wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To determine boundaries between the semantic components, CSD searches for critical points where the object cross-section substantially changes . CSD then cuts the object at critical points and assigns the same label to those object parts which are along the same sub-skeleton, defining a semantic tubular component.
Revised:  To find the intersection of the tubular components, CSD searches for critical points where the object cross-section substantially changes . CSD then cuts the object at critical points and assigns the same label to those object parts which are along the same sub-skeleton, defining a semantic tubular component.
Edit intent: clarity
Original:  To determine boundaries between the semantic components, CSD searches for critical points where the object cross-section substantially changes . CSD then cuts the object at critical points and assigns the same label to those object parts which are along the same sub-skeleton, defining a semantic tubular component.
Revised:  To determine boundaries between the semantic components, CSD translationally sweeps the object in decomposition intervals to identify critical points at which the shape of the object changes substantially. CSD cuts the object at critical points and assigns the same label to those object parts which are along the same sub-skeleton, defining a semantic tubular component.
Edit intent: meaning-changed
Original:  To determine boundaries between the semantic components, CSD searches for critical points where the object cross-section substantially changes . CSD then cuts the object at critical points and assigns the same label to those object parts which are along the same sub-skeleton, defining a semantic tubular component.
Revised:  To determine boundaries between the semantic components, CSD searches for critical points where the object cross-section substantially changes . CSD then cuts the object at critical points and assigns the same label to parts along the same sub-skeleton, defining a semantic tubular component.
Edit intent: clarity
Original:  To determine boundaries between the semantic components, CSD searches for critical points where the object cross-section substantially changes . CSD then cuts the object at critical points and assigns the same label to those object parts which are along the same sub-skeleton, defining a semantic tubular component.
Revised:  To determine boundaries between the semantic components, CSD searches for critical points where the object cross-section substantially changes . CSD then cuts the object at critical points and assigns the same label to those object parts which are along the same sub-skeleton, thereby constructing a semantic component.
Edit intent: clarity
Original: We revisit the scaling analysis and commonly accepted conditions for the validity of the standard, reverse and total quasi-steady-state approximations (sQSSA, rQSSA and tQSSA) through the lens of dimensional Tikhonov--Fenichel small parameters and their respective critical manifolds.
Revised: In this work, we revisit the scaling analysis and commonly accepted conditions for the validity of the standard, reverse and total quasi-steady-state approximations (sQSSA, rQSSA and tQSSA) through the lens of dimensional Tikhonov--Fenichel small parameters and their respective critical manifolds.
Edit intent: clarity
Original: We revisit the scaling analysis and commonly accepted conditions for the validity of the standard, reverse and total quasi-steady-state approximations (sQSSA, rQSSA and tQSSA) through the lens of dimensional Tikhonov--Fenichel small parameters and their respective critical manifolds.
Revised: We revisit the scaling analysis and commonly accepted conditions for the validity of the standard, reverse and total quasi-steady-state approximations through the lens of dimensional Tikhonov--Fenichel small parameters and their respective critical manifolds.
Edit intent: clarity
Original: We revisit the scaling analysis and commonly accepted conditions for the validity of the standard, reverse and total quasi-steady-state approximations (sQSSA, rQSSA and tQSSA) through the lens of dimensional Tikhonov--Fenichel small parameters and their respective critical manifolds.
Revised: We revisit the scaling analysis and commonly accepted conditions for the validity of the standard, reverse and total quasi-steady-state approximations (sQSSA, rQSSA and tQSSA) through the lens of dimensional Tikhonov-Fenichel parameters and their respective critical manifolds.
Edit intent: clarity
Original:  By combining Tikhonov--Fenichel small parameter with scaling analysis  , we derive improved upper bounds on the  error for the sQSSA, rQSSA and tQSSA.
Revised:  By combining Tikhonov-Fenichel parameters with scaling analysis , we derive improved upper bounds on the error for the sQSSA, rQSSA and tQSSA.
