wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Addressing these issues  , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Revised:  Addressing these issues , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) , (ii) a data-driven weight prediction (WP-MRP) variant.
Edit intent: fluency
Original:  Addressing these issues  , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant.
Revised:  Addressing these issues , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) an accurate but mostly theoretical optimised MRP (O-MRP), and (iii) a transferable weight prediction MRP (WP-MRP) variant.
Edit intent: meaning-changed
Original:  Addressing these issues  , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) variant. Both these interpolation variants operate locally, while  implicitly accounting for global spatial relationships in the entire system through recursion.
Revised:  Addressing these issues , we propose a value propagation method, inspired by Markov reward processes (MRPs) , as a spatial interpolation method, and introduce two variants thereof: (i) a static discount (SD-MRP) and (ii) a data-driven weight prediction (WP-MRP) . All variants of MRP interpolation operate locally, while implicitly accounting for global spatial relationships in the entire system through recursion.
Edit intent: clarity
Original:   We evaluated our proposed methods by comparing the mean absolute errors and running times of interpolated grid cells to those of 7 common baselines . Our analysis involved detailed experiments on two synthetic and two real-world datasets over 44 total experimental conditions. Experimental results show the competitive advantage of MRP interpolation on real-world data, as the average performance of SD-MRP on real-world data under all experimental conditionswas ranked significantly higher than that of all other methods, followed by WP-MRP.
Revised:  We evaluated our proposed methods by comparing the mean absolute errors and running times of interpolated grid cells to those of 7 common baselines , selected from models based on spatial autocorrelation, (spatial) regression, and deep learning. We performed detailed evaluations on two publicly available datasets (local GDP values, and COVID-19 patient trajectory data). The results from these experiments clearly show the competitive advantage of MRP interpolation on real-world data, as the average performance of SD-MRP on real-world data under all experimental conditionswas ranked significantly higher than that of all other methods, followed by WP-MRP.
Edit intent: meaning-changed
Original:  Experimental results show the competitive advantage of MRP interpolation on real-world data, as the average performance of SD-MRP on real-world data under all experimental conditionswas ranked significantly higher than that of all other methods, followed by WP-MRP. On synthetic data, we show that WP-MRP can perform better than SD-MRP given sufficiently informative features. We further found that, even in cases where our methods had no significant advantage over baselines numerically, our methods preserved the spatial structure of the target grid better than the baselines .
Revised:  Experimental results show the competitive advantage of MRP interpolation , which achieved significantly lower errors than the existing methods in 23 out of 40 experimental conditions, or 35 out of 40 when including O-MRP .
Edit intent: meaning-changed
Original:  To address thisissue, we propose a general motif prediction problem . We establish the theoretical foundation of motif prediction and we propose several heuristics that , for a fixed set of nodes in a graph and a specified motif, assess the chances for this motif to appear.
Revised:  To address thisissue, we propose a general motif prediction problem and we propose several heuristics that , for a fixed set of nodes in a graph and a specified motif, assess the chances for this motif to appear.
Edit intent: coherence
Original:  To make the scores realistic, our heuristics  - among others - consider correlations between links, i.e., the potential impact of some arriving links on the appearance of other parts of a given motif.
Revised:  To make the scores realistic, our heuristics consider - among others - consider correlations between links, i.e., the potential impact of some arriving links on the appearance of other parts of a given motif.
Edit intent: fluency
Original:  While our heuristics are fast and do not need any training, using GNNs ensures highest accuracy when predicting the arrival of complex graph structures, both dense (e.g., k-cliques) and sparse (e.g., k-stars).
Revised:  While our heuristics are fast and do not need any training, GNNs ensure highest accuracy of predicting motifs, both for dense (e.g., k-cliques) and sparse (e.g., k-stars).
Edit intent: clarity
Original:  While our heuristics are fast and do not need any training, using GNNs ensures highest accuracy when predicting the arrival of complex graph structures, both dense (e.g., k-cliques) and sparse (e.g., k-stars).
Revised:  While our heuristics are fast and do not need any training, using GNNs ensures highest accuracy when predicting the arrival of complex graph structures, both dense (e.g., k-cliques) and for sparse ones (e.g., k-stars).
Edit intent: clarity
Original:  Moreover, these imputation methods tend to initialize these missing valueswith strong prior assumptions, while the unreliability of such initialization is rarely considered. To tackle these problems, a novel semi-supervised conditional normalizing flow (SSCFlow) is proposed in this paper.
Revised:  Moreover, these imputation methods tend to initialize these missing valueswith strong prior assumptions, while the unreliability of such initialization might lead to inferior performance. To address these problems, a novel semi-supervised conditional normalizing flow (SSCFlow) is proposed in this paper.
Edit intent: clarity
Original:  Moreover, SSCFlow takes the initialized missing values as corrupted initial imputation and iteratively reconstructs their latent representations with an overcomplete denoising autoencoder to approximate the true conditional probability density of missing values.
