wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Inspired by the structural balance theory, in this article, we innovatively propose a non-backtracking operator for signed networks . Besides, we theoretically derive a detectability threshold and prove the feasibility of the non-backtracking operator in community detection.
Revised:  Inspired by the structural balance theory, in this article, we innovatively propose a non-backtracking operator for signed networks , and theoretically derive a detectability threshold and prove the feasibility of the non-backtracking operator in community detection.
Edit intent: coherence
Original:   Simulation results demonstrate that the  non-backtracking matrix-based approach significantly outperforms the adjacency matrix-based algorithm, and shows great potential to detect communities with or without overlap.
Revised:  Furthermore, we improve the operator by considering the balanced paths in the network (denoted as balanced non-backtracking operator). Simulation results demonstrate that the non-backtracking matrix-based approach significantly outperforms the adjacency matrix-based algorithm, and shows great potential to detect communities with or without overlap.
Edit intent: meaning-changed
Original:   Simulation results demonstrate that the  non-backtracking matrix-based approach significantly outperforms the adjacency matrix-based algorithm, and shows great potential to detect communities with or without overlap.
Revised:  Simulation results demonstrate that the non-backtracking matrix-based approach significantly outperforms the adjacency matrix-based and the signed non-backtracking matrix-based algorithm. It shows great potential to detect communities with or without overlap.
Edit intent: meaning-changed
Original: This paperpresents a novel framework for valuation and hedging of the insurer's net liability on a Guaranteed Minimum Maturity Benefit (GMMB) embedded in variable annuity (VA) contracts whose underlying mutual fund dynamics evolve under the influence of the regime-switching model.
Revised: In this paper, we adopted a net liability model which assesses both market risk on the liability side and revenue risk on the asset side for a Guaranteed Minimum Maturity Benefit (GMMB) embedded in variable annuity (VA) contracts whose underlying mutual fund dynamics evolve under the influence of the regime-switching model.
Edit intent: meaning-changed
Original: This paperpresents a novel framework for valuation and hedging of the insurer's net liability on a Guaranteed Minimum Maturity Benefit (GMMB) embedded in variable annuity (VA) contracts whose underlying mutual fund dynamics evolve under the influence of the regime-switching model. Numerical solutions for valuations and Greeks (i.e. valuation sensitivities with respect to model parameters) of GMMB under stochastic mortality are derived. Valuation and hedging is performed using an accurate , fast and efficient Fourier Space Time-stepping (FST) algorithm.
Revised: This paperpresents a novel framework for valuation and hedging of the insurer's net liability on a Guaranteed Minimum Maturity Benefit (GMMB) embedded in variable annuity (VA) contracts . Numeric solutions for net liabilities, fair rate of fees and Greeks of GMMB are obtained by a more accurate and fast Fourier Space time-stepping (FST) algorithm.
Edit intent: clarity
Original:  The hedge effectiveness is assessed by comparing profit-and-loss performances for an unhedged and three statically hedged portfolios . The results provide a comprehensive analysis on valuation and hedging the longevity risk, interest rate risk and equity risk for the GMMB embedded in VAs, and highlight the benefits to insurance providers who offer those products .
Revised:  The hedge effectiveness is assessed by comparing profit-and-loss performances for an unhedged and three statically hedged portfolios are introduced, and their performances are assessed by comparing the short term and long term portfolio's volatility. Recently, we noticed FST algorithm can only be used to hedge the gross liability of GMMB and it leads to an incorrect result when applying FST algorithm to the net liability model. We have modified the method we used in the hedging part and obtained the reliable result now. They will be reported in near future .
Edit intent: meaning-changed
Original:  To obtain our results, we introduce new techniques and ideas, including a reduction to a new geometric line segment selection together with an efficient solution .
Revised:  Additionally, we show a lower bound proving that the query time is optimal for any solution using near-linear space. To achieve our bounds for random access in persistent strings we show how to reduce the problem to the following natural geometric selection problem on line segments. Consider a set of horizontal line segments in the plane. Given parameters i and j, a new geometric line segment selection together with an efficient solution .
Edit intent: meaning-changed
Original:  To obtain our results, we introduce new techniques and ideas, including a reduction to a new geometric line segment selection together with an efficient solution .
