wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Many efforts have been made to implement stiff chemistry solvers on GPUs but have not been highly successful because of the logical divergence in traditional stiff algorithms like CVODE or LSODE. This study will demonstrate a machine learned hybrid algorithm implemented in TensorFlow for stiff problems and the speed gains relative to the traditional LSODE solver used in the Multiphase Flow with Interphase eXchanges (MFiX) Computational Fluid Dynamics (CFD) code. The results will show a dramatic decrease in total simulation time while maintaining the same degree of accuracy.
Revised:  Many efforts have been made to implement stiff chemistry solvers on GPUs but have not been highly successful because of the logical divergence in traditional stiff solver algorithms. Because of these constrains, a novel Explicit Stabilized Variable-load (STEV) solver has been developed. Overstepping due to the relatively large time steps is prevented by introducing limits to the maximum changes of chemical species per time step. To prevent oscillations, a discrete Fourier transform is introduced to dampen ringing. In contrast to conventional explicit approaches, a variable-load approach is used where each cell in the computational domain is advanced with its unique time step. This approach allows cells to be integrated simultaneously while maintaining warp convergence but finish at different iterations and be removed from the workload. To improve the computational performance of the introduced solver, specific thermodynamic quantities of interest were estimated using shallow neural networks in place of polynomial fits, leading to an additional 10\% savings in clock time with minimal training and implementation requirements. However ML specific hardware could increase the time savings to as much as 28\%. While the complexity of these particular machine learning models is not high by modern standards, the impact on computational efficiency should not be ignored. The results show a dramatic decrease in total simulation time while maintaining the same degree of accuracy.
Edit intent: meaning-changed
Original:  Both the two methods derive from the GPR-MC method and they mainly differ in the method used to approximate the continuation value: a single step of binomial tree or integration according to the probability density of the process.
Revised:  Both the two methods derive from the GPR-MC method and they mainly differ in the approach used to compute the continuation value: a single step of binomial tree or integration according to the probability density of the process.
Edit intent: clarity
Original:  Numerical results show that these two methods are accurate and reliable and improve the results of the GPR-MC method in handling American options on very large baskets of assets  .
Revised:  Numerical results show that these two methods are accurate and reliable and improve the results of the GPR-MC method in handling American options on very large baskets of assets . Moreover we also consider the rough Bergomi model, which provides stochastic volatility with memory. Despite this model is only bidimensional, the whole history of the process impacts on the price, and handling all this information is not obvious at all. To this aim, we present how to adapt the GPR-Tree and GPR-EI methods and we focus on pricing American options in this non-Markovian framework .
Edit intent: meaning-changed
Original: In this study, we perform a detailed analysis of the 2015 financial bubble in the Chinese stock market by calibrating the Log Periodic Power Law Singularity (LPPLS) model to two important Chinese stock indices, SSEC and SZSC, from early 2014 to June 2015.
Revised: In this study, we perform a novel analysis of the 2015 financial bubble in the Chinese stock market by calibrating the Log Periodic Power Law Singularity (LPPLS) model to two important Chinese stock indices, SSEC and SZSC, from early 2014 to June 2015.
Edit intent: clarity
Original:  The back tests of  2015 Chinese stock market bubbles indicates that the LPPLS model can readily detect the bubble behavior of the faster-than-exponential increase corrected by the accelerating logarithm-periodic oscillations in the 2015 Chinese Stock market.
Revised:  The back tests of the 2015 Chinese stock market bubbles indicates that the LPPLS model can readily detect the bubble behavior of the faster-than-exponential increase corrected by the accelerating logarithm-periodic oscillations in the 2015 Chinese Stock market.
Edit intent: fluency
Original:  Compared to the traditional optimization method used in  LPPLS model,  the covariance matrix adaptation evolution strategy (CMA-ES) may have a significantly lower computation cost . The CMA-ES is recommended as an alternative algorithm in the LPPLS model . Moreover, the exponent m does not show a remarkable feature of change when the start day t1 is fixed while the end day t2 is moved towards the actual critical time t_c in the expanding windows. In the LPPLS fitting with expanding windows, the gap (tc -t2) shows a significant decrease when the end day t2 approaches the actual bubble crash time.
Revised:  Compared to the traditional optimization method used in LPPLS model, the covariance matrix adaptation evolution strategy (CMA-ES) may have a significantly lower computation cost , and thus recommend this as a better alternative algorithm for LPPLS model fit. Furthermore, in the LPPLS fitting with expanding windows, the gap (tc -t2) shows a significant decrease when the end day t2 approaches the actual bubble crash time.
