wanyu/IteraTeR_v2 huggingface dataset:
Original: When deploying artificial intelligence (AI) in the real world , being able to trust the operation of the AI by characterizing how it performs is an ever-present and important topic.
Revised: Ensuring trusted artificial intelligence (AI) in the real world , being able to trust the operation of the AI by characterizing how it performs is an ever-present and important topic.
Edit intent: clarity
Original:  An important and still largely unexplored task in this characterization is determining major factors within the real world that affect the AI's behavior , such as weather conditions or lighting, and either a ) being able to give justification for why it may have failed or b)eliminating the influence the factor has.
Revised:  An important and still largely unexplored task is the determination of the major factors within the real world that affect the AI's behavior , such as weather conditions or lighting, and either a ) being able to give justification for why it may have failed or b)eliminating the influence the factor has.
Edit intent: clarity
Original:  Determining these sensitive factors heavily relies on collected data that is diverse enough to cover numerous combinations of these factors, which becomes more onerous when having many potential sensitive factors or operating in complex environments.
Revised:  Determining these sensitive factors heavily relies on the availability of data that is diverse enough to cover numerous combinations of these factors, which becomes more onerous when having many potential sensitive factors or operating in complex environments.
Edit intent: clarity
Original:  Determining these sensitive factors heavily relies on collected data that is diverse enough to cover numerous combinations of these factors, which becomes more onerous when having many potential sensitive factors or operating in complex environments.
Revised:  Determining these sensitive factors heavily relies on collected data that is diverse enough to cover numerous combinations of these factors, but the exhaustive collection of this data is onerous and sometimes impossible in complex environments.
Edit intent: clarity
Original:  This paper investigates methods that discover and separate out individual semantic sensitive factors from a given dataset to conduct this characterization as well as addressing mitigation of these factors' sensitivity .
Revised:  This paper investigates methods that discover and mitigate the effects of semantic sensitive factors from a given dataset to conduct this characterization as well as addressing mitigation of these factors' sensitivity .
Edit intent: clarity
Original:  This paper investigates methods that discover and separate out individual semantic sensitive factors from a given dataset to conduct this characterization as well as addressing mitigation of these factors' sensitivity .
Revised:  This paper investigates methods that discover and separate out individual semantic sensitive factors within a given dataset to conduct this characterization as well as addressing mitigation of these factors' sensitivity .
Edit intent: clarity
Original:  This paper investigates methods that discover and separate out individual semantic sensitive factors from a given dataset to conduct this characterization as well as addressing mitigation of these factors' sensitivity .
Revised:  This paper investigates methods that discover and separate out individual semantic sensitive factors from a given dataset .
Edit intent: coherence
Original:  We also broaden remediation of fairness, which normally only addresses socially relevant factors, and widen it to deal with  the desensitization of AI  with regard to all possible aspects of variation in the domain.
Revised:  We also broaden remediation of fairness, which normally only addresses socially relevant factors, and widen it to deal with the desensitization of AI systems with regard to all possible aspects of variation in the domain.
Edit intent: clarity
Original:  In experiments using the road sign (GTSRB) and facial imagery (CelebA) datasets, we show the promise of using this scheme to perform this characterization and remediation and demonstrate that our approach outperforms state of the art approaches.
Revised:  In experiments using road sign (GTSRB) and facial imagery (CelebA) datasets, we show the promise of using this scheme to perform this characterization and remediation and demonstrate that our approach outperforms state of the art approaches.
Edit intent: clarity
Original:  In experiments using the road sign (GTSRB) and facial imagery (CelebA) datasets, we show the promise of using this scheme to perform this characterization and remediation and demonstrate that our approach outperforms state of the art approaches.
Revised:  In experiments using the road sign (GTSRB) and facial imagery (CelebA) datasets, we show the promise of these new methods and show that they outperform state of the art approaches.
Edit intent: clarity
Original: The task of manipulating the level and/or effects of individual instruments to recompose a mixture of recording , or remixing, is common across a variety of applications such as music production, audio-visual post-production, podcasts, and more.
Revised: The task of manipulating the level and/or effects of individual instruments to recompose a mixture of recordings , or remixing, is common across a variety of applications such as music production, audio-visual post-production, podcasts, and more.
Edit intent: fluency
Original:  In this work, we seek to learn to remix music directly  .
Revised:  In this work, we learn to remix music directly .
Edit intent: clarity
Original:  We evaluate our methods using the Slakh and MUSDB datasets and report both source separation performance and the remixing quality .
Revised:  We evaluate our methods using the Slakh and MUSDB datasets and report remixing performance as well as the impact on source separation as a byproduct .
Edit intent: clarity
Original:  Our results suggest  learning-to-remix significantly outperforms a strong separation baseline , is particularly useful for small changes, and can provide interactive user-controls .
