wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We consider robotic planning using Markov decision processes (MDPs) and develop an algorithm to automatically generate a personalized explanation of an optimal robotic plan (i.e., an optimal MDP policy) based on the user preference regarding four elements (i.e., objective, locality, specificity, and abstraction ).
Revised:  We consider robotic planning using Markov decision processes (MDPs) and develop an algorithm to automatically generate a personalized explanation of an optimal robotic plan (i.e., an optimal MDP policy) based on the user preference regarding four elements (i.e., objective, locality, specificity, and corpus ).
Edit intent: clarity
Original:  [Quant. Finance, 2018, 18(10), 1753-1765] provide a small-time asymptotics for the mass at zero under the uncorrelated SABR model by approximating the integrated variance with a moment-matched lognormal distribution.
Revised:  [Quant. Finance, 2018, 18(10), 1753-1765] provide a small-time asymptotics for the mass at zero under the uncorrelated stochastic-alpha-beta-rho (SABR) model by approximating the integrated variance with a moment-matched lognormal distribution.
Edit intent: meaning-changed
Original:  We further obtain the option price by integrating the CEV option prices in the same manner without resorting to the small-strike volatility smile asymptotics of De Marco et al.
Revised:  We further obtain the option price by integrating the constant elasticity of variance (CEV) option prices in the same manner without resorting to the small-strike volatility smile asymptotics of De Marco et al.
Edit intent: meaning-changed
Original:  Applications to a bottom-up mean-variance-skewness-kurtosis or CRRA (Constant Relative Risk Aversion) optimization with short-sale portfolio constraints in both simulation and real market (China A-shares and U.S. equity markets) environments are studied and shown to perform very well .
Revised:  Applications to equity portfolio management in U.S. equity markets) environments are studied and shown to perform very well .
Edit intent: clarity
Original:  Applications to a bottom-up mean-variance-skewness-kurtosis or CRRA (Constant Relative Risk Aversion) optimization with short-sale portfolio constraints in both simulation and real market (China A-shares and U.S. equity markets) environments are studied and shown to perform very well .
Revised:  Applications to a bottom-up mean-variance-skewness-kurtosis or CRRA (Constant Relative Risk Aversion) optimization with short-sale portfolio constraints in both simulation and real market (China A-shares and U.S. and China equity markets are studied and shown to perform very well .
Edit intent: clarity
Original:  Applications to a bottom-up mean-variance-skewness-kurtosis or CRRA (Constant Relative Risk Aversion) optimization with short-sale portfolio constraints in both simulation and real market (China A-shares and U.S. equity markets) environments are studied and shown to perform very well .
Revised:  Applications to a bottom-up mean-variance-skewness-kurtosis or CRRA (Constant Relative Risk Aversion) optimization with short-sale portfolio constraints in both simulation and real market (China A-shares and U.S. equity markets) environments are studied and we document significant excess returns to the selected benchmarks .
Edit intent: meaning-changed
Original: The turnpike problem of recovering a set of points in \mathbb{R in R^d from intensity-only measurements in dimension d \geq 2. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly } related to the phase retrieval problem of recovering a signal from magnitude-only measurements of its Fourier transform.
Revised: We consider the problem of recovering a set of points in \mathbb{R in R^d from intensity-only measurements in dimension d \geq 2. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly } related to the phase retrieval problem of recovering a signal from magnitude-only measurements of its Fourier transform.
Edit intent: clarity
Original: The turnpike problem of recovering a set of points in \mathbb{R in R^d from intensity-only measurements in dimension d \geq 2. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly } related to the phase retrieval problem of recovering a signal from magnitude-only measurements of its Fourier transform.
Revised: The turnpikephase retrieval problem of recovering a set of points in \mathbb{R in R^d from intensity-only measurements in dimension d \geq 2. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly } related to the phase retrieval problem of recovering a signal from magnitude-only measurements of its Fourier transform.
Edit intent: fluency
Original: The turnpike problem of recovering a set of points in \mathbb{R in R^d from intensity-only measurements in dimension d \geq 2. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly } related to the phase retrieval problem of recovering a signal from magnitude-only measurements of its Fourier transform.
Revised: The turnpike problem of recovering a sparse signal \mathbf{x in R^d from intensity-only measurements in dimension d \geq 2. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly } related to the phase retrieval problem of recovering a signal from magnitude-only measurements of its Fourier transform.
Edit intent: meaning-changed
Original: The turnpike problem of recovering a set of points in \mathbb{R in R^d from intensity-only measurements in dimension d \geq 2. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly } related to the phase retrieval problem of recovering a signal from magnitude-only measurements of its Fourier transform.
Revised: The turnpike problem of recovering a set of points in \mathbb{R in R^d from intensity-only measurements in dimension d \geq 2. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly } related to the combinatorial problem of recovering a signal from magnitude-only measurements of its Fourier transform.
