wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In order to elucidate the relationship between our distance between two dynamic metric spaces and the bottleneck distance between their Rips zigzag barcodes, we exploit recent advances in the stability of zigzag persistence ( due to Botnan and Lesnick ).
Revised:  In order to elucidate the relationship between our distance between two dynamic metric spaces and the bottleneck distance between their Rips zigzag barcodes, we exploit recent advances in the stability of zigzag persistence due to Botnan and Lesnick ).
Edit intent: fluency
Original:  In order to elucidate the relationship between our distance between two dynamic metric spaces and the bottleneck distance between their Rips zigzag barcodes, we exploit recent advances in the stability of zigzag persistence ( due to Botnan and Lesnick ). By providing explicit constructions, we prove that for each integer k\geq 1 there exist pairs of DMSs at finite interleaving distance whose k-th persistent homology barcodes are at infinite barcode distance .
Revised:  In order to elucidate the relationship between our distance between two dynamic metric spaces and the bottleneck distance between their Rips zigzag barcodes, we exploit recent advances in the stability of zigzag persistence ( due to Botnan and Lesnick , and to Bjerkevik .
Edit intent: meaning-changed
Original:  We first estimate marginal effects and treatment propensities  to form an objective function that isolates the heterogeneous treatment effects, and then optimize the learned objective . This approach has several advantages over existing methods.
Revised:  We first estimate marginal effects and treatment propensities to form an objective function that isolates the causal component of the signal. Then, we optimize this data-adaptive objective function. Our approach has several advantages over existing methods.
Edit intent: meaning-changed
Original:  From a practical perspective, our method is very flexible and easy to use: In both steps, we can use any method of our choice , e.g., penalized regression, a deep net , or boosting;
Revised:  From a practical perspective, our method is flexible and easy to use: In both steps, we can use any method of our choice , e.g., penalized regression, a deep net , or boosting;
Edit intent: coherence
Original:  From a practical perspective, our method is very flexible and easy to use: In both steps, we can use any method of our choice , e.g., penalized regression, a deep net , or boosting;
Revised:  From a practical perspective, our method is very flexible and easy to use: For both steps, we can use any method of our choice , e.g., penalized regression, a deep net , or boosting;
Edit intent: clarity
Original:  From a practical perspective, our method is very flexible and easy to use: In both steps, we can use any method of our choice , e.g., penalized regression, a deep net , or boosting;
Revised:  From a practical perspective, our method is very flexible and easy to use: In both steps, we can use any loss-minimization method , e.g., penalized regression, a deep net , or boosting;
Edit intent: clarity
Original:  From a practical perspective, our method is very flexible and easy to use: In both steps, we can use any method of our choice , e.g., penalized regression, a deep net , or boosting;
Revised:  From a practical perspective, our method is very flexible and easy to use: In both steps, we can use any method of our choice , e.g., penalized regression, deep neutral networks , or boosting;
Edit intent: meaning-changed
Original:  Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property , whereby even if our pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same regret bounds as an oracle who has a-priori knowledge of these  nuisance components.
Revised:  Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property : Even if the pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same regret bounds as an oracle who has a-priori knowledge of these nuisance components.
Edit intent: coherence
Original:  Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property , whereby even if our pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same regret bounds as an oracle who has a-priori knowledge of these  nuisance components.
Revised:  Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property , whereby even if our pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same regret bounds as an oracle who has a priori knowledge of these nuisance components.
Edit intent: fluency
Original:  Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property , whereby even if our pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same regret bounds as an oracle who has a-priori knowledge of these  nuisance components.
Revised:  Meanwhile, in the case of penalized kernel regression, we show that our method has a quasi-oracle property , whereby even if our pilot estimates for marginal effects and treatment propensities are not particularly accurate, we achieve the same regret bounds as an oracle who has a-priori knowledge of these two nuisance components.
Edit intent: clarity
Original:  DeepPicar is a small scale replication of a real self-driving car called Dave-2 by NVIDIA, which drove on public roads using a deep convolutional neural network (CNN), that takes images from a front-facing camera as input and produces car steering angles as output.
Revised:  DeepPicar is a small scale replication of a real self-driving car called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN), that takes images from a front-facing camera as input and produces car steering angles as output.
Edit intent: clarity
Original:  DeepPicar is a small scale replication of a real self-driving car called Dave-2 by NVIDIA, which drove on public roads using a deep convolutional neural network (CNN), that takes images from a front-facing camera as input and produces car steering angles as output.
Revised:  DeepPicar is a small scale replication of a real self-driving car called Dave-2 by NVIDIA, which drove on public roads using a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces car steering angles as output.