Edit intent: meaning-changed
Original:  By combining Tikhonov--Fenichel small parameter with scaling analysis  , we derive improved upper bounds on the  error for the sQSSA, rQSSA and tQSSA.
Revised:  By combining Tikhonov--Fenichel small parameter with scaling analysis and energy methods , we derive improved upper bounds on the error for the sQSSA, rQSSA and tQSSA.
Edit intent: meaning-changed
Original:  By combining Tikhonov--Fenichel small parameter with scaling analysis  , we derive improved upper bounds on the  error for the sQSSA, rQSSA and tQSSA.
Revised:  By combining Tikhonov--Fenichel small parameter with scaling analysis , we derive improved upper bounds on the approximation error for the sQSSA, rQSSA and tQSSA.
Edit intent: meaning-changed
Original:  By combining Tikhonov--Fenichel small parameter with scaling analysis  , we derive improved upper bounds on the  error for the sQSSA, rQSSA and tQSSA. We find that the condition for the validity of the rQSSA is far less restrictive than was previously assumed, and we derive a new small parameter that determines the validity of this approximation.
Revised:  By combining Tikhonov--Fenichel small parameter with scaling analysis , we derive improved upper bounds on the error for the standard, reverse and total quasi-steady-state approximations. Furthermore, previous analyses suggest that the reverse quasi-steady-state approximation is only valid when initial enzyme concentrations greatly exceed initial substrate concentrations. However, our results indicate that this approximation can be valid when initial enzyme and substrate concentrations are of equal magnitude. Using energy methods, we find that the condition for the validity of the rQSSA is far less restrictive than was previously assumed, and we derive a new small parameter that determines the validity of this approximation.
Edit intent: meaning-changed
Original:  Moreover, we show for the first time that the critical manifold of the rQSSA contains a singular point where normal hyperbolicity is lost. Associated with this singularity is a transcritical bifurcation, and the corresponding normal form of this bifurcation is recovered through scaling analysis. It is commonly accepted that the rQSSA is only valid in regions of parameter space where the influence of the singular point can be ignored. However, by using the method of slowly-varying Lyapunov functions, we provide asymptotic estimates for the validity of the rQSSA in parameter regions where the effect of the singular point cannot be disregarded.
Edit intent: meaning-changed
Original:  In doing so, we extend the established domain of validity for the rQSSA. Previous analysis has suggested that the rQSSA is only valid when initial enzyme concentrations greatly exceed initial substrate concentrations. However, our results indicate that this approximationcan be valid when initial enzyme and substrate concentrations are of equal magnitude . Consequently, this opens  the possibility of utilizing the rQSSA to model enzyme catalyzed reactions and estimate kinetic parameters in enzymatic assays at much lower enzyme to substrate ratios than was previously thought  .
Revised:  In doing so, we extend the established domain of validity for the reverse quasi-steady-state approximation . Consequently, this opens the possibility of utilizing the rQSSA to model enzyme catalyzed reactions and estimate kinetic parameters in enzymatic assays at much lower enzyme to substrate ratios than was previously thought .
Edit intent: meaning-changed
Original:  However, our results indicate that this approximationcan be valid when initial enzyme and substrate concentrations are of equal magnitude . Consequently, this opens  the possibility of utilizing the rQSSA to model enzyme catalyzed reactions and estimate kinetic parameters in enzymatic assays at much lower enzyme to substrate ratios than was previously thought  .
Revised:  However, our results indicate that this approximationcan be valid when initial enzyme and substrate concentrations are of equal magnitude . Consequently, this opens up the possibility of utilizing the rQSSA to model enzyme catalyzed reactions and estimate kinetic parameters in enzymatic assays at much lower enzyme to substrate ratios than was previously thought .
Edit intent: fluency
Original:  However, our results indicate that this approximationcan be valid when initial enzyme and substrate concentrations are of equal magnitude . Consequently, this opens  the possibility of utilizing the rQSSA to model enzyme catalyzed reactions and estimate kinetic parameters in enzymatic assays at much lower enzyme to substrate ratios than was previously thought  .