Edit intent: clarity
Original: The turnpike problem of recovering a set of points in \mathbb{R in R^d from intensity-only measurements in dimension d \geq 2. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly } related to the phase retrieval problem of recovering a signal from magnitude-only measurements of its Fourier transform. We propose the Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in  less time.
Revised: The turnpike problem of recovering a set of points in \mathbb{R in R^d from intensity-only measurements in dimension d \geq 2. Phase retrieval can be equivalently formulated as the problem of recovering a signal from its autocorrelation, which is in turn directly } related to the phase retrieval problem of recovering a set from its pairwise differences. In one spatial dimension, this problem is well studied and known as the Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in less time.
Edit intent: meaning-changed
Original:  We propose the Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in  less time.
Revised:  We propose theturnpike problem Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in less time.
Edit intent: meaning-changed
Original:  We propose the Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in  less time.
Revised:  We propose the. In this work, we present MISTR ( Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in less time.
Edit intent: meaning-changed
Original:  We propose the Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in  less time.
Revised:  We propose the Multidimensional Intersection Sparse supporT Recovery), an algorithm which exploits this formulation to recover the support of a multidimensional signal from magnitude-only measurements. MISTR takes advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in less time.
Edit intent: meaning-changed
Original:  We propose the Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in  less time.
Revised:  We propose the Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions to provably achieve the same accuracy as the best one-dimensional algorithms in less time.
Edit intent: clarity
Original:  We propose the Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in  less time.
Revised:  We propose the Multidimensional Intersection Sparse Phase Retrieval (MISTR)algorithm for solving the turnpike problem in dimension D \geq 2. By taking advantage of the structure of multiple dimensions , we are able to achieve the same accuracy as the best one-dimensional algorithms in dramatically less time.
Edit intent: clarity
Original:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process  as long as sparsity is at most O \left( N ^{ D(}\right)  1/2 - \delta)}\left( \right) .
Revised:  We prove theoretically that MISTR correctly recovers the support of signals distributed as a Gaussian point process as long as sparsity is at most O \left( N ^{ D(}\right) 1/2 - \delta)}\left( \right) .
Edit intent: meaning-changed
Original:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process  as long as sparsity is at most O \left( N ^{ D(}\right)  1/2 - \delta)}\left( \right) .
Revised:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process with high probability as long as sparsity is at most O \left( N ^{ D(}\right) 1/2 - \delta)}\left( \right) .
Edit intent: meaning-changed
Original:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process  as long as sparsity is at most O \left( N ^{ D(}\right)  1/2 - \delta)}\left( \right) .
Revised:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process as long as sparsity is at most \mathcal{O \left( N ^{ D(}\right) 1/2 - \delta)}\left( \right) .
Edit intent: meaning-changed
Original:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process  as long as sparsity is at most O \left( N ^{ D(}\right)  1/2 - \delta)}\left( \right) .
Revised:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process as long as sparsity is at most O \left( n ^{ D(}\right) 1/2 - \delta)}\left( \right) .
Edit intent: fluency
Original:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process  as long as sparsity is at most O \left( N ^{ D(}\right)  1/2 - \delta)}\left( \right) .
Revised:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process as long as sparsity is at most O \left( N ^{ d\theta}\right) 1/2 - \delta)}\left( \right) .
Edit intent: meaning-changed
Original:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process  as long as sparsity is at most O \left( N ^{ D(}\right)  1/2 - \delta)}\left( \right) .
Revised:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process as long as sparsity is at most O \left( N ^{ D(}\right) for any \theta < 1/2 - \delta)}\left( \right) .
Edit intent: meaning-changed
Original:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process  as long as sparsity is at most O \left( N ^{ D(}\right)  1/2 - \delta)}\left( \right) .
Revised:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process as long as sparsity is at most O \left( N ^{ D(}\right) 1/2 , where n^d represents pixel size in a fixed image window. In the case that magnitude measurements are corrupted by noise, we provide a thresholding scheme with theoretical guarantees for sparsity at most \mathcal{O}\left( \right) .
Edit intent: meaning-changed
Original:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process  as long as sparsity is at most O \left( N ^{ D(}\right)  1/2 - \delta)}\left( \right) .
Revised:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process as long as sparsity is at most O \left( N ^{ D(}\right) 1/2 - \delta)}\left(n^{d\theta \right) .
Edit intent: meaning-changed
Original:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process  as long as sparsity is at most O \left( N ^{ D(}\right)  1/2 - \delta)}\left( \right) .
Revised:  We prove theoretically that MISTR correctly recovers most signals distributed as a Gaussian point process as long as sparsity is at most O \left( N ^{ D(}\right) 1/2 - \delta)}\left( \right) for \theta < 1/4 that obviates the need for MISTR to explicitly handle noisy autocorrelation data.
Edit intent: meaning-changed
Original:  Detailed and reproducible numerical experiments demonstrate the effectiveness of our algorithm, showing that in practice MISTR enjoys O%DIFDELCMD < \left(%%% k^2\log{k}%DIFDELCMD < \right) %%% average-case time complexity , nearly linear in the size of the input .