Revised:  Our results suggest that learning-to-remix significantly outperforms a strong separation baseline , is particularly useful for small changes, and can provide interactive user-controls .
Edit intent: fluency
Original:  Our results suggest  learning-to-remix significantly outperforms a strong separation baseline , is particularly useful for small changes, and can provide interactive user-controls .
Revised:  Our results suggest learning-to-remix significantly outperforms a strong separation baseline and is particularly useful for small changes, and can provide interactive user-controls .
Edit intent: fluency
Original:  Our results suggest  learning-to-remix significantly outperforms a strong separation baseline , is particularly useful for small changes, and can provide interactive user-controls .
Revised:  Our results suggest learning-to-remix significantly outperforms a strong separation baseline , is particularly useful for small volume changes .
Edit intent: clarity
Original:  To study the optimal allocation of limited testing and vaccination resources in a heterogeneous contact network of interacting susceptible, recovered, and infected individuals, we present a degree-based testing and vaccination model for which we use control-theoretic methods to derive optimal testing and vaccination policies using control-theoretic methods .
Revised:  To study the optimal allocation of limited testing and vaccination resources in a heterogeneous contact network of interacting susceptible, recovered, and infected individuals, we present a degree-based testing and vaccination model for which we use control-theoretic methods to derive optimal testing and vaccination policies .
Edit intent: coherence
Original:  With supervision from the ground truth created by semantic labels , the network is embedded with contextual information so that it could understand the scene better , utilizing dependent features to make accurate prediction.
Revised:  With supervision from the ground truth created by semantic label , the network is embedded with contextual information so that it could understand the scene better , utilizing dependent features to make accurate prediction.
Edit intent: fluency
Original:  With supervision from the ground truth created by semantic labels , the network is embedded with contextual information so that it could understand the scene better , utilizing dependent features to make accurate prediction.
Revised:  With supervision from the ground truth created by semantic labels , the network is embedded with contextual information so that it could understand scene better and utilize correlated features to make accurate prediction.
Edit intent: clarity
Original: Depth completion deals with the problem of recovering dense depth maps from sparse ones, where color images are often used to facilitate this completion .
Revised: Depth completion deals with the problem of recovering dense depth maps from sparse ones, where color images are often used to facilitate this task .
Edit intent: clarity
Original:  However, blurry image guidance and object structures in depth still impede the performance of  image guided frameworks.
Revised:  However, blurry guidance in image and unclear structure in depth still impede the performance of image guided frameworks.
Edit intent: clarity
Original:  However, blurry image guidance and object structures in depth still impede the performance of  image guided frameworks.
Revised:  However, blurry image guidance and object structures in depth still impede the performance of the image guided frameworks.
Edit intent: coherence
Original:  To tackle these problems , we explore a repetitive design in our image guided network to sufficiently and gradually recover depth values.
Revised:  To tackle these problems , we explore a repetitive design in our image guided network to gradually and sufficiently recover depth values.
Edit intent: fluency
Original:  Specifically, the repetition is embodied in a color image guidance branch and a depth generation branch.
Revised:  Specifically, the repetition is embodied in both the image guidance branch and a depth generation branch.
Edit intent: clarity
Original:  Specifically, the repetition is embodied in a color image guidance branch and a depth generation branch.
Revised:  Specifically, the repetition is embodied in a color image guidance branch and depth generation branch.
Edit intent: fluency
Original:  In the former branch, we design a repetitive hourglass network to extract higher-level image features of complex environments, which can provide powerful context guidance for depth prediction.
Revised:  In the former branch, we design a repetitive hourglass network to extract higher-level image features of complex environments, which can provide powerful contextual instruction for depth prediction.
Edit intent: clarity
Original:  In the latter branch, we design a repetitive guidance module based on dynamic convolution where the convolution factorization is applied to simultaneously reduce its complexity and progressively model high-frequency structures , e. g., boundaries.
Revised:  In the latter branch, we introduce a repetitive guidance module based on dynamic convolution where the convolution factorization is applied to simultaneously reduce its complexity and progressively model high-frequency structures , e. g., boundaries.
Edit intent: clarity
Original:  In the latter branch, we design a repetitive guidance module based on dynamic convolution where the convolution factorization is applied to simultaneously reduce its complexity and progressively model high-frequency structures , e. g., boundaries.
Revised:  In the latter branch, we design a repetitive guidance module based on dynamic convolution , in which an efficient convolution factorization is applied to simultaneously reduce its complexity and progressively model high-frequency structures , e. g., boundaries.
Edit intent: clarity
Original:  In the latter branch, we design a repetitive guidance module based on dynamic convolution where the convolution factorization is applied to simultaneously reduce its complexity and progressively model high-frequency structures , e. g., boundaries.