Revised:  However, our results indicate that this approximationcan be valid when initial enzyme and substrate concentrations are of equal magnitude . Consequently, this opens the possibility of utilizing the reverse quasi-steady-state approximation to model enzyme catalyzed reactions and estimate kinetic parameters in enzymatic assays at much lower enzyme to substrate ratios than was previously thought .
Edit intent: meaning-changed
Original:  However, our results indicate that this approximationcan be valid when initial enzyme and substrate concentrations are of equal magnitude . Consequently, this opens  the possibility of utilizing the rQSSA to model enzyme catalyzed reactions and estimate kinetic parameters in enzymatic assays at much lower enzyme to substrate ratios than was previously thought  .
Revised:  However, our results indicate that this approximationcan be valid when initial enzyme and substrate concentrations are of equal magnitude . Consequently, this opens the possibility of utilizing the rQSSA to model enzyme catalyzed reactions and estimate kinetic parameters in enzymatic assays at much lower enzyme to substrate ratios than was previously thought . Moreover, we show for the first time that the critical manifold of the reverse quasi-steady-state approximation contains a singular point where normal hyperbolicity is lost. Associated with this singularity is a transcritical bifurcation, and the corresponding normal form of this bifurcation is recovered through scaling analysis .
Edit intent: meaning-changed
Original:  Firstly, as well as being more explainable, time-series CL approaches outperform LSTM and a simple sliding window learner ( feed-forward neural net (FFNN) ) . Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM).
Revised:  Firstly, as well as being more explainable, time-series CL approaches outperform LSTMs as well as a simple sliding window learner ( feed-forward neural net (FFNN) ) . Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM).
Edit intent: coherence
Original:  Firstly, as well as being more explainable, time-series CL approaches outperform LSTM and a simple sliding window learner ( feed-forward neural net (FFNN) ) . Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM).
Revised:  Firstly, as well as being more explainable, time-series CL approaches outperform LSTM and a simple sliding window learner using feed-forward neural net (FFNN) ) . Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM).
Edit intent: clarity
Original:  Firstly, as well as being more explainable, time-series CL approaches outperform LSTM and a simple sliding window learner ( feed-forward neural net (FFNN) ) . Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM).
Revised:  Firstly, as well as being more explainable, time-series CL approaches outperform LSTM and a simple sliding window learner ( feed-forward neural networks (FFNN) ) . Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM).
Edit intent: fluency
Original:  Firstly, as well as being more explainable, time-series CL approaches outperform LSTM and a simple sliding window learner ( feed-forward neural net (FFNN) ) . Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM).
Revised:  Firstly, as well as being more explainable, time-series CL approaches outperform LSTM and a simple sliding window learner ( feed-forward neural net (FFNN) . Secondly, we show that CL based on a sliding window learner (FFNN) is more effective than CL based on a sequential learner (LSTM).
Edit intent: fluency
Original:  CLA is also used to test several distance approaches used in a memory recall-gate: euclidean distance (ED), dynamic time warping (DTW), auto-encoder (AE) and a novel hybrid approach, warp-AE.
Revised:  CLA is also used to test several distance approaches used in a memory recall-gate: Euclidean distance (ED), dynamic time warping (DTW), auto-encoder (AE) and a novel hybrid approach, warp-AE.
Edit intent: fluency
Original:  CLA is also used to test several distance approaches used in a memory recall-gate: euclidean distance (ED), dynamic time warping (DTW), auto-encoder (AE) and a novel hybrid approach, warp-AE.
Revised:  CLA is also used to test several distance approaches used in a memory recall-gate: euclidean distance (ED), dynamic time warping (DTW), auto-encoders (AE) and a novel hybrid approach, warp-AE.