Revised:  To obtain our results, we introduce new techniques and ideas, including a reduction to a segment selection query returns the jth smallest segment (the segment with the jth smallest y-coordinate) among the segments crossing the vertical line through x-coordinate i. The segment selection problem is to preprocess a set of horizontal line segments into a compact data structure that supports fast segment selection queries. We present a solution that uses O(n) space and support segment selection queries in O(\log n/ \log \log n) time, where n is the number of segments. Furthermore, we prove that that this query time is also optimal for any solution using near-linear space .
Edit intent: meaning-changed
Original: How to explicitly encode positional information into neural networks is an important problem in natural language processing. In the Transformer model , the positional information is simply encoded as embedding vectors, which are used in the input layer, or encoded as a bias term in the self-attention module.
Revised: How to explicitly encode positional information into neural networks is important in learning the representation of natural languages, such as BERT. Based on the Transformer architecture , the positional information is simply encoded as embedding vectors, which are used in the input layer, or encoded as a bias term in the self-attention module.
Edit intent: clarity
Original:  Little research has been done on understanding pricing and popularity in the market for commercial sex work. This study investigates the determinants of pricing and popularity in the market for commercial sexual services online by using data from the largest UK network of online sexual services, a platform that is the " industry-standard " for sex workers.
Revised:  This study investigated the determinants of pricing and popularity in the market for commercial sexual services online by using data from the largest UK network of online sexual services, a platform that is the " industry-standard " for sex workers.
Edit intent: clarity
Original:  This study investigates the determinants of pricing and popularity in the market for commercial sexual services online by using data from the largest UK network of online sexual services, a platform that is the " industry-standard " for sex workers.
Revised:  This study investigates the determinants of pricing and popularity in the market for commercial sexual services online by using data from the largest UK network of online sexual services, a platform that is the industry-standard " for sex workers.
Edit intent: fluency
Original:  This study investigates the determinants of pricing and popularity in the market for commercial sexual services online by using data from the largest UK network of online sexual services, a platform that is the " industry-standard " for sex workers.
Revised:  This study investigates the determinants of pricing and popularity in the market for commercial sexual services online by using data from the largest UK network of online sexual services, a platform that is the " industry-standard for sex workers.
Edit intent: fluency
Original:  A 30x  speedup over CPU-only implementations using non-CUDA-Aware MPI communications is demonstrated up to 1,536 NVIDIA V100 GPUs and parallel strong scalability is shown up to 6,144 NVIDIA V100 GPUs for a problem containing 345 billion unknowns.
Revised:  A 30x time-to-solution speedup over CPU-only implementations using non-CUDA-Aware MPI communications is demonstrated up to 1,536 NVIDIA V100 GPUs and parallel strong scalability is shown up to 6,144 NVIDIA V100 GPUs for a problem containing 345 billion unknowns.
Edit intent: meaning-changed
Original:  A comparison of CUDA-Aware MPI communication to non-GPU-direct communication is performed demonstrating an additional 24\% speedup on eight nodes composed of 32 NVIDIA V100 GPUs.
Revised:  A comparison of CUDA-Aware MPI communication to non-GPUDirect communication is performed demonstrating an additional 24\% speedup on eight nodes composed of 32 NVIDIA V100 GPUs.
Edit intent: fluency
Original:  However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text ( > 1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus.
Revised:  However, as our systematic examination reveals, it is still challenging for such models to generate coherent long passages of text ( e.g., 1000 tokens), especially when the models are fine-tuned to the target domain on a small corpus.
Edit intent: clarity
Original:  We conduct a comprehensive empirical study with a broad set of evaluation metrics, and show that our approach significantly improves upon the fine-tuned GPT-2 in terms of domain-specific quality and sample efficiency.
Revised:  We conduct a comprehensive empirical study with a broad set of evaluation metrics, and show that our approach significantly improves upon the fine-tuned large LMs and various planning-then-generation methods in terms of domain-specific quality and sample efficiency.
Edit intent: meaning-changed
Original: Deep learning has significantly revolutionized the design of numerical algorithms for solving high-dimensional partial differential equations (PDEs) . Yet the empirical successes of such approaches remains mysterious in theory. In deep learning-based PDE solvers, solving the original PDE is formulated into an expectation minimization problemwith a PDE solution space discretized via deep neural networks .
