wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our experimental evaluation on a large set of real-world instances indicates that our strategies are able to improve the performance of the state-of-the-art branch-and-reduce algorithm by Akiba and Iwata .
Revised:  Our experimental evaluation on a large set of real-world instances indicate that our strategies are able to improve the performance of the state-of-the-art branch-and-reduce algorithm by Akiba and Iwata .
Edit intent: fluency
Original: The widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and fierce rivalry in motorsportprovide a testbed to accelerate technology development . A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or in the case of a racing car, the ideal racing line.
Revised: Widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and fierce rivalry in motorsportprovide a testbed to accelerate technology development . A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or in the case of a racing car, the ideal racing line.
Edit intent: fluency
Original: The widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and fierce rivalry in motorsportprovide a testbed to accelerate technology development . A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or in the case of a racing car, the ideal racing line.
Revised: The widespread development of driverless vehicles has led to the formation of autonomous racing , where technological development is accelerated by the high speeds and fierce rivalry in motorsportprovide a testbed to accelerate technology development . A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or in the case of a racing car, the ideal racing line.
Edit intent: clarity
Original: The widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and fierce rivalry in motorsportprovide a testbed to accelerate technology development . A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or in the case of a racing car, the ideal racing line.
Revised: The widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and competitive environment of motorsport . A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or in the case of a racing car, the ideal racing line.
Edit intent: clarity
Original: The widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and fierce rivalry in motorsportprovide a testbed to accelerate technology development . A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or in the case of a racing car, the ideal racing line.
Revised: The widespread development of driverless vehicles has led to the formation of autonomous racing competitions, where the high speeds and fierce rivalry in motorsportprovide a testbed to accelerate technology development . A particular challenge for an autonomous vehicle is that of identifying a target trajectory - or in the case of a racing car, the racing line.
Edit intent: clarity
Original:  Many existing approaches to identifying the racing line are either not the time-optimal solutions, or have solution times which are computationally expensive, thus rendering them unsuitable for real-time application using on-board processing hardware.
Revised:  Many existing approaches to finding the racing line are either not the time-optimal solutions, or have solution times which are computationally expensive, thus rendering them unsuitable for real-time application using on-board processing hardware.
Edit intent: clarity
Original:  Many existing approaches to identifying the racing line are either not the time-optimal solutions, or have solution times which are computationally expensive, thus rendering them unsuitable for real-time application using on-board processing hardware.
Revised:  Many existing approaches to identifying the racing line are either not time-optimal solutions, or have solution times which are computationally expensive, thus rendering them unsuitable for real-time application using on-board processing hardware.
Edit intent: clarity
Original:  The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control system.
Revised:  The network predicts the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control system.
Edit intent: clarity
Original:  The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control system.
Revised:  The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to autonomous vehicle control system.
Edit intent: clarity
Original:  The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control system. The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
Revised:  The network is capable of predicting the racing line with a mean absolute error of +/-0.27m, meaning that the accuracy outperforms a human driver, and is comparable to other parts of the autonomous vehicle control subsystems. The approach generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
Edit intent: clarity
Original:  The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
Revised:  The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal trajectory . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
Edit intent: clarity
Original:  The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
Revised:  The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
Edit intent: fluency
Original:  The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
Revised:  The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approaches to find near-optimal racing lines than traditional computational methods.
Edit intent: clarity
Original:  The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines than traditional computational methods.
Revised:  The system generates predictions within 33ms, making it over 9,000 times faster than traditional methods of finding the optimal racing line . Results suggest that a data-driven approach may therefore be favourable for real-time generation of near-optimal racing lines may be favourable to traditional computational methods.
Edit intent: coherence
Original: We explore the scaling properties of price-volatility nexus in cryptocurrency markets and address the issue of the asymmetric volatility effect within a framework of fractal analysis .
Revised: Asymmetric relationship between price and volatility is a prominent feature of the financial market time series. This paper explores the price-volatility nexus in cryptocurrency markets and address the issue of the asymmetric volatility effect within a framework of fractal analysis .
Edit intent: meaning-changed
Original: We explore the scaling properties of price-volatility nexus in cryptocurrency markets and address the issue of the asymmetric volatility effect within a framework of fractal analysis .
Revised: We explore the scaling properties of price-volatility nexus in cryptocurrency markets and address the issue of the asymmetric volatility effect between uptrend (bull) and downtrend (bear) regimes. The conventional GARCH-class models have shown that in cryptocurrency markets, asymmetric reactions of volatility to returns differ from those of other traditional financial assets. We address this issue from a viewpoint of fractal analysis .