Edit intent: coherence
Original: This paper aims to present an advance bubble detection methodology based on LPPLS confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock market using the daily data on the Shanghai Shenzhen CSI 300 stock market index from January 2002 through April 2018.
Revised: We present an advance bubble detection methodology based on LPPLS confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock market using the daily data on the Shanghai Shenzhen CSI 300 stock market index from January 2002 through April 2018.
Edit intent: clarity
Original: This paper aims to present an advance bubble detection methodology based on LPPLS confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock market using the daily data on the Shanghai Shenzhen CSI 300 stock market index from January 2002 through April 2018.
Revised: This paper aims to present an advance bubble detection methodology based on the Log Periodic Power Law Singularity (LPPLS) confidence indicator for the early causal identification of positive and negative bubbles in the Chinese stock market using the daily data on the Shanghai Shenzhen CSI 300 stock market index from January 2002 through April 2018.
Edit intent: meaning-changed
Original:  We account for the damping condition of LPPLS model in the search space and implement the stricter filter conditions for the qualification of the valid LPPLS fits by taking account of the maximum relative error,  Lomb log-periodic test of the detrended residual, and unit-root tests of the logarithmic residual based on both the Phillips-Perron test and Dickey-Fuller test to improve the performance of LPPLS confidence indicator.
Revised:  We account for the damping condition of LPPLS model in the search space and implement the stricter filter conditions for the qualification of the valid LPPLS fits by taking account of the maximum relative error, performing the Lomb log-periodic test of the detrended residual, and unit-root tests of the logarithmic residual based on both the Phillips-Perron test and Dickey-Fuller test to improve the performance of LPPLS confidence indicator.
Edit intent: clarity
Original:  We find that the probability density distribution of the estimated beginning time of bubbles appears to be skewed and the mass of the distribution is concentrated on the area where the bubbles start to have a super-exponentially growth.
Revised:  We find that the probability density distribution of the estimated beginning time of bubbles appears to be skewed and the mass of the distribution is concentrated on the area where the price starts to have an obvious super-exponentially growth.
Edit intent: clarity
Original:  This study presents that it is possible to detect the potential positive and negative bubbles and crashes ahead of time, which provides a prerequisite for limiting the bubble sizes and eventually minimizing the damage from the bubble crash.
Revised:  This study is the first work in the literature that identifies the existence of bubbles in the Chinese stock market using the daily data of CSI 300 index with the advance bubble detection methodology of LPPLS confidence indicator. We have shown that it is possible to detect the potential positive and negative bubbles and crashes ahead of time, which provides a prerequisite for limiting the bubble sizes and eventually minimizing the damage from the bubble crash.
Edit intent: meaning-changed
Original:  The new architecture, which we call MultiPrior, was designed to be a fully-trainable, three-dimensional convolutional network. Thus, the resulting architecture represents a neural network with learnable spatial memories. When trained on a set of stroke patients and healthy subjects, MultiPrior outperforms the state-of-the-art segmentation tools such as DeepMedic and SPM segmentation.
Revised:  We train and test these networks on images of 43 stroke patients and healthy subjects, MultiPrior outperforms the state-of-the-art segmentation tools such as DeepMedic and SPM segmentation.
Edit intent: clarity
Original:  When trained on a set of stroke patients and healthy subjects, MultiPrior outperforms the state-of-the-art segmentation tools such as DeepMedic and SPM segmentation. The approach is further demonstrated on patients with disorders of consciousness , where we find that cognitive state correlates positively with gray-matter volumes and negatively with the extent of ventricles .
Revised:  When trained on a set of stroke patients and 4 healthy individuals which have been manually segmented. The analysis demonstrates the benefits of leveraging the three sources of prior information. We also provide an out-of-sample validation and clinical application of the approach on an additional 47 patients with disorders of consciousness , where we find that cognitive state correlates positively with gray-matter volumes and negatively with the extent of ventricles .
Edit intent: meaning-changed
Original:  Here we add to a three-dimensional convolutional network  spatial priors with a TPM, morphological priors with conditional random fields, and  context with a wider field-of-view at lower resolution.
Revised:  Approach: We add three sources of prior information to a three-dimensional convolutional network spatial priors with a TPM, morphological priors with conditional random fields, and context with a wider field-of-view at lower resolution.
Edit intent: clarity
Original:  Here we add to a three-dimensional convolutional network  spatial priors with a TPM, morphological priors with conditional random fields, and  context with a wider field-of-view at lower resolution.
Revised:  Here we add to a three-dimensional convolutional network , namely, spatial priors with a TPM, morphological priors with conditional random fields, and context with a wider field-of-view at lower resolution.