Edit intent: fluency
Original:  DeepPicar uses almost the exact same network architecture---9 layers, 27 million connections and 250K parameters---and can be trained to drive itself , in real-time , using a web camera and a modest Raspberry Pi 3 quad-core platform.
Revised:  DeepPicar uses the same network architecture---9 layers, 27 million connections and 250K parameters---and can be trained to drive itself , in real-time , using a web camera and a modest Raspberry Pi 3 quad-core platform.
Edit intent: clarity
Original:  DeepPicar uses almost the exact same network architecture---9 layers, 27 million connections and 250K parameters---and can be trained to drive itself , in real-time , using a web camera and a modest Raspberry Pi 3 quad-core platform.
Revised:  DeepPicar uses almost the exact same network architecture---9 layers, 27 million connections and 250K parameters---and can drive itself in real-time , using a web camera and a modest Raspberry Pi 3 quad-core platform.
Edit intent: clarity
Original:  DeepPicar uses almost the exact same network architecture---9 layers, 27 million connections and 250K parameters---and can be trained to drive itself , in real-time , using a web camera and a modest Raspberry Pi 3 quad-core platform.
Revised:  DeepPicar uses almost the exact same network architecture---9 layers, 27 million connections and 250K parameters---and can be trained to drive itself , in real-time using a web camera and a modest Raspberry Pi 3 quad-core platform.
Edit intent: fluency
Original:  DeepPicar uses almost the exact same network architecture---9 layers, 27 million connections and 250K parameters---and can be trained to drive itself , in real-time , using a web camera and a modest Raspberry Pi 3 quad-core platform.
Revised:  DeepPicar uses almost the exact same network architecture---9 layers, 27 million connections and 250K parameters---and can be trained to drive itself , in real-time , using a web camera and a Raspberry Pi 3 quad-core platform.
Edit intent: clarity
Original:  However,  shared resource contention remains an important issue that must be considered in applying deep-learning models on shared memory based embedded computing platforms  .
Revised:  However, shared resource contention remains an important issue that must be considered in applying deep-learning models on shared memory based embedded computing platforms ; we observe up to 11.6X execution time increase in the CNN based control loop due to shared resource contention. To protect the CNN workload, we also evaluate state-of-the-art cache partitioning and memory bandwidth throttling techniques on the Pi 3. We find that cache partitioning is ineffective, while memory bandwidth throttling is an effective solution .
Edit intent: meaning-changed
Original:  Anti-amyloid beta (ABeta) antibodies are the leading drug candidates to treat AD, but the results of clinical trials over the past two decades have been mostly disappointing. This was due to either low effectiveness or serious side effects that emerged during clinical trials.
Revised:  Anti-amyloid beta (ABeta) antibodies are the leading drug candidates to treat AD, but the results of clinical trials have been disappointing.
Edit intent: coherence
Original:  Introducing rational mutations into anti-ABeta antibodies to increase their effectiveness and remove harmful side reactions is a way forward, but the path to take is unclear since the key structural characteristics that determine the binding affinity and selectivity of anti-ABeta antibodies towards amyloid species are not well understood .
Revised:  Introducing rational mutations into anti-ABeta antibodies to increase their effectiveness is a way forward, but the path to take is unclear since the key structural characteristics that determine the binding affinity and selectivity of anti-ABeta antibodies towards amyloid species are not well understood .
Edit intent: coherence
Original:  Introducing rational mutations into anti-ABeta antibodies to increase their effectiveness and remove harmful side reactions is a way forward, but the path to take is unclear since the key structural characteristics that determine the binding affinity and selectivity of anti-ABeta antibodies towards amyloid species are not well understood .
Revised:  Introducing rational mutations into anti-ABeta antibodies to increase their effectiveness and remove harmful side reactions is a way forward, but the path to take is unclear .
Edit intent: coherence
Original:  In this study, we have taken a computational approach to examine how an anti-ABeta antibody binds to one or more ABeta epitopes in its antigen-combining site.
Revised:  In this study, we demonstrate the use of computational fragment-based docking and MMPBSA binding free energy calculations in the analysis of anti-ABeta antibody binds to one or more ABeta epitopes in its antigen-combining site.
Edit intent: meaning-changed
Original:  In this study, we have taken a computational approach to examine how an anti-ABeta antibody binds to one or more ABeta epitopes in its antigen-combining site. Our unbiased fragment-based docking method successfully predicted the emergence of the key EFRH epitopecommonly observed when ABeta binds to anti-ABeta antibodies.
Revised:  In this study, we have taken a computational approach to examine how an anti-ABeta antibodies for rational drug design efforts. Our fragment-based docking method successfully predicted the emergence of the key EFRH epitopecommonly observed when ABeta binds to anti-ABeta antibodies.
