance study on two blockchains, namely Quorum and Hyperledger Fabric, and three distributed databases, namely CockroachDB, TiDB and etcd.
Revised:  We then conduct an extensive and in-depth performance analysis of two blockchains, namely Quorum and Hyperledger Fabric, and three distributed databases, namely CockroachDB, TiDB and etcd.
Edit intent: clarity
Original:  We then conduct an extensive and in-depth performance study on two blockchains, namely Quorum and Hyperledger Fabric, and three distributed databases, namely CockroachDB, TiDB and etcd.
Revised:  We then conduct an extensive and in-depth performance study on two blockchains, namely Quorum and Hyperledger Fabric, and two distributed databases, namely CockroachDB, TiDB and etcd.
Edit intent: clarity
Original:  We then conduct an extensive and in-depth performance study on two blockchains, namely Quorum and Hyperledger Fabric, and three distributed databases, namely CockroachDB, TiDB and etcd.
Revised:  We then conduct an extensive and in-depth performance study on two blockchains, namely Quorum and Hyperledger Fabric, and three distributed databases, namely TiDB, and etcd.
Edit intent: coherence
Original:  We demonstrate how the different design choices in the four dimensions lead to different performance. In addition, we show that for most workloads, blockchain's performance is still lagging far behind that of a distributed database. However, the gap is not as significant as previously reported, and under high contention or constrained workloads, blockchains and databases are even comparable. Our work provides a framework for exploring the design space of hybrid database-blockchain systems .
Revised:  Lastly, we propose a framework for back-of-the-envelope performance forecast of blockchain-database hybrids .
Edit intent: clarity
Original:  Our main findings are that imposing a factor structure yields point identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these systems .
Revised:  Our main findings are that imposing a factor structure yields point identification of parameters of interest, such as the coefficient associated with the endogenous regressor in the outcome equation, under weaker assumptions than usually required in these models .
Edit intent: clarity
Original:  In particular, we show that an exclusion restriction , requiring an explanatory variable in the outcome equation to be excluded from the treatment equation , is no longer necessary for identification . Under such settings, we propose a rank estimator for both the factor loading and the causal effect parameter that are root-n consistent and asymptotically normal. The estimator's finite sample properties are evaluated through a simulation study.
Revised:  In particular, we show that an exclusion restriction , requiring an explanatory variable in the outcome equation to be excluded from the treatment equation , is no longer necessary for identification , even in cases where all of the regressors from the outcome equation are discrete.
Edit intent: meaning-changed
Original:  Finally, using quantile regressions, we assess the significance of the contemporaneous and lagged explanatory power of sentiment on returns finding strong statistical evidence when extreme returns, especially negative ones, are considered  .
Revised:  Finally, using quantile regressions, we assess the significance of the contemporaneous and lagged explanatory power of sentiment on returns finding strong statistical evidence when extreme returns, especially negative ones, are considered . Finally, the lagged relation is exploited in a portfolio allocation exercise .
Edit intent: meaning-changed
Original: The mathematical theory of neural networks is ``meant to tell us what is possible and, sometimes equally importantly, what is not." This paper contributes a case that fits into this principle. We propose that "option price or time value " is a natural hyperparameter in the design of neural network option models. Hutchinson, Lo and Poggio asked the question that if learning networks can learn the Black-Scholes formula, and they studied the network (S_t/K, \tau)%DIFDELCMD < \to %%% C_t/K where S_t, K, \tau, C_t are the underlying price, strike, time to maturity and option price.
Revised: %DIFDELCMD < \to %%% C_t/K where S_t, K, \tau, C_t are the underlying price, strike, time to maturity and option price.
Edit intent: meaning-changed
Original:  Hutchinson, Lo and Poggio asked the question that if learning networks can learn the Black-Scholes formula, and they studied the network (S_t/K, \tau)%DIFDELCMD < \to %%% C_t/K where S_t, K, \tau, C_t are the underlying price, strike, time to maturity and option price. In this paper we propose a novel decision function and study the network (S_t/K, \tau)%DIFDELCMD < \to %%% V_t/K where V_t is the time value.
Revised:  Hutchinson, Lo and Poggio asked the question that if learning networks can learn the Black-Scholes formula, and they studied the network (S_t/K, \tau)%DIFDELCMD < \to %%% %DIFDELCMD < \to %%% V_t/K where V_t is the time value.
Edit intent: coherence
Original:  In this paper we propose a novel decision function and study the network (S_t/K, \tau)%DIFDELCMD < \to %%% V_t/K where V_t is the time value. Empirical experiments will be carried out to demonstrate that this new decision function significantly improves  Hutchinson-Lo-Poggio 's model by faster learning and better generalization performance.
Revised:  In this paper we propose a novel decision function and study the network (S_t/K, \tau)%DIFDELCMD < \to %%% We propose a time value related decision function to treat a classical option pricing problem raised by Hutchinson-Lo-Poggio. In numerical experiments, the new decision function significantly improves Hutchinson-Lo-Poggio 's model by faster learning and better generalization performance.
Edit intent: meaning-changed
Original:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% 0, 1 ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% 0, 1 . This justifies the benefit of time value oriented decision functions in option pricing models .
Revised:  (R \times }%DIFDELCMD < [%%% 0, 1 ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% 0, 1 . This justifies the benefit of time value oriented decision functions in option pricing models .
Edit intent: coherence
Original:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% 0, 1 ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% 0, 1 . This justifies the benefit of time value oriented decision functions in option pricing models .
Revised:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% 0, 1 . This justifies the benefit of time value oriented decision functions in option pricing models .
Edit intent: fluency
Original:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% 0, 1 ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% 0, 1 . This justifies the benefit of time value oriented decision functions in option pricing models .
Revised:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% 0, 1 By proving a novel universal approximation theorem, we show that our decision function rather than Hutchinson-Lo-Poggio's can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% 0, 1 . This justifies the benefit of time value oriented decision functions in option pricing models .
Edit intent: meaning-changed
Original:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% 0, 1 ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% 0, 1 . This justifies the benefit of time value oriented decision functions in option pricing models .
Revised:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% 0, 1 ). As a corollary V_t/K but not C_t/K can be approximated ^+ \times }%DIFDELCMD < [%%% 0, 1 . This justifies the benefit of time value oriented decision functions in option pricing models .
Edit intent: clarity
Original:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% 0, 1 ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% 0, 1 . This justifies the benefit of time value oriented decision functions in option pricing models .
Revised:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% 0, 1 ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% . This justifies the benefit of time value oriented decision functions in option pricing models .
Edit intent: fluency
Original:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% 0, 1 ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% 0, 1 . This justifies the benefit of time value oriented decision functions in option pricing models .
Revised:  We prove that a shallow neural network with the logistic activation is a universal approximator in L^{2(R \times }%DIFDELCMD < [%%% 0, 1 ). As a corollary V_t/K but not C_t/K can be approximated by superpositions of logistic functions on \mathbb{R^+ \times }%DIFDELCMD < [%%% 0, 1 on the entire domain of definition by neural networks. Thus the experimental results are partially explained by the representation properties of networks .
Edit intent: meaning-changed