Edit intent: clarity
Original:  Here we add to a three-dimensional convolutional network  spatial priors with a TPM, morphological priors with conditional random fields, and  context with a wider field-of-view at lower resolution.
Revised:  Here we add to a three-dimensional convolutional network spatial priors with a TPM, morphological priors with conditional random fields, and spatial context with a wider field-of-view at lower resolution.
Edit intent: fluency
Original:  The analysis demonstrates the benefits of leveraging the three sources of prior information .
Revised:  The analysis demonstrates the benefits of leveraging the three sources of prior information , and we show that the new architecture, which we call Multiprior network, improves the performance of existing segmentation software, such as SPM, FSL, and DeepMedic for abnormal anatomies. The relevance of the different priors was compared and the TPM was found to be most beneficial. The benefit of adding a TPM is generic in that it can boost the performance of established segmentation networks such as the DeepMedic and a UNet.
Edit intent: meaning-changed
Original:  We make the code and trained networks freely available  .
Revised:  We make the code and trained networks freely available . Conclusions: Biomedical images follow imaging protocols that can be leveraged as prior information into deep convolutional neural networks to improve performance. The network segmentations match human manual corrections performed in 3D, and are comparable in performance to human segmentations obtained from scratch in 2D for abnormal brain anatomies .
Edit intent: meaning-changed
Original:  To address this , we introduce a method for TL mediated by human brains to improve the performance of TL especially on pattern recognition in which human high-level cognition is considered .
Revised:  To examine if the internal representation of the brain could be used to achieve more efficient TL , we introduce a method for TL mediated by human brains to improve the performance of TL especially on pattern recognition in which human high-level cognition is considered .
Edit intent: meaning-changed
Original:  If uniform events, each of which is followed by the same period of absolute refractoriness, occur randomly, as in the Bernoulli scheme, then the event probability as a function of time exhibits damped oscillations .
Revised:  If uniform events, each of which is followed by the same period of absolute refractoriness, occur randomly, as in the Bernoulli scheme, then the event probability as a function of time can exhibit damped transient oscillations caused by a specific initial condition .
Edit intent: meaning-changed
Original:  To model an ever-changing environment, we develop a framework of evolutionary dynamics with stochastic game transitions, where individuals' behaviors together with the games they play in one time step decide the games to be played next time step.
Revised:  To capture an ever-changing environment, we develop a framework of evolutionary dynamics with stochastic game transitions, where individuals' behaviors together with the games they play in one time step decide the games to be played next time step.
Edit intent: clarity
Original:  To model an ever-changing environment, we develop a framework of evolutionary dynamics with stochastic game transitions, where individuals' behaviors together with the games they play in one time step decide the games to be played next time step.
Revised:  To model an ever-changing environment, we consider a model of evolutionary dynamics with stochastic game transitions, where individuals' behaviors together with the games they play in one time step decide the games to be played next time step.
Edit intent: clarity
Original:  To model an ever-changing environment, we develop a framework of evolutionary dynamics with stochastic game transitions, where individuals' behaviors together with the games they play in one time step decide the games to be played next time step.
Revised:  To model an ever-changing environment, we develop a framework of evolutionary dynamics with game transitions, where individuals' behaviors together with the games they play in one time step decide the games to be played next time step.
Edit intent: clarity
Original:  To model an ever-changing environment, we develop a framework of evolutionary dynamics with stochastic game transitions, where individuals' behaviors together with the games they play in one time step decide the games to be played next time step.
Revised:  To model an ever-changing environment, we develop a framework of evolutionary dynamics with stochastic game transitions, where individuals' behaviors together with the games they play in one time step influence the games to be played next time step.
Edit intent: clarity
Original:  Within this framework , we study the evolution of cooperation in structured populations and find a simple rule: natural selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, b, to the corresponding cost, c, exceeds k-k', which means b/c>k-k', where k is the average number of neighbors  and k' captures the effects from game transitions.
Revised:  Within this model , we study the evolution of cooperation in structured populations and find a simple rule: natural selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, b, to the corresponding cost, c, exceeds k-k', which means b/c>k-k', where k is the average number of neighbors and k' captures the effects from game transitions.
Edit intent: clarity
Original:  Within this framework , we study the evolution of cooperation in structured populations and find a simple rule: natural selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, b, to the corresponding cost, c, exceeds k-k', which means b/c>k-k', where k is the average number of neighbors  and k' captures the effects from game transitions.
Revised:  Within this framework , we study the evolution of cooperation in structured populations and find a simple rule: weak selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, b, to the corresponding cost, c, exceeds k-k', which means b/c>k-k', where k is the average number of neighbors and k' captures the effects from game transitions.