Edit intent: fluency
Original:  We find CLA out-performs simple LSTM and FFNN learners and CLA based on a sliding window (CLA-FFNN) out-performs a LSTM (CLA-LSTM) implementation. While for memory-addressing, ED under-performs DTW and AE but warp-AE shows the best overall performance in a real-world financial task.
Revised:  We find that ED under-performs DTW and AE but warp-AE shows the best overall performance in a real-world financial task.
Edit intent: coherence
Original:  This can be expressed as the image of an `origami map' \varphi :D\to \boldsymbol  ^3 such that \Gamma is the singular set of \varphi , the word `origami' coming from the Japanese term for paper folding.
Revised:  This can be expressed as the image of an "origami map" \Phi :D\to \boldsymbol ^3 such that \Gamma is the singular set of \varphi , the word `origami' coming from the Japanese term for paper folding.
Edit intent: clarity
Original:  This can be expressed as the image of an `origami map' \varphi :D\to \boldsymbol  ^3 such that \Gamma is the singular set of \varphi , the word `origami' coming from the Japanese term for paper folding.
Revised:  This can be expressed as the image of an `origami map' \varphi :D\to \boldsymbol R ^3 such that \Gamma is the singular set of \varphi , the word `origami' coming from the Japanese term for paper folding.
Edit intent: meaning-changed
Original:  This can be expressed as the image of an `origami map' \varphi :D\to \boldsymbol  ^3 such that \Gamma is the singular set of \varphi , the word `origami' coming from the Japanese term for paper folding.
Revised:  This can be expressed as the image of an `origami map' \varphi :D\to \boldsymbol ^3 such that \Gamma is the singular set of \Phi , the word `origami' coming from the Japanese term for paper folding.
Edit intent: clarity
Original:  This can be expressed as the image of an `origami map' \varphi :D\to \boldsymbol  ^3 such that \Gamma is the singular set of \varphi , the word `origami' coming from the Japanese term for paper folding.
Revised:  This can be expressed as the image of an `origami map' \varphi :D\to \boldsymbol ^3 such that \Gamma is the singular set of \varphi , the word "origami" coming from the Japanese term for paper folding.
Edit intent: fluency
Original:  In this paper, we show that the case of four mutually non-congruent curved foldings with the same crease and crease pattern occurs if and only if  \Gamma and C do not admit any symmetries.
Revised:  In this paper, we determine the possibility of the number N of congruence classes of curved foldings with the same crease and crease pattern occurs if and only if \Gamma and C do not admit any symmetries.
Edit intent: meaning-changed
Original:  In this paper, we show that the case of four mutually non-congruent curved foldings with the same crease and crease pattern occurs if and only if  \Gamma and C do not admit any symmetries.
Revised:  In this paper, we show that the case of four mutually non-congruent curved foldings with the same crease and crease pattern . As a consequence, if C is a non-closed simple arc, then N=4 if and only if \Gamma and C do not admit any symmetries.
Edit intent: meaning-changed
Original:  In this paper, we show that the case of four mutually non-congruent curved foldings with the same crease and crease pattern occurs if and only if  \Gamma and C do not admit any symmetries.
Revised:  In this paper, we show that the case of four mutually non-congruent curved foldings with the same crease and crease pattern occurs if and only if both \Gamma and C do not admit any symmetries.
Edit intent: meaning-changed
Original: Statistical modeling techniques, such as projection pursuit regression (PPR) and convolutional neural network (CNN)approaches, provide state-of-the-art performance in characterizing visual cortical neurons' receptive fields and predicting their responses to arbitrary input stimuli.
Revised: System identification techniques---projection pursuit regression models (PPR) and convolutional neural network (CNN)approaches, provide state-of-the-art performance in characterizing visual cortical neurons' receptive fields and predicting their responses to arbitrary input stimuli.
Edit intent: clarity
Original: Statistical modeling techniques, such as projection pursuit regression (PPR) and convolutional neural network (CNN)approaches, provide state-of-the-art performance in characterizing visual cortical neurons' receptive fields and predicting their responses to arbitrary input stimuli.