Revised:  In the latter branch, we design a repetitive guidance module based on dynamic convolution where the convolution factorization is proposed to simultaneously reduce its complexity and progressively model high-frequency structures , e. g., boundaries.
Edit intent: clarity
Original:  Extensive experiments show that our method achieves state-of-the-art result on the NYUv2 dataset and ranks 1st on the KITTI benchmark at the time of submission .
Revised:  Extensive experiments show that our method achieves state-of-the-art result on the NYUv2 dataset .
Edit intent: clarity
Original:  In this article, we consider a quasi-genetic algorithm and the continuation Newton method  for this problem.
Revised:  In this article, we consider the continuation Newton method for this problem.
Edit intent: coherence
Original:  Firstly, we use the continuation Newton method with the deflation technique to find critical points of the objective function as many as possible.
Revised:  Firstly, we use the continuation Newton method with the deflation technique to find the stationary points from several determined initial points as many as possible.
Edit intent: clarity
Original:  Then, we use those critical points as the initial evolutionary seeds of the quasi-genetic algorithm.
Revised:  Then, we use those found stationary points as the initial evolutionary seeds of the quasi-genetic algorithm.
Edit intent: clarity
Original:  After evolving into several generations such as twenty generations , we obtain a suboptimal point of the optimization problem.
Revised:  After it evolves into several generations such as twenty generations , we obtain a suboptimal point of the optimization problem.
Edit intent: clarity
Original:  After evolving into several generations such as twenty generations , we obtain a suboptimal point of the optimization problem.
Revised:  After evolving into several generations , we obtain a suboptimal point of the optimization problem.
Edit intent: clarity
Original:  Finally, we use  this suboptimal point as the initial point of the continuation Newton method to obtain the critical pointof the original objective function , and output the minimizer between this final critical point and the  suboptimal point of the quasi-genetic algorithm as the global minimum point of the original optimization problem .
Revised:  Finally, we use this suboptimal point as the initial point to obtain the critical pointof the original objective function , and output the minimizer between this final critical point and the suboptimal point of the quasi-genetic algorithm as the global minimum point of the original optimization problem .
Edit intent: clarity
Original:  Finally, we use  this suboptimal point as the initial point of the continuation Newton method to obtain the critical pointof the original objective function , and output the minimizer between this final critical point and the  suboptimal point of the quasi-genetic algorithm as the global minimum point of the original optimization problem .
Revised:  Finally, we use this suboptimal point as the initial point of the continuation Newton method to obtain the stationary point , and output the minimizer between this final critical point and the suboptimal point of the quasi-genetic algorithm as the global minimum point of the original optimization problem .
Edit intent: clarity
Original:  Finally, we use  this suboptimal point as the initial point of the continuation Newton method to obtain the critical pointof the original objective function , and output the minimizer between this final critical point and the  suboptimal point of the quasi-genetic algorithm as the global minimum point of the original optimization problem .
Revised:  Finally, we use this suboptimal point as the initial point of the continuation Newton method to obtain the critical pointof the original objective function , and output the minimizer between this final stationary point and the suboptimal point of the quasi-genetic algorithm as the global minimum point of the original optimization problem .
Edit intent: fluency
Original:  Finally, we use  this suboptimal point as the initial point of the continuation Newton method to obtain the critical pointof the original objective function , and output the minimizer between this final critical point and the  suboptimal point of the quasi-genetic algorithm as the global minimum point of the original optimization problem .
Revised:  Finally, we use this suboptimal point as the initial point of the continuation Newton method to obtain the critical pointof the original objective function , and output the minimizer between this final critical point and the suboptimal point of the quasi-genetic algorithm .
Edit intent: clarity
Original:  Numerical results show that the proposed method is quite reliable to find the global optimal point of the unconstrained optimization problem ,compared to the multi-start method (the built-in subroutine GlobalSearch.m of the MATLAB R2020a environment) .
Revised:  Numerical results show that the proposed method performs well for the global optimization problems ,compared to the multi-start method (the built-in subroutine GlobalSearch.m of the MATLAB R2020a environment) .
Edit intent: clarity
Original:  This is first-order logic on graphs which is allowed to use the edge relation, and for every n \in \{0,1,\ldots \} a relation of arity n+2 which says that `` vertex s can be connected to vertex t by a path that avoids vertices v_1,\ldots,v_n '' . 2. Star-free graph expressions.
Revised:  This is first-order logic on graphs which is allowed to use the edge relation, and for every n \in \{0,1,\ldots \} a relation of arity n+2 which says that " vertex s can be connected to vertex t by a path that avoids vertices v_1,\ldots,v_n '' . 2. Star-free graph expressions.