Edit intent: meaning-changed
Original:  Within this framework , we study the evolution of cooperation in structured populations and find a simple rule: natural selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, b, to the corresponding cost, c, exceeds k-k', which means b/c>k-k', where k is the average number of neighbors  and k' captures the effects from game transitions.
Revised:  Within this framework , we study the evolution of cooperation in structured populations and find a simple rule: natural selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, b, to the corresponding cost, c, exceeds k-k', where k is the average number of neighbors and k' captures the effects from game transitions.
Edit intent: coherence
Original:  Within this framework , we study the evolution of cooperation in structured populations and find a simple rule: natural selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, b, to the corresponding cost, c, exceeds k-k', which means b/c>k-k', where k is the average number of neighbors  and k' captures the effects from game transitions.
Revised:  Within this framework , we study the evolution of cooperation in structured populations and find a simple rule: natural selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, b, to the corresponding cost, c, exceeds k-k', which means b/c>k-k', where k is the average number of neighbors of an individual and k' captures the effects from game transitions.
Edit intent: clarity
Original:  Within this framework , we study the evolution of cooperation in structured populations and find a simple rule: natural selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, b, to the corresponding cost, c, exceeds k-k', which means b/c>k-k', where k is the average number of neighbors  and k' captures the effects from game transitions.
Revised:  Within this framework , we study the evolution of cooperation in structured populations and find a simple rule: natural selection favors cooperation over defection if the ratio of the benefit provided by an altruistic behavior, b, to the corresponding cost, c, exceeds k-k', which means b/c>k-k', where k is the average number of neighbors and k' captures the effects of the game transitions.
Edit intent: fluency
Original:  We show our methods have high-order convergence rates for the metric and other geometric quantities of the manifold , for the truncation errors of exterior calculus operators , and for the solution errors of the Stokes problem for hydrodynamic flows on curved surfaces.
Revised:  Using a Hodge decomposition, we develop methods for manifolds having spherical topology. We show the methods exhibit high-order convergence rates for the metric and other geometric quantities of the manifold , for the truncation errors of exterior calculus operators , and for the solution errors of the Stokes problem for hydrodynamic flows on curved surfaces.
Edit intent: meaning-changed
Original:  We show our methods have high-order convergence rates for the metric and other geometric quantities of the manifold , for the truncation errors of exterior calculus operators , and for the solution errors of the Stokes problem for hydrodynamic flows on curved surfaces.
Revised:  We show our methods have high-order convergence rates for solving hydrodynamic flows on curved surfaces. The methods also provide general high-order approximations for the metric, curvature, and other geometric quantities of the manifold , for the truncation errors of exterior calculus operators , and for the solution errors of the Stokes problem for hydrodynamic flows on curved surfaces.
Edit intent: meaning-changed
Original:  We show our methods have high-order convergence rates for the metric and other geometric quantities of the manifold , for the truncation errors of exterior calculus operators , and for the solution errors of the Stokes problem for hydrodynamic flows on curved surfaces.
Revised:  We show our methods have high-order convergence rates for the metric and other geometric quantities of the manifold and associated exterior calculus operators , and for the solution errors of the Stokes problem for hydrodynamic flows on curved surfaces.
Edit intent: clarity
Original:  We show our methods have high-order convergence rates for the metric and other geometric quantities of the manifold , for the truncation errors of exterior calculus operators , and for the solution errors of the Stokes problem for hydrodynamic flows on curved surfaces. Our approaches also may be utilized to develop high-order solvers for other scalar-valued and vector-valued problems on manifolds.
Revised:  We show our methods have high-order convergence rates for the metric and other geometric quantities of the manifold , for the truncation errors of exterior calculus operators . The approaches also can be utilized to develop high-order solvers for other scalar-valued and vector-valued problems on manifolds.
Edit intent: clarity
Original:  In this paper we aim to show that the concept of ensemble of trajectories can be applied to a variety of stochastic models of gene expression and hence , apart from asymptotic behavior of remote tails of probability distributions of the dynamical observables, generating functions for the cumulants of these observables can easily be obtained.
Revised:  In this paper we aim to show that the concept of ensemble of trajectories can be applied to a variety of stochastic models of gene expression ranging from a simple birth-death process to a more sophisticate model containing burst and switch. By considering the protein numbers as a relevant dynamical observable , apart from asymptotic behavior of remote tails of probability distributions of the dynamical observables, generating functions for the cumulants of these observables can easily be obtained.
Edit intent: meaning-changed