Edit intent: meaning-changed
Original: We explore the scaling properties of price-volatility nexus in cryptocurrency markets and address the issue of the asymmetric volatility effect within a framework of fractal analysis . The MF-ADCCA method is applied to examine nonlinear interactions and asymmetric multifractality of cross-correlations between price and volatility for Bitcoin  , Ethereum, Ripple, and Litecoin .
Revised: We explore the scaling properties of price-volatility nexus in cryptocurrency markets and address the issue of the asymmetric volatility effect within a framework of fractal analysis , which can cover the nonlinear interactions and asymmetric multifractality of cross-correlations between price and volatility for Bitcoin , Ethereum, Ripple, and Litecoin .
Edit intent: coherence
Original:  The MF-ADCCA method is applied to examine nonlinear interactions and asymmetric multifractality of cross-correlations between price and volatility for Bitcoin  , Ethereum, Ripple, and Litecoin .
Revised:  The MF-ADCCA method is applied to examine nonlinear interactions and the self-similarity properties widely acknowledged in the field of econophysics. The asymmetric cross-correlations between price and volatility for Bitcoin , Ethereum, Ripple, and Litecoin .
Edit intent: meaning-changed
Original:  The MF-ADCCA method is applied to examine nonlinear interactions and asymmetric multifractality of cross-correlations between price and volatility for Bitcoin  , Ethereum, Ripple, and Litecoin .
Revised:  The MF-ADCCA method is applied to examine nonlinear interactions and asymmetric multifractality of cross-correlations between price and volatility for Bitcoin (BTC), Ethereum (ETH), Ripple (XRP) , Ethereum, Ripple, and Litecoin .
Edit intent: meaning-changed
Original:  The MF-ADCCA method is applied to examine nonlinear interactions and asymmetric multifractality of cross-correlations between price and volatility for Bitcoin  , Ethereum, Ripple, and Litecoin .
Revised:  The MF-ADCCA method is applied to examine nonlinear interactions and asymmetric multifractality of cross-correlations between price and volatility for Bitcoin , and Litecoin .
Edit intent: clarity
Original:  The MF-ADCCA method is applied to examine nonlinear interactions and asymmetric multifractality of cross-correlations between price and volatility for Bitcoin  , Ethereum, Ripple, and Litecoin . Furthermore, asymmetric reactions in the volatility process to price fluctuations between uptrend (bull) and downtrend (bear) regimes are discussed from the viewpoint of cross-correlations quantified by the asymmetric DCCA coefficient , which is a different approach from the conventional GARCH-class models .
Revised:  The MF-ADCCA method is applied to examine nonlinear interactions and asymmetric multifractality of cross-correlations between price and volatility for Bitcoin , Ethereum, Ripple, and Litecoin (LTC) during the period from June 1, 2016 to December 28, 2020 are investigated using the MF-ADCCA method and quantified via the viewpoint of cross-correlations quantified by the asymmetric DCCA coefficient , which is a different approach from the conventional GARCH-class models .
Edit intent: meaning-changed
Original:  Furthermore, asymmetric reactions in the volatility process to price fluctuations between uptrend (bull) and downtrend (bear) regimes are discussed from the viewpoint of cross-correlations quantified by the asymmetric DCCA coefficient , which is a different approach from the conventional GARCH-class models .
Revised:  Furthermore, asymmetric reactions in the volatility process to price fluctuations between uptrend (bull) and downtrend (bear) regimes are discussed from the asymmetric DCCA coefficient , which is a different approach from the conventional GARCH-class models .
Edit intent: clarity
Original:  Furthermore, asymmetric reactions in the volatility process to price fluctuations between uptrend (bull) and downtrend (bear) regimes are discussed from the viewpoint of cross-correlations quantified by the asymmetric DCCA coefficient , which is a different approach from the conventional GARCH-class models .
Revised:  Furthermore, asymmetric reactions in the volatility process to price fluctuations between uptrend (bull) and downtrend (bear) regimes are discussed from the viewpoint of cross-correlations quantified by the asymmetric DCCA coefficient . The approaches take into account the nonlinearity and asymmetric multifractal scaling properties, providing new insights in investigating the relationships in a dynamical way .
Edit intent: meaning-changed
Original:  In contrast, for Ripple and Litecoin , inverted reactions are present where cross-correlations are stronger in uptrend markets . Our empirical findings uncover the dynamics of asymmetric volatility structure and provide new guidance in investigating dynamical relationships between price and volatility for cryptocurrencies .
Revised:  In contrast, for Ripple and Litecoin , inverted reactions are present where cross-correlations are stronger in uptrend markets .
Edit intent: clarity
Original:  MSPM involves two asynchronously updated units:  Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM possesses its reusability to have connections to multiple SAMs. A SAM is responsible for the assets reallocation of a portfolio using profound information from the EAMs connected .
Revised:  MSPM involves two asynchronously updated units: an Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM possesses its reusability to have connections to multiple SAMs. A SAM is responsible for the assets reallocation of a portfolio using profound information from the EAMs connected .
