wanyu/IteraTeR_full_sent huggingface dataset:
Original:  By utilizing an Anti-Brownian electrokinetic trap (ABEL trap), single-molecule F\"orster resonance energy transfer (smFRET) -based subunit rotation monitoring was prolonged from milliseconds to seconds . The extended observation times for single proteoliposomes in solution allowed to observe fluctuating rotation rates of individual enzymes and to map the broad distributions of ATP-dependent calatytic rates in FoF1-ATP synthase .
Revised:  By utilizing an Anti-Brownian electrokinetic trap (ABEL trap), single-molecule F\"orster resonance energy transfer (smFRET) -based subunit rotation monitoring was prolonged from milliseconds to seconds by utilizing an Anti-Brownian electrokinetic trap (ABEL trap). These extended observation times for single proteoliposomes in solution allowed to observe fluctuating rotation rates of individual enzymes and to map the broad distributions of ATP-dependent calatytic rates in FoF1-ATP synthase .
Edit intent: coherence
Original:  The extended observation times for single proteoliposomes in solution allowed to observe fluctuating rotation rates of individual enzymes and to map the broad distributions of ATP-dependent calatytic rates in FoF1-ATP synthase .
Revised:  The extended observation times allowed us to observe fluctuating rotation rates of individual enzymes and to map the broad distributions of ATP-dependent calatytic rates in FoF1-ATP synthase .
Edit intent: clarity
Original:  The extended observation times for single proteoliposomes in solution allowed to observe fluctuating rotation rates of individual enzymes and to map the broad distributions of ATP-dependent calatytic rates in FoF1-ATP synthase .
Revised:  The extended observation times for single proteoliposomes in solution allowed to observe fluctuating rates of functional rotation for individual FoF1-liposomes in solution. Broad distributions of ATP-dependent calatytic rates in FoF1-ATP synthase .
Edit intent: clarity
Original:  The extended observation times for single proteoliposomes in solution allowed to observe fluctuating rotation rates of individual enzymes and to map the broad distributions of ATP-dependent calatytic rates in FoF1-ATP synthase .
Revised:  The extended observation times for single proteoliposomes in solution allowed to observe fluctuating rotation rates of individual enzymes and to map the broad distributions of ATP-dependent catalytic rates were revealed .
Edit intent: clarity
Original:  In the presence of ionophores and uncouplers the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second , i. e. much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previoulsy for isolated single F1 fragments without coupling to the membrane-embedded Fo domain of the enzyme .
Revised:  In the presence of ionophores or uncouplers, the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second , i. e. much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previoulsy for isolated single F1 fragments without coupling to the membrane-embedded Fo domain of the enzyme .
Edit intent: fluency
Original:  In the presence of ionophores and uncouplers the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second , i. e. much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previoulsy for isolated single F1 fragments without coupling to the membrane-embedded Fo domain of the enzyme .
Revised:  In the presence of ionophores and uncouplers the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second . This was much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previoulsy for isolated single F1 fragments without coupling to the membrane-embedded Fo domain of the enzyme .
Edit intent: coherence
Original:  In the presence of ionophores and uncouplers the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second , i. e. much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previoulsy for isolated single F1 fragments without coupling to the membrane-embedded Fo domain of the enzyme .
Revised:  In the presence of ionophores and uncouplers the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second , i. e. much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previously for isolated single F1 fragments without coupling to the membrane-embedded Fo domain of the enzyme .
Edit intent: fluency
Original:  In the presence of ionophores and uncouplers the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second , i. e. much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previoulsy for isolated single F1 fragments without coupling to the membrane-embedded Fo domain of the enzyme .
Revised:  In the presence of ionophores and uncouplers the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second , i. e. much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previoulsy for isolated single F1 complexes uncoupled from the membrane-embedded Fo domain of the enzyme .
Edit intent: clarity
Original:  In the presence of ionophores and uncouplers the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second , i. e. much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previoulsy for isolated single F1 fragments without coupling to the membrane-embedded Fo domain of the enzyme .
Revised:  In the presence of ionophores and uncouplers the fastest subunit rotation speeds measured in single reconstituted FoF1-ATP synthases were 180 full rounds per second , i. e. much faster than measured by biochemical ensemble averaging, but not as fast as the maximum rotational speed reported previoulsy for isolated single F1 fragments without coupling to the membrane-embedded Fo complex. Further application of ABEL trap measurements should help resolve the mechanistic causes of such fluctuating rates of subunit rotation .
Edit intent: meaning-changed
Original:  However, the mismatch between training and real-world data causes a shift of speaker embedding space and severely degrades the recognition performance . Various complicated neural architectures are presented to address speaker recognition in the wild but neglect storage and computation requirements.
Revised:  The mismatch between development and real-world data causes a shift of speaker embedding space and severely degrades the recognition performance . Various complicated neural architectures are presented to address speaker recognition in the wild but neglect storage and computation requirements.
Edit intent: coherence
Original:  However, the mismatch between training and real-world data causes a shift of speaker embedding space and severely degrades the recognition performance . Various complicated neural architectures are presented to address speaker recognition in the wild but neglect storage and computation requirements.