Revised: Statistical modeling techniques, such as projection pursuit regression (PPR) and convolutional neural networks (CNNs)---provide state-of-the-art performance in characterizing visual cortical neurons' receptive fields and predicting their responses to arbitrary input stimuli.
Edit intent: clarity
Original: Statistical modeling techniques, such as projection pursuit regression (PPR) and convolutional neural network (CNN)approaches, provide state-of-the-art performance in characterizing visual cortical neurons' receptive fields and predicting their responses to arbitrary input stimuli.
Revised: Statistical modeling techniques, such as projection pursuit regression (PPR) and convolutional neural network (CNN)approaches, provide state-of-the-art performance in predicting visual cortical neurons' receptive fields and predicting their responses to arbitrary input stimuli.
Edit intent: clarity
Original: Statistical modeling techniques, such as projection pursuit regression (PPR) and convolutional neural network (CNN)approaches, provide state-of-the-art performance in characterizing visual cortical neurons' receptive fields and predicting their responses to arbitrary input stimuli.
Revised: Statistical modeling techniques, such as projection pursuit regression (PPR) and convolutional neural network (CNN)approaches, provide state-of-the-art performance in characterizing visual cortical neurons' responses to arbitrary input stimuli.
Edit intent: clarity
Original:  However, the latent feature components recovered by these methods, particularly that of CNN , are often noisy and lack coherent structures , making it difficult to understand the underlying feature components that are used to construct the receptive fieldfeature tunings of the neurons .
Revised:  However, the constituent kernels recovered by these methods, particularly that of CNN , are often noisy and lack coherent structures , making it difficult to understand the underlying feature components that are used to construct the receptive fieldfeature tunings of the neurons .
Edit intent: meaning-changed
Original:  However, the latent feature components recovered by these methods, particularly that of CNN , are often noisy and lack coherent structures , making it difficult to understand the underlying feature components that are used to construct the receptive fieldfeature tunings of the neurons .
Revised:  However, the latent feature components recovered by these methods, particularly those of CNNs , are often noisy and lack coherent structures , making it difficult to understand the underlying feature components that are used to construct the receptive fieldfeature tunings of the neurons .
Edit intent: coherence
Original:  However, the latent feature components recovered by these methods, particularly that of CNN , are often noisy and lack coherent structures , making it difficult to understand the underlying feature components that are used to construct the receptive fieldfeature tunings of the neurons .
Revised:  However, the latent feature components recovered by these methods, particularly that of CNN , are often noisy and lack coherent structure , making it difficult to understand the underlying feature components that are used to construct the receptive fieldfeature tunings of the neurons .
Edit intent: coherence
Original:  However, the latent feature components recovered by these methods, particularly that of CNN , are often noisy and lack coherent structures , making it difficult to understand the underlying feature components that are used to construct the receptive fieldfeature tunings of the neurons .
Revised:  However, the latent feature components recovered by these methods, particularly that of CNN , are often noisy and lack coherent structures , making it difficult to understand the underlying component features of a neuron's receptive field .
Edit intent: clarity
Original:  In this paper, we showed that using a dictionary of complex sparse codes,  learned from natural scenes based on efficient coding theory, as the front-end for these methods can improve their performance in neural response prediction.
Revised:  In this paper, we showed that using a dictionary of complex sparse codes, which are learned from natural scenes based on efficient coding theory, as the front-end for these methods can improve their performance in neural response prediction.
Edit intent: coherence
Original:  In this paper, we showed that using a dictionary of complex sparse codes,  learned from natural scenes based on efficient coding theory, as the front-end for these methods can improve their performance in neural response prediction.
Revised:  In this paper, we showed that using a dictionary of complex sparse codes, learned from natural scenes based on efficient coding theory, as the front-end for PPR and CNNs can improve their performance in neural response prediction.
Edit intent: meaning-changed