Revised:  Moreover, SSCFlow treats the initialized missing values as corrupted initial imputation and iteratively reconstructs their latent representations with an overcomplete denoising autoencoder to approximate the true conditional probability density of missing values.
Edit intent: clarity
Original:  Moreover, SSCFlow takes the initialized missing values as corrupted initial imputation and iteratively reconstructs their latent representations with an overcomplete denoising autoencoder to approximate the true conditional probability density of missing values. Experiments have been conducted with real-world datasets to demonstrate the robustness and efficiency of the proposed algorithm.
Revised:  Moreover, SSCFlow takes the initialized missing values as corrupted initial imputation and iteratively reconstructs their latent representations with an overcomplete denoising autoencoder to approximate their true conditional distribution. Experiments on real-world datasets to demonstrate the robustness and efficiency of the proposed algorithm.
Edit intent: clarity
Original: The duality of sensory inference and optimal control was known since 1960s and has recently been recognized as common computations required for  posterior distributions in dynamic Bayesian inference and  value functions in optimal control.
Revised: The duality of sensory inference and motor control has been known since the 1960s and has recently been recognized as common computations required for posterior distributions in dynamic Bayesian inference and value functions in optimal control.
Edit intent: clarity
Original: The duality of sensory inference and optimal control was known since 1960s and has recently been recognized as common computations required for  posterior distributions in dynamic Bayesian inference and  value functions in optimal control.
Revised: The duality of sensory inference and optimal control was known since 1960s and has recently been recognized as the commonality in computations required for posterior distributions in dynamic Bayesian inference and value functions in optimal control.
Edit intent: clarity
Original: The duality of sensory inference and optimal control was known since 1960s and has recently been recognized as common computations required for  posterior distributions in dynamic Bayesian inference and  value functions in optimal control.
Revised: The duality of sensory inference and optimal control was known since 1960s and has recently been recognized as common computations required for the posterior distributions in dynamic Bayesian inference and value functions in optimal control.
Edit intent: fluency
Original: The duality of sensory inference and optimal control was known since 1960s and has recently been recognized as common computations required for  posterior distributions in dynamic Bayesian inference and  value functions in optimal control.
Revised: The duality of sensory inference and optimal control was known since 1960s and has recently been recognized as common computations required for posterior distributions in dynamic Bayesian inference and the value functions in optimal control.
Edit intent: fluency
Original:  Meanwhile, an intriguing question about the brain is why  entire neocortex shares a canonical six-layer architecture , while its posterior and anterior halves are engaged in sensory processing and motor control, respectively.
Revised:  Meanwhile, an intriguing question about the brain is why the entire neocortex shares a canonical six-layer architecture , while its posterior and anterior halves are engaged in sensory processing and motor control, respectively.
Edit intent: fluency
Original:  Meanwhile, an intriguing question about the brain is why  entire neocortex shares a canonical six-layer architecture , while its posterior and anterior halves are engaged in sensory processing and motor control, respectively.
Revised:  Meanwhile, an intriguing question about the brain is why entire neocortex shares a canonical six-layer architecture while its posterior and anterior halves are engaged in sensory processing and motor control, respectively.
Edit intent: fluency
Original:  We  explore how different types of cortical neurons may represent different variables , such as prior and posterior distributions and value functions, and what cortical dynamics may realize required computations.
Revised:  We first review the classic duality of inference and control in linear quadratic systems and then review the correspondence between dynamic Bayesian inference and optimal control. Based on the architecture of the canonical cortical circuit, we explore how different types of cortical neurons may represent different variables , such as prior and posterior distributions and value functions, and what cortical dynamics may realize required computations.
Edit intent: meaning-changed
Original:  Unfortunately , recent studies indicate that such advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are purposely changed. However, little is known what's the difference between adversarial nodes and clean nodes, and what's the preference of each attack method, in terms of network structure. In this paper , we theoretically investigate three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax , and find that different attack methods have their specific attack preferences on changing network structure .
Revised:  Unfortunately , recent studies indicate that such advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This paper investigates three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax , and find that different attack methods have their specific attack preferences on changing network structure .
Edit intent: coherence
Original:  In this paper , we theoretically investigate three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax , and find that different attack methods have their specific attack preferences on changing network structure .
Revised:  In this paper , we theoretically investigate three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax . It is found that different attack methods have their specific attack preferences on changing network structure .
Edit intent: coherence
Original:  In this paper , we theoretically investigate three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax , and find that different attack methods have their specific attack preferences on changing network structure .
Revised:  In this paper , we theoretically investigate three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax , and find that different attack methods have their specific attack preferences on changing the target network structures .
Edit intent: clarity
Original:  Such attack patterns are further validated by the experimental results on  real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method.
Revised:  Such attack pattern are further verified by experimental results on real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method.
Edit intent: clarity
Original:  Such attack patterns are further validated by the experimental results on  real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method.