Revised:  However, the mismatch between training and real-world data causes a shift of speaker embedding space and severely degrades the performance of speaker recognition. Extensive efforts have been devoted to address speaker recognition in the wild but neglect storage and computation requirements.
Edit intent: clarity
Original:  However, the mismatch between training and real-world data causes a shift of speaker embedding space and severely degrades the recognition performance . Various complicated neural architectures are presented to address speaker recognition in the wild but neglect storage and computation requirements. To address this issue , we propose a neural architecture search-based efficient time-delay neural network (EfficientTDNN)  to improve inference efficiency while maintaining recognition accuracy.
Revised:  However, the mismatch between training and real-world data causes a shift of speaker embedding space and severely degrades the recognition performance . Various complicated neural architectures are presented to address speaker recognition in the wild , but these often neglect computation and storage requirements. In this work , we propose a neural architecture search-based efficient time-delay neural network (EfficientTDNN) to improve inference efficiency while maintaining recognition accuracy.
Edit intent: clarity
Original:  The proposed EfficientTDNN contains three phases . First, supernet designconstructs a dynamic neural architecture that consists of sequential cells and enables network pruning. Second , progressive training optimizes randomly sampled subnets that inherit the weights of the supernet .
Revised:  The proposed EfficientTDNN contains three phases : supernet design , progressive training optimizes randomly sampled subnets that inherit the weights of the supernet .
Edit intent: coherence
Original:  Second , progressive training optimizes randomly sampled subnets that inherit the weights of the supernet . Third, three search methods, including manual grid search, random search, and model predictive evolutionary search, are introduced to find a trade-off between accuracy and efficiency.
Revised:  Second , progressive training , and architecture search. Firstly, we borrow the design of TDNN to construct a supernet that enables sampling subnets with different depth, kernel, and efficiency.
Edit intent: coherence
Original:  Third, three search methods, including manual grid search, random search, and model predictive evolutionary search, are introduced to find a trade-off between accuracy and efficiency. Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
Revised:  Third, three search methods, including manual grid search, random search, and model predictive evolutionary search, are introduced to find a trade-off between accuracy and width. Secondly, the supernet is progressively trained with multi-condition data augmentation to mitigate interference between subnets and overcome the challenge of optimizing a huge search space. Thirdly, an accuracy predictor and efficiency estimator are proposed to use in the architecture search to derive the specialized subnet under the given efficiency constraints. Experimental results on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
Edit intent: meaning-changed
Original:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
Revised:  Results of experiments on the VoxCeleb dataset show EfficientTDNN achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
Edit intent: clarity
Original:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
Revised:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% equal error rate (EER) and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
Edit intent: meaning-changed
Original:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
Revised:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 detection cost function (DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
Edit intent: meaning-changed
Original:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
Revised:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13 achieves 1.55\% EER and 0.138 DCF_{0.01 with 565M multiply-accumulate operations (MACs) as well as 0.96\% EER and 0.108 DCF_{0.01} with 1.46G MACs.
Edit intent: meaning-changed
Original:  The paper estimates the effects of being excepted from amnesty on elites' future wealth and political power using a regression discontinuity design  . Results on a sample of delegates to Reconstruction conventions show that exclusion from amnesty substantially decreases the likelihood of holding political office .
Revised:  I estimate the effect of being excluded from amnesty on elites' future wealth and political power using a regression discontinuity design . Results on a sample of delegates to Reconstruction conventions show that exclusion from amnesty substantially decreases the likelihood of holding political office .
Edit intent: clarity
Original:  The paper estimates the effects of being excepted from amnesty on elites' future wealth and political power using a regression discontinuity design  . Results on a sample of delegates to Reconstruction conventions show that exclusion from amnesty substantially decreases the likelihood of holding political office .
Revised:  The paper estimates the effects of being excepted from amnesty on elites' future wealth and political power using a regression discontinuity design that compares individuals just above and below a wealth threshold that determined exclusion from amnesty . Results on a sample of delegates to Reconstruction conventions show that exclusion from amnesty substantially decreases the likelihood of holding political office .
Edit intent: meaning-changed
Original:  The paper estimates the effects of being excepted from amnesty on elites' future wealth and political power using a regression discontinuity design  . Results on a sample of delegates to Reconstruction conventions show that exclusion from amnesty substantially decreases the likelihood of holding political office .
Revised:  The paper estimates the effects of being excepted from amnesty on elites' future wealth and political power using a regression discontinuity design . Results on a sample of Reconstruction convention delegates show that exclusion from amnesty substantially decreases the likelihood of holding political office .
Edit intent: clarity
Original:  The paper estimates the effects of being excepted from amnesty on elites' future wealth and political power using a regression discontinuity design  . Results on a sample of delegates to Reconstruction conventions show that exclusion from amnesty substantially decreases the likelihood of holding political office .
Revised:  The paper estimates the effects of being excepted from amnesty on elites' future wealth and political power using a regression discontinuity design . Results on a sample of delegates to Reconstruction conventions show that exclusion from amnesty significantly decreased the likelihood of holding political office .