Edit intent: fluency
Original:  This is first-order logic on graphs which is allowed to use the edge relation, and for every n \in \{0,1,\ldots \} a relation of arity n+2 which says that `` vertex s can be connected to vertex t by a path that avoids vertices v_1,\ldots,v_n '' . 2. Star-free graph expressions.
Revised:  This is first-order logic on graphs which is allowed to use the edge relation, and for every n \in \{0,1,\ldots \} a relation of arity n+2 which says that `` vertex s can be connected to vertex t by a path that avoids vertices v_1,\ldots,v_n " . 2. Star-free graph expressions.
Edit intent: fluency
Original: We study a risk-averse optimal control problem with a finite-horizon Borel model, where the cost is assessed via exponential utility.
Revised: We study a risk-averse optimal control problem for a finite-horizon Borel model, where the cost is assessed via exponential utility.
Edit intent: clarity
Original: We study a risk-averse optimal control problem with a finite-horizon Borel model, where the cost is assessed via exponential utility.
Revised: We study a risk-averse optimal control problem with a finite-horizon Borel model, where a cumulative cost is assessed via exponential utility.
Edit intent: clarity
Original:  In three dimensional test cases, the generated fused kernels are found to reduce total runtime by {\sim}25\\%DIF < , and, when compared to the standard ACM formulation, %DIF > , and, when compared to the standard ACM formulation, simulations demonstrate that a speedup of 2.3 times can be achieved.
Revised:  In three dimensional test cases, the generated fused kernels are found to reduce total runtime by {\sim}25\\%DIF < , and, %DIF > , and, when compared to the standard ACM formulation, simulations demonstrate that a speedup of 2.3 times can be achieved.
Edit intent: fluency
Original:  Understanding students' motivations for pursuing a STEM major, and the roles gender, ethnicity , parental education attainment, and socio-economic background play in shaping students' motivations can support the design of more effective recruitment efforts towards these groups.
Revised:  Understanding students' motivations for pursuing a STEM major, and the roles gender, nationality , parental education attainment, and socio-economic background play in shaping students' motivations can support the design of more effective recruitment efforts towards these groups.
Edit intent: clarity
Original:  In this paper, we propose and develop a novel text mining approach incorporating the Latent Dirichlet Allocation and word embeddings to extract and analyze applicants' motivational factors to choosing an engineering program.
Revised:  In this paper, we propose and develop a novel text mining approach incorporating the Latent Dirichlet Allocation and word embeddings to analyze applicants' motivational factors to choosing an engineering program.
Edit intent: clarity
Original:  We find that interest in technology and the desire to make social impacts are the two most powerful motivators for applicants.
Revised:  We find that interest in technology and the desire to make social impact are the two most powerful motivators for applicants.
Edit intent: fluency
Original:  Additionally, while we find significant motivational differences related to applicants' nationality and family socioeconomic status, gender differences are isolated from the effects of these factors.
Revised:  Additionally, while we find significant motivational differences related to applicants' nationality and family socio-economic status, gender differences are isolated from the effects of these factors.
Edit intent: fluency
Original:  In this paper, we propose and develop a novel text mining approach incorporating the Latent Dirichlet Allocation and word embeddings to analyze applicants' motivational factors to choosing an engineering program.
Revised:  In this paper, we propose and develop a novel text mining approach incorporating the Latent Dirichlet Allocation and word embeddings to analyze applicants' motivational factors for choosing an engineering program.
Edit intent: fluency
Original:  We then investigate the relationship between applicants' gender, nationality, family income, and educational attainment, and their stated motivations for applying to their engineering program of choice.
Revised:  We then investigate the relationship between applicants' gender, nationality, and family income and educational attainment, and their stated motivations for applying to their engineering program of choice.
Edit intent: coherence
Original:  Additionally, while we find significant motivational differences related to applicants' nationality and family socio-economic status, gender differences are isolated from the effects of these factors .
Revised:  Additionally, while we find significant motivational differences related to applicants' nationality and family socio-economic status, gender has the strongest and the most robust impact on students' motivations for studying engineering .
Edit intent: clarity
Original: A core objective of the TERRA-REF project was to generate an open-access reference dataset for the study of evaluation of sensing technology to study plants under field conditions.
Revised: A core objective of the TERRA-REF project was to generate an open-access reference dataset for the evaluation of sensing technology to study plants under field conditions.
Edit intent: clarity
Original: A core objective of the TERRA-REF project was to generate an open-access reference dataset for the study of evaluation of sensing technology to study plants under field conditions.
Revised: A core objective of the TERRA-REF project was to generate an open-access reference dataset for the study of evaluation of sensing technologies to study plants under field conditions.
Edit intent: fluency