Edit intent: clarity
Original:  MD simulations coupled with MMPBSA binding free energy calculations were used to analyze scenarios described in prior studies concerning the nature of anti-ABeta antibody binding to various ABeta species. Finally, based on observing our MD trajectories of the PFA1 antibody bound to ABeta_2-7 and pE3ABeta_3-8 ,  we introduced rational mutations into PFA1 in an effort to improve the calculated binding affinity of PFA1 towards the pE3-ABeta_3-8 form of ABeta.
Revised:  MD simulations coupled with MMPBSA binding free energy calculations were used to analyze scenarios described in prior studies , we introduced rational mutations into PFA1 in an effort to improve the calculated binding affinity of PFA1 towards the pE3-ABeta_3-8 form of ABeta.
Edit intent: coherence
Original:  Finally, based on observing our MD trajectories of the PFA1 antibody bound to ABeta_2-7 and pE3ABeta_3-8 ,  we introduced rational mutations into PFA1 in an effort to improve the calculated binding affinity of PFA1 towards the pE3-ABeta_3-8 form of ABeta.
Revised:  Finally, based on observing our MD trajectories of the PFA1 antibody bound to ABeta_2-7 and pE3ABeta_3-8 , and we introduced rational mutations into PFA1 in an effort to improve the calculated binding affinity of PFA1 towards the pE3-ABeta_3-8 form of ABeta.
Edit intent: coherence
Original:  Finally, based on observing our MD trajectories of the PFA1 antibody bound to ABeta_2-7 and pE3ABeta_3-8 ,  we introduced rational mutations into PFA1 in an effort to improve the calculated binding affinity of PFA1 towards the pE3-ABeta_3-8 form of ABeta.
Revised:  Finally, based on observing our MD trajectories of the PFA1 antibody bound to ABeta_2-7 and pE3ABeta_3-8 , we introduced rational mutations into PFA1 to improve its calculated binding affinity of PFA1 towards the pE3-ABeta_3-8 form of ABeta.
Edit intent: clarity
Original:  Finally, based on observing our MD trajectories of the PFA1 antibody bound to ABeta_2-7 and pE3ABeta_3-8 ,  we introduced rational mutations into PFA1 in an effort to improve the calculated binding affinity of PFA1 towards the pE3-ABeta_3-8 form of ABeta.
Revised:  Finally, based on observing our MD trajectories of the PFA1 antibody bound to ABeta_2-7 and pE3ABeta_3-8 , we introduced rational mutations into PFA1 in an effort to improve the calculated binding affinity towards the pE3-ABeta3-8 form of ABeta.
Edit intent: clarity
Original: There has been growing evidence that cooperative interactions and configurational rearrangements underpin protein functions. But in spite of vast genetic and structural data, the information-dense, heterogeneous nature of protein has held back the progress in understanding the underlying principles. Here we outline a general theory of protein that quantitatively links sequence, dynamics and function: The protein is a strongly-coupled amino acid network whose interactions and large-scale motions are captured by the mechanical propagator, also known as the Green function.
Revised: There has been growing evidence that cooperative interactions and rearrangements of their amino acids, which exhibit large-scale dynamical modes. Long-range correlations have also been revealed in protein sequences, and this has motivated the search for physical links between the observed genetic and dynamic cooperativity. We outline here a simplified theory of protein that quantitatively links sequence, dynamics and function: The protein is a strongly-coupled amino acid network whose interactions and large-scale motions are captured by the mechanical propagator, also known as the Green function.
Edit intent: coherence
Original:  Here we outline a general theory of protein that quantitatively links sequence, dynamics and function: The protein is a strongly-coupled amino acid network whose interactions and large-scale motions are captured by the mechanical propagator, also known as the Green function.
Revised:  Here we outline a general theory of protein , which relates sequence correlations to physical interactions and to the emergence of mechanical function. Our protein is modeled as a strongly-coupled amino acid network whose interactions and large-scale motions are captured by the mechanical propagator, also known as the Green function.
Edit intent: clarity
Original:  Here we outline a general theory of protein that quantitatively links sequence, dynamics and function: The protein is a strongly-coupled amino acid network whose interactions and large-scale motions are captured by the mechanical propagator, also known as the Green function.
Revised:  Here we outline a general theory of protein that quantitatively links sequence, dynamics and function: The protein is a strongly-coupled amino acid network whose interactions and motions are captured by the mechanical propagator, also known as the Green function.
Edit intent: clarity
Original:  Here we outline a general theory of protein that quantitatively links sequence, dynamics and function: The protein is a strongly-coupled amino acid network whose interactions and large-scale motions are captured by the mechanical propagator, also known as the Green function.