Revised:  Detailed and reproducible numerical experiments demonstrate the effectiveness of our algorithm, showing that in practice MISTR enjoys %DIFDELCMD < \left(%%% k^2\log{k}%DIFDELCMD < \right) %%% average-case time complexity , nearly linear in the size of the input .
Edit intent: fluency
Original:  Detailed and reproducible numerical experiments demonstrate the effectiveness of our algorithm, showing that in practice MISTR enjoys O%DIFDELCMD < \left(%%% k^2\log{k}%DIFDELCMD < \right) %%% average-case time complexity , nearly linear in the size of the input .
Revised:  Detailed and reproducible numerical experiments demonstrate the effectiveness of our algorithm, showing that in practice MISTR enjoys O%DIFDELCMD < \left(%%% }%DIFDELCMD < \right) %%% average-case time complexity , nearly linear in the size of the input .
Edit intent: meaning-changed
Original:  Detailed and reproducible numerical experiments demonstrate the effectiveness of our algorithm, showing that in practice MISTR enjoys O%DIFDELCMD < \left(%%% k^2\log{k}%DIFDELCMD < \right) %%% average-case time complexity , nearly linear in the size of the input .
Revised:  Detailed and reproducible numerical experiments demonstrate the effectiveness of our algorithm, showing that in practice MISTR enjoys O%DIFDELCMD < \left(%%% k^2\log{k}%DIFDELCMD < \right) %%% time complexity which is nearly linear in the size of the input .
Edit intent: clarity
Original:  Detailed and reproducible numerical experiments demonstrate the effectiveness of our algorithm, showing that in practice MISTR enjoys O%DIFDELCMD < \left(%%% k^2\log{k}%DIFDELCMD < \right) %%% average-case time complexity , nearly linear in the size of the input . This represents a substantial improvement over existing one-dimensional algorithms .
Revised:  Detailed and reproducible numerical experiments demonstrate the effectiveness of our algorithm, showing that in practice MISTR enjoys O%DIFDELCMD < \left(%%% k^2\log{k}%DIFDELCMD < \right) %%% average-case time complexity , nearly linear in the size of the input .
Edit intent: meaning-changed
Original:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation 3%DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya 4 . When these methods are combined , highly efficient integration of weakly-singular functions is realized.
Revised:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities 3%DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya 4 . When these methods are combined , highly efficient integration of weakly-singular functions is realized.
Edit intent: clarity
Original:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation 3%DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya 4 . When these methods are combined , highly efficient integration of weakly-singular functions is realized.
Revised:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation %DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya 4 . When these methods are combined , highly efficient integration of weakly-singular functions is realized.
Edit intent: coherence
Original:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation 3%DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya 4 . When these methods are combined , highly efficient integration of weakly-singular functions is realized.
Revised:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation 3%DIFDELCMD < ] %%% 4 . When these methods are combined , highly efficient integration of weakly-singular functions is realized.
Edit intent: coherence
Original:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation 3%DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya 4 . When these methods are combined , highly efficient integration of weakly-singular functions is realized.
Revised:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation 3%DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya . When these methods are combined , highly efficient integration of weakly-singular functions is realized.
Edit intent: coherence
Original:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation 3%DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya 4 . When these methods are combined , highly efficient integration of weakly-singular functions is realized.
Revised:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation 3%DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya 4 . When these methods are used , highly efficient integration of weakly-singular functions is realized.
Edit intent: clarity
Original:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation 3%DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya 4 . When these methods are combined , highly efficient integration of weakly-singular functions is realized.
Revised:  This paper also introduces several methods for smoothing integrands with point singularities and near-singularities based on the generalized Duffy transformation 3%DIFDELCMD < ] %%% and a distance transformation described in Ma and Kamiya 4 . When these methods are combined , highly efficient integration of weakly singular functions is realized.
Edit intent: fluency
Original: Large scale modeling and simulation problems, from nanoscale materials to universe-scale cosmology, have in the past used the massive computing resources of High-Performance Computing (HPC) systems. Over the last decade, cloud computing has gained popularity for business applications and increasingly for computationally intensive machine learning problems. Despite the prolific literature, the question remains open whether cloud computing can provide HPC-competitive performance for a wide range of scientific applications.
Revised: Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open whether cloud computing can provide HPC-competitive performance for a wide range of scientific applications.
Edit intent: clarity
Original:  Despite the prolific literature, the question remains open whether cloud computing can provide HPC-competitive performance for a wide range of scientific applications. The answer to this question is crucial in guiding the design of future systems and providing access to high-performance resources to a broadened community.
Revised:  Despite the prolific literature, the question remains open . Answers are crucial for designing future systems and providing access to high-performance resources to a broadened community.
Edit intent: clarity