Edit intent: clarity
Original:  The paper estimates the effects of being excepted from amnesty on elites' future wealth and political power using a regression discontinuity design  . Results on a sample of delegates to Reconstruction conventions show that exclusion from amnesty substantially decreases the likelihood of holding political office .
Revised:  The paper estimates the effects of being excepted from amnesty on elites' future wealth and political power using a regression discontinuity design . Results on a sample of delegates to Reconstruction conventions show that exclusion from amnesty substantially decreases the likelihood of ex-post officeholding .
Edit intent: clarity
Original:  a binary classification (offensive versus non-offensive comments ), the level of the offense (highly offensive, moderately offensive and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism and fat phobia).
Revised:  a binary classification (offensive versus non-offensive language ), the level of the offense (highly offensive, moderately offensive and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism and fat phobia).
Edit intent: clarity
Original:  a binary classification (offensive versus non-offensive comments ), the level of the offense (highly offensive, moderately offensive and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism and fat phobia).
Revised:  a binary classification (offensive versus non-offensive comments ), the level of offense (highly offensive, moderately offensive and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism and fat phobia).
Edit intent: fluency
Original: This paperdescribes a corpus annotation process  to support the identification of hate speech and offensive language in social media.
Revised: The understanding of an offense is subjective and people may have different opinions about the offensiveness of a comment. Also, offenses and hate speech may occur through sarcasm, which hides the real intention of the comment and makes the decision of the annotators more confusing. Therefore, provide a well-structured annotation process is crucial to a better understanding of hate speech and offensive language phenomena, as well as supply better performance for machine learning classifiers. In this paper, we describe a corpus annotation process to support the identification of hate speech and offensive language in social media.
Edit intent: coherence
Original:  a binary classification (offensive versus non-offensive language), the level of offense (highly offensive, moderately offensive  and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism  and fat phobia).
Revised:  a binary classification (offensive versus non-offensive language), the level of offense (highly offensive, moderately offensive , and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism and fat phobia).
Edit intent: fluency
Original:  a binary classification (offensive versus non-offensive language), the level of offense (highly offensive, moderately offensive  and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism  and fat phobia).
Revised:  a binary classification (offensive versus non-offensive language), the level of offense (highly offensive, moderately offensive and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religious intolerance, partyism, apology to the dictatorship, antisemitism and fat phobia).
Edit intent: fluency
Original:  a binary classification (offensive versus non-offensive language), the level of offense (highly offensive, moderately offensive  and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism  and fat phobia).
Revised:  a binary classification (offensive versus non-offensive language), the level of offense (highly offensive, moderately offensive and slightly offensive messages), and the identification regarding the target of the discriminatory content (xenophobia, racism, homophobia, sexism, religion intolerance, partyism, apology to the dictatorship, antisemitism , and fat phobia).
Edit intent: fluency
Original:  We show that the core of the cellular autocatalytic network is the transcription-translation machinery which by itself is an autocatalytic network composed of several coupled autocatalytic cycles like ribosome, RNA-polymerase and tRNA charging cycles.
Revised:  We show that the core of the cellular autocatalytic network is the transcription--translation machinery -- in itself an autocatalytic network composed of several coupled autocatalytic cycles like ribosome, RNA-polymerase and tRNA charging cycles.
Edit intent: coherence
Original:  We show that the core of the cellular autocatalytic network is the transcription-translation machinery which by itself is an autocatalytic network composed of several coupled autocatalytic cycles like ribosome, RNA-polymerase and tRNA charging cycles.
Revised:  We show that the core of the cellular autocatalytic network is the transcription-translation machinery which by itself is an autocatalytic network comprising several coupled autocatalytic cycles like ribosome, RNA-polymerase and tRNA charging cycles.
Edit intent: fluency
Original:  We show that the core of the cellular autocatalytic network is the transcription-translation machinery which by itself is an autocatalytic network composed of several coupled autocatalytic cycles like ribosome, RNA-polymerase and tRNA charging cycles.
Revised:  We show that the core of the cellular autocatalytic network is the transcription-translation machinery which by itself is an autocatalytic network composed of several coupled autocatalytic cycles , including the ribosome, RNA polymerase, and tRNA charging cycles.
Edit intent: clarity
Original:  We derive two kinds of growth laws per autocatalytic cycle,  relating growth rate to the relative fraction of the catalyst and its catalysis rate and relating growth rate to all the time-scales in the cycle.
Revised:  We derive two kinds of growth laws per autocatalytic cycle, one relating growth rate to the relative fraction of the catalyst and its catalysis rate and relating growth rate to all the time-scales in the cycle.
Edit intent: meaning-changed
Original:  We derive two kinds of growth laws per autocatalytic cycle,  relating growth rate to the relative fraction of the catalyst and its catalysis rate and relating growth rate to all the time-scales in the cycle.
Revised:  We derive two kinds of growth laws per autocatalytic cycle, relating growth rate to the relative fraction of the catalyst and its catalysis rate , and the other relating growth rate to all the time-scales in the cycle.
Edit intent: coherence