Revised: Deep learning has significantly revolutionized the design of numerical algorithms for solving high-dimensional partial differential equations (PDEs) can be formulated into a least-squares minimization problem, where neural networks are used to parametrize PDE solutions .
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Original:  Typically, gradient descent-based methods are applied to minimize the expectation. This papershows that gradient descent can identify a global minimizer of the optimization problem with a well-controlled generalization error in the case of two-layer neural networks in the over-parameterization regime (i. e., the network width is sufficiently large).
Revised:  In this paper, we show that the gradient descent method can identify a global minimizer of the optimization problem with a well-controlled generalization error in the case of two-layer neural networks in the over-parameterization regime (i. e., the network width is sufficiently large).
Edit intent: clarity
Original:  This papershows that gradient descent can identify a global minimizer of the optimization problem with a well-controlled generalization error in the case of two-layer neural networks in the over-parameterization regime (i. e., the network width is sufficiently large).
Revised:  This papershows that gradient descent can identify a global minimizer of the least-squares optimization for solving second-order linear PDEs with two-layer neural networks in the over-parameterization regime (i. e., the network width is sufficiently large).
Edit intent: meaning-changed
Original:  This papershows that gradient descent can identify a global minimizer of the optimization problem with a well-controlled generalization error in the case of two-layer neural networks in the over-parameterization regime (i. e., the network width is sufficiently large). The generalization error of the gradient descent solution does not suffer from the curse of dimensonality if the solution is in a Barron-type space .
Revised:  This papershows that gradient descent can identify a global minimizer of the optimization problem with a well-controlled generalization error in the case of two-layer neural networks under the assumption of over-parametrization. We also analyze the generalization error of the gradient descent solution does not suffer from the curse of dimensonality if the solution is in a Barron-type space .
Edit intent: clarity
Original:  The generalization error of the gradient descent solution does not suffer from the curse of dimensonality if the solution is in a Barron-type space . The theories developed here could form a theoretical foundation of deep learning-based PDE solvers .
Revised:  The generalization error of the gradient descent solution does not suffer from the curse of dimensonality if the solution is in a Barron-type space and the least-squares optimization is regularized with a Barron-type norm, without the over-parametrization assumption .
Edit intent: meaning-changed
Original:  We also clarify which results change when moving from 1-0 binary utilities to the more general bi-valued  utilities.
Revised:  We also clarify which results change when moving from 1-0 binary utilities to the more general bi-valued utilities. Finally, we prove that the closely related Nash bargaining solution violates envy-freeness and strategyproofness even under 1-0 utilities.
Edit intent: meaning-changed
Original:  For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance n^{- \Theta (  \epsilon ^{-1/2 )  } must use at least 2^{n^c} \epsilon queries for some constant  c > 0, where n is the dimension and \epsilon is the accuracy parameter.
Revised:  For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance n^{- ( \epsilon ^{-1/2 ) } must use at least 2^{n^c} \epsilon queries for some constant c > 0, where n is the dimension and \epsilon is the accuracy parameter.
Edit intent: meaning-changed
Original:  For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance n^{- \Theta (  \epsilon ^{-1/2 )  } must use at least 2^{n^c} \epsilon queries for some constant  c > 0, where n is the dimension and \epsilon is the accuracy parameter.
Revised:  For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance n^{- \Theta ( 1/ \epsilon ^{-1/2 ) } must use at least 2^{n^c} \epsilon queries for some constant c > 0, where n is the dimension and \epsilon is the accuracy parameter.
Edit intent: meaning-changed
Original:  For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance n^{- \Theta (  \epsilon ^{-1/2 )  } must use at least 2^{n^c} \epsilon queries for some constant  c > 0, where n is the dimension and \epsilon is the accuracy parameter.
Revised:  For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance n^{- \Theta ( \epsilon ) } must use at least 2^{n^c} \epsilon queries for some constant c > 0, where n is the dimension and \epsilon is the accuracy parameter.