Edit intent: fluency
Original:  MSPM involves two asynchronously updated units:  Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM possesses its reusability to have connections to multiple SAMs. A SAM is responsible for the assets reallocation of a portfolio using profound information from the EAMs connected .
Revised:  MSPM involves two asynchronously updated units: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM employs its reusability to have connections to multiple SAMs. A SAM is responsible for the assets reallocation of a portfolio using profound information from the EAMs connected .
Edit intent: clarity
Original:  MSPM involves two asynchronously updated units:  Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM possesses its reusability to have connections to multiple SAMs. A SAM is responsible for the assets reallocation of a portfolio using profound information from the EAMs connected .
Revised:  MSPM involves two asynchronously updated units: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM possesses its reusability to have connections to multiple SAMs. An SAM is responsible for the assets reallocation of a portfolio using profound information from the EAMs connected .
Edit intent: fluency
Original:  MSPM involves two asynchronously updated units:  Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM possesses its reusability to have connections to multiple SAMs. A SAM is responsible for the assets reallocation of a portfolio using profound information from the EAMs connected .
Revised:  MSPM involves two asynchronously updated units: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM possesses its reusability to have connections to multiple SAMs. A SAM is responsible for asset reallocation in a portfolio using profound information from the EAMs connected .
Edit intent: clarity
Original:  MSPM involves two asynchronously updated units:  Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM possesses its reusability to have connections to multiple SAMs. A SAM is responsible for the assets reallocation of a portfolio using profound information from the EAMs connected .
Revised:  MSPM involves two asynchronously updated units: Evolving Agent Module (EAM) and Strategic Agent Module (SAM). A self-sustained EAM produces signal-comprised information for a specific asset using heterogeneous data inputs, and each EAM possesses its reusability to have connections to multiple SAMs. A SAM is responsible for the assets reallocation of a portfolio using profound information from the connected EAMs .
Edit intent: clarity
Original:  We also tackle  data-shortage issue of newly-listed stocks by transfer learning, and validate the necessity of EAM . Experiments on 8-year U.S. stock markets data prove the effectiveness of MSPM in profits accumulation by its outperformance over existing benchmarks.
Revised:  We also tackle the data-shortage issue of newly-listed stocks by transfer learning, and validate the necessity of EAM . Experiments on 8-year U.S. stock markets data prove the effectiveness of MSPM in profits accumulation by its outperformance over existing benchmarks.
Edit intent: fluency
Original:  We also tackle  data-shortage issue of newly-listed stocks by transfer learning, and validate the necessity of EAM . Experiments on 8-year U.S. stock markets data prove the effectiveness of MSPM in profits accumulation by its outperformance over existing benchmarks.
Revised:  We also tackle data-shortage issue of newly-listed stocks by transfer learning, and validate the indispensability of EAM with four different portfolios . Experiments on 8-year U.S. stock markets data prove the effectiveness of MSPM in profits accumulation by its outperformance over existing benchmarks.
Edit intent: meaning-changed
Original:  We also tackle  data-shortage issue of newly-listed stocks by transfer learning, and validate the necessity of EAM . Experiments on 8-year U.S. stock markets data prove the effectiveness of MSPM in profits accumulation by its outperformance over existing benchmarks.
Revised:  We also tackle data-shortage issue of newly-listed stocks by transfer learning, and validate the necessity of EAM . Experiments on 8-year U.S. stock market data prove the effectiveness of MSPM in profits accumulation by its outperformance over existing benchmarks.
Edit intent: fluency
Original:  We also tackle  data-shortage issue of newly-listed stocks by transfer learning, and validate the necessity of EAM . Experiments on 8-year U.S. stock markets data prove the effectiveness of MSPM in profits accumulation by its outperformance over existing benchmarks.
Revised:  We also tackle data-shortage issue of newly-listed stocks by transfer learning, and validate the necessity of EAM . Experiments on 8-year U.S. stock markets data prove the effectiveness of MSPM in profit accumulation, by its outperformance over existing benchmarks.
Edit intent: fluency
Original:  To avoid the tedious and repetitive derivations for implementing an error-state Kalman filter for a certain system, this paperproposes a generic symbolic representation for error-state Kalman filters on manifolds. Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Revised:  In this paper, we propose a method to develop Kalman filters for such on-manifold systems. Utilizing \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Edit intent: clarity
Original:  Utilizing the \boxplus \backslash \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Revised:  Utilizing the \boxplus , \boxminus operations and further defining a \oplus operation on the respective manifold, we propose a canonical representation of the robotic system, which enables us to separate the manifold structures from the system descriptions in each step of the Kalman filter, ultimately leading to a generic , symbolic and manifold-embedding Kalman filter framework .
Edit intent: coherence