Revised:  Such attack patterns are further validated by the experimental results on some real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method.
Edit intent: clarity
Original:  Such attack patterns are further validated by the experimental results on  real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method.
Revised:  Such attack patterns are further validated by the experimental results on real-world networks, revealing that generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method.
Edit intent: coherence
Original:  Such attack patterns are further validated by the experimental results on  real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method.
Revised:  Such attack patterns are further validated by the experimental results on real-world networks, i.e., generally the top four most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method.
Edit intent: fluency
Original:  Such attack patterns are further validated by the experimental results on  real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method.
Revised:  Such attack patterns are further validated by the experimental results on real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples suffice to explain the preference of each attack method.
Edit intent: clarity
Original:  Such attack patterns are further validated by the experimental results on  real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of each attack method.
Revised:  Such attack patterns are further validated by the experimental results on real-world networks, i.e., generally the top 4 most important network attributes on detecting adversarial samples are sufficient to explain the preference of an attack method.
Edit intent: clarity
Original:  Finally, considering the physical properties of heat transfer , this work proposes four different losses and joint learns the deep surrogate model with these losses  . Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep surrogate models for temperature field reconstruction .
Revised:  Finally, considering the physical characteristics of heat conduction as well as the boundary conditions , this work proposes four different losses and joint learns the deep surrogate model with these losses . Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep surrogate models for temperature field reconstruction .
Edit intent: meaning-changed
Original:  Finally, considering the physical properties of heat transfer , this work proposes four different losses and joint learns the deep surrogate model with these losses  . Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep surrogate models for temperature field reconstruction .
Revised:  Finally, considering the physical properties of heat transfer , this work proposes the physics-informed reconstruction loss including four training losses and joint learns the deep surrogate model with these losses . Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep surrogate models for temperature field reconstruction .
Edit intent: meaning-changed
Original:  Finally, considering the physical properties of heat transfer , this work proposes four different losses and joint learns the deep surrogate model with these losses  . Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep surrogate models for temperature field reconstruction .
Revised:  Finally, considering the physical properties of heat transfer , this work proposes four different losses and joint learns the deep surrogate model with these losses unsupervisedly . Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep surrogate models for temperature field reconstruction .
Edit intent: meaning-changed
Original:  Finally, considering the physical properties of heat transfer , this work proposes four different losses and joint learns the deep surrogate model with these losses  . Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep surrogate models for temperature field reconstruction .
Revised:  Finally, considering the physical properties of heat transfer , this work proposes four different losses and joint learns the deep surrogate model with these losses . Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep reversible regression models for TFR-HSS task .
Edit intent: clarity
Original:  All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by URLanism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions:  1) Strong AI is not possible.
Revised:  All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by URLanism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions: ( 1) Strong AI is not possible.
Edit intent: fluency
Original:  All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by URLanism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions:  1) Strong AI is not possible.
Revised:  All biological adaptations are either affordances seized by heritable variation and selection or, far faster, by URLanism acting in its world finding uses of X to accomplish Y. Based on this, we reach rather astonishing conclusions: 1) Artificial General Intelligence based on
Edit intent: clarity
Original:   4) Based on Heisenberg's interpretation of the quantum state as "Potentia" converted to "Actuals" by Measurement, a natural hypothesis is that mind actualizes Potentia.
Revised:  ( 4) Based on Heisenberg's interpretation of the quantum state as "Potentia" converted to "Actuals" by Measurement, a natural hypothesis is that mind actualizes Potentia.
Edit intent: fluency
Original:  Based on the traditional Bayesian neural network framework, the ResNet-18 _BNN network constructed in this paper has been improved in the following three aspects: (1) A new objective function is proposed, which is composed of the KL loss of uncertain parameters and the intersection of specific parameters.
Revised:  Based on the traditional Bayesian neural network framework, the ResNet18 _BNN network constructed in this paper has been improved in the following three aspects: (1) A new objective function is proposed, which is composed of the KL loss of uncertain parameters and the intersection of specific parameters.
Edit intent: fluency
Original:  According to experimental analysis, our method achieves an accuracy of 98.28\% on the evaluation setof the Aff-Wild2 database . Compared with the traditional Bayesian Neural Network , our method brings the highest classification accuracy gain.
Revised:  Through testing on the FER2013 test set, we achieved 71.5\% and 73.1\% accuracy in PublicTestSet and PrivateTestSet, respectively . Compared with the traditional Bayesian Neural Network , our method brings the highest classification accuracy gain.
Edit intent: meaning-changed
Original:  According to experimental analysis, our method achieves an accuracy of 98.28\% on the evaluation setof the Aff-Wild2 database . Compared with the traditional Bayesian Neural Network , our method brings the highest classification accuracy gain.
Revised:  According to experimental analysis, our method achieves an accuracy of 98.28\% on the evaluation setof the Aff-Wild2 database . Compared with traditional Bayesian neural networks , our method brings the highest classification accuracy gain.
Edit intent: fluency