Edit intent: meaning-changed
Original:  For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance n^{- \Theta (  \epsilon ^{-1/2 )  } must use at least 2^{n^c} \epsilon queries for some constant  c > 0, where n is the dimension and \epsilon is the accuracy parameter.
Revised:  For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance n^{- \Theta ( \epsilon ^{-1/2 ) ^b } must use at least 2^{n^c} \epsilon queries for some constant c > 0, where n is the dimension and \epsilon is the accuracy parameter.
Edit intent: meaning-changed
Original:  For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance n^{- \Theta (  \epsilon ^{-1/2 )  } must use at least 2^{n^c} \epsilon queries for some constant  c > 0, where n is the dimension and \epsilon is the accuracy parameter.
Revised:  For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance n^{- \Theta ( \epsilon ^{-1/2 ) } must use at least 2^{n^c} \epsilon queries for some constant b, c > 0, where n is the dimension and \epsilon is the accuracy parameter.
Edit intent: meaning-changed
Original:  Our system leverages both supervised and unsupervised approaches to provide informative answers using DenSPI (Seo et al. , 2019) and BEST (Lee et al., 2016).
Revised:  Our system also leverages information retrieval (IR) approaches to provide informative answers using DenSPI (Seo et al. , 2019) and BEST (Lee et al., 2016).
Edit intent: meaning-changed
Original:  Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on facts about COVID-19 . We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19  but for future pandemics as well.
Revised:  Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on information from various sources, including the CDC and the WHO . We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19 but for future pandemics as well.
Edit intent: meaning-changed
Original:  Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on facts about COVID-19 . We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19  but for future pandemics as well.
Revised:  Evaluation of covidAsk is carried out by using a manually created dataset called COVID-19 Questions which is based on facts about COVID-19 . We hope our system will be able to aid researchers in their search for knowledge and information not only for COVID-19 , but for future pandemics as well.
Edit intent: fluency
Original: A mathematical model for the COVID-19 pandemic spread in England is presented. The model integrates age-structured Susceptible-Exposed-Infected-Removed dynamics with real mobile phone data accounting for the population mobility  .
Revised: A mathematical model for the COVID-19 pandemic spread , which integrates age-structured Susceptible-Exposed-Infected-Removed dynamics with real mobile phone data accounting for the population mobility .
Edit intent: coherence
Original:  Through an extensive computational validation of the methodology, it is shown that it is possible to compute robust exit strategies with realistic reduced mobility values to inform public policy making .
Revised:  Through an extensive computational validation of the methodology, it is shown that it is possible to compute robust exit strategies with realistic reduced mobility values to inform public policy making , and we exemplify the applicability of the methodology using datasets from England and France. Code implementing the described experiments is available at URL
Edit intent: meaning-changed
Original:  We model the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy .
Revised:  We simulate the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy .
Edit intent: clarity
Original:  We model the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy .
Revised:  We model the impact of these distributed consent models on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy .
Edit intent: clarity
Original:  We model the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy .
Revised:  We model the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of networks to preserve their connectivity and privacy .
Edit intent: clarity
Original:  We model the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy . Distributed consent is of course not a silver bullet, since it does not follow data as it flows in and out of the system, but it is one of the most straightforward non-traditional models to implement and it better accommodates the fuzzy, distributed nature of online data .
Revised:  We model the impact of distributed consent on the observability of social networks and find that relatively low adoption of even the simplest formulation of distributed consent would allow macroscopic subsets of online networks to preserve their connectivity and privacy .
Edit intent: coherence
Original:  Last, we evaluate the backdoor attack and the results show the excellent performance of with 100 %DIFDELCMD < \\%%% %DIF < backdoor attack rate and sacrificing of 0.71\\% on text classification text.abstract 
Revised:  Last, we evaluate the backdoor attack and the results show the excellent performance of with 100 %DIFDELCMD < \\%%% %DIF < backdoor attack rate and sacrificing of 0.71\\% on text classification text.abstract \% backdoor attack rate and sacrificing of 0.71\% on text classification text.
Edit intent: meaning-changed
Original: Deep learning has been widely adopted in natural language processing applicationsin recent years. Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em   However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Revised: Recently, advanced NLP models have seen a surge in the usage of various applications . {\em However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Edit intent: clarity