Revised:  Here we outline a general theory of protein that quantitatively links sequence, dynamics and function: The protein is a strongly-coupled amino acid network whose interactions and large-scale motions are captured by the mechanical propagator, the Green function.
Edit intent: coherence
Original:  In order to have a general model for systemic (or macroscopic) events, we show that the above system converges to a unique mean field limit , which is characterized by a nonlinear SPDE on the half-line  with a Dirichlet boundary condition  .
Revised:  In order to have a general model for systemic (or macroscopic) events, we show that the system converges to a unique mean field limit , which is characterized by a nonlinear SPDE on the half-line with a Dirichlet boundary condition .
Edit intent: clarity
Original:  In order to have a general model for systemic (or macroscopic) events, we show that the above system converges to a unique mean field limit , which is characterized by a nonlinear SPDE on the half-line  with a Dirichlet boundary condition  .
Revised:  In order to have a general model for systemic (or macroscopic) events, we show that the above system converges to a unique mean field limit characterized by a nonlinear SPDE on the half-line with a Dirichlet boundary condition .
Edit intent: coherence
Original:  In order to have a general model for systemic (or macroscopic) events, we show that the above system converges to a unique mean field limit , which is characterized by a nonlinear SPDE on the half-line  with a Dirichlet boundary condition  .
Revised:  In order to have a general model for systemic (or macroscopic) events, we show that the above system converges to a unique mean field limit , which is characterized by a nonlinear SPDE on the half-line ( with a Dirichlet boundary condition .
Edit intent: fluency
Original:  In order to have a general model for systemic (or macroscopic) events, we show that the above system converges to a unique mean field limit , which is characterized by a nonlinear SPDE on the half-line  with a Dirichlet boundary condition  .
Revised:  In order to have a general model for systemic (or macroscopic) events, we show that the above system converges to a unique mean field limit , which is characterized by a nonlinear SPDE on the half-line with a Dirichlet boundary condition ) which governs the conditional law of a 'conditional McKean-Vlasov' type diffusion .
Edit intent: meaning-changed
Original:  In other words,  there are events of small probability that  can give rise to systemic default cascades sparked by a devaluation of the common exposures and amplified by herd behaviour .
Revised:  In other words, sparked by a devaluation of the common exposures, there are events of small probability that can give rise to systemic default cascades sparked by a devaluation of the common exposures and amplified by herd behaviour .
Edit intent: fluency
Original:  In other words,  there are events of small probability that  can give rise to systemic default cascades sparked by a devaluation of the common exposures and amplified by herd behaviour .
Revised:  In other words, there are events of small probability that , through amplification by herd behaviour, can give rise to systemic default cascades sparked by a devaluation of the common exposures and amplified by herd behaviour .
Edit intent: clarity
Original:  It is often of considerable interest to obtain numerical solutions for such equations, but the singular (" point-like " ) modeling of the sources in these problems typically introduces nontrivial obstacles for devising a satisfactory numerical implementation .
Revised:  It is often of considerable interest to obtain numerical solutions for such equations, but any singular (" point-like " ) modeling of the sources in these problems typically introduces nontrivial obstacles for devising a satisfactory numerical implementation .
Edit intent: clarity
Original:  It is often of considerable interest to obtain numerical solutions for such equations, but the singular (" point-like " ) modeling of the sources in these problems typically introduces nontrivial obstacles for devising a satisfactory numerical implementation .
Revised:  It is often of considerable interest to obtain numerical solutions for such equations, but the singular (" particle " ) modeling of the sources in these problems typically introduces nontrivial obstacles for devising a satisfactory numerical implementation .
Edit intent: meaning-changed
Original:  Previously introduced in the context of the self-force problem in gravitational physics, the idea is to discretize the computational domain into two (or more) disjoint pseudospectral (Chebyshev-Lobatto) grids in such a way that the "particle" (the singular source location) is always at the interface between them;
Revised:  Previously introduced in the context of the self-force problem in gravitational physics, the idea is to discretize the computational domain into two (or more) disjoint pseudospectral (Chebyshev-Lobatto) grids such that the "particle" (the singular source location) is always at the interface between them;
Edit intent: fluency
Original:  Previously introduced in the context of the self-force problem in gravitational physics, the idea is to discretize the computational domain into two (or more) disjoint pseudospectral (Chebyshev-Lobatto) grids in such a way that the "particle" (the singular source location) is always at the interface between them;
Revised:  Previously introduced in the context of the self-force problem in gravitational physics, the idea is to discretize the computational domain into two (or more) disjoint pseudospectral (Chebyshev-Lobatto) grids in such a way that the "particle" is always at the interface between them;
Edit intent: clarity