wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Through empirical experimentation, which involves long term forecasting in daily sampled cross-asset futures, and short term forecasting in minutely sampled cash currency pairs, we find that the online learning techniques outperform the offline learning ones. We also find that , in the subset of models we use , sequential learning in time with online Ridge regression, provides the best next step ahead forecasts, and continual learning with an online radial basis function network, provides the best multi-step ahead forecasts.
Revised:  The experiments we conduct show that there is a benefit to online transfer learning , in the subset of models we use , sequential learning in time with online Ridge regression, provides the best next step ahead forecasts, and continual learning with an online radial basis function network, provides the best multi-step ahead forecasts.
Edit intent: clarity
Original:  We also find that , in the subset of models we use , sequential learning in time with online Ridge regression, provides the best next step ahead forecasts, and continual learning with an online radial basis function network, provides the best multi-step ahead forecasts.
Revised:  We also find that , in the form of radial basis function networks, beyond the sequential updating of recursive least-squares models. We show that the radial basis function networks, which make use of clustering algorithms to construct a kernel Gram matrix, are more beneficial than treating each training vector as separate basis functions, as occurs with kernel Ridge regression. We demonstrate quantitative procedures to determine the very structure of the radial basis function networks. Finally, we conduct experiments on the log returns of financial time series and show that the online learning models, particularly the radial basis function network, provides the best multi-step ahead forecasts.
Edit intent: meaning-changed
Original:  We also find that , in the subset of models we use , sequential learning in time with online Ridge regression, provides the best next step ahead forecasts, and continual learning with an online radial basis function network, provides the best multi-step ahead forecasts. We combine the benefits of both in a precision weighted ensemble of the forecast errors and find superior forecast performance overall .
Revised:  We also find that , in the subset of models we use , sequential learning in time with online Ridge regression, provides the best next step ahead forecasts, and continual learning with an online radial basis function networks, are able to outperform a random walk baseline, whereas the offline learning models struggle to do so .
Edit intent: meaning-changed
Original:  When pursuing a retrieval approach, we observe an mAP@R of 0.9748 and a precision@1 of 0.9963  . Based on this high identification rate, a potential attacker may leak patient-related information and additionally cross-reference images to obtain more information.
Revised:  When pursuing a retrieval approach, we observe an mAP@R of 0.9748 and a precision@1 of 0.9963 . Furthermore, we achieve an AUC of up to 0.9870 and a precision@1 of up to 0.9444 when evaluating our trained networks on CheXpert and the COVID-19 Image Data Collection . Based on this high identification rate, a potential attacker may leak patient-related information and additionally cross-reference images to obtain more information.
Edit intent: meaning-changed
Original:  The class G is the family of quadrangulations obtained from the 4-cycle C_4 by successively inserting a single vertex or a 4-cycle of vertices into a face  .
Revised:  The class G is the family of quadrangulations obtained from the 4-cycle C_4 by successively inserting a single vertex or a 4-cycle of vertices into a face . For every graph G\in \mathcal{G .
Edit intent: meaning-changed
Original:  We show that, even without information asymmetries,  the order execution mechanism of the blockchain-based exchange induces adverse selection problems for liquidity providers if token prices are volatile.
Revised:  We show that, even without information asymmetries, liquidity providers lose token value if exchange rates are volatile due to the order execution mechanism of the blockchain-based exchange induces adverse selection problems for liquidity providers if token prices are volatile.
Edit intent: meaning-changed
Original:  We show that, even without information asymmetries,  the order execution mechanism of the blockchain-based exchange induces adverse selection problems for liquidity providers if token prices are volatile. AMM is more likely to be adopted for pairs of coins which are stable or of high personal use for investors.
Revised:  We show that, even without information asymmetries, the order execution mechanism of the blockchain-based exchange . AMMs are more likely to be adopted for pairs of coins which are stable or of high personal use for investors.
Edit intent: clarity
Original:  AMMs are more likely to be adopted for pairs of coins which are stable or of high personal use for investors  . Liquidity providers may not deposit their tokens if the exchange rate volatility is high, leading to a liquidity freeze.
Revised:  AMMs are adopted only if their token pairs are of high personal use for investors . Liquidity providers may not deposit their tokens if the exchange rate volatility is high, leading to a liquidity freeze.
Edit intent: clarity
Original:  AMMs are more likely to be adopted for pairs of coins which are stable or of high personal use for investors  . Liquidity providers may not deposit their tokens if the exchange rate volatility is high, leading to a liquidity freeze.
Revised:  AMMs are more likely to be adopted for pairs of coins which are stable or of high personal use for investors , or the token price movements of the pair are highly correlated. A pricing curve with higher curvature reduces the arbitrage problem but also investors' surplus. Pooling multiple tokens exacerbates the arbitrage problem . Liquidity providers may not deposit their tokens if the exchange rate volatility is high, leading to a liquidity freeze.
Edit intent: meaning-changed
Original:  AMMs are more likely to be adopted for pairs of coins which are stable or of high personal use for investors  . Liquidity providers may not deposit their tokens if the exchange rate volatility is high, leading to a liquidity freeze. The adoption of AMMs leads to a surge of transaction fees on the underlying blockchain if token exchange rates fluctuate wildly.
Revised:  AMMs are more likely to be adopted for pairs of coins which are stable or of high personal use for investors . 
Edit intent: clarity
Original: For a bounded subset S of d\times d complex matrices, the Berger-Wang theorem and Bochi's inequality allow to approximate the joint spectral radius of S from below by the spectral radius of a short product of elements from S.
Revised: We prove explicit polynomial bounds for Bochi's inequality allow to approximate the joint spectral radius of S from below by the spectral radius of a short product of elements from S.
Edit intent: meaning-changed
Original: For a bounded subset S of d\times d complex matrices, the Berger-Wang theorem and Bochi's inequality allow to approximate the joint spectral radius of S from below by the spectral radius of a short product of elements from S.
Revised: For a bounded subset S of d\times d complex matrices, the Berger-Wang theorem and Bochi's inequalities regarding the joint spectral radius of S from below by the spectral radius of a short product of elements from S.
Edit intent: clarity
Original: For a bounded subset S of d\times d complex matrices, the Berger-Wang theorem and Bochi's inequality allow to approximate the joint spectral radius of S from below by the spectral radius of a short product of elements from S. Our goal is two-fold: we review these results, providing self-contained proofs, and we derive an improved version with explicit bounds that are polynomial in d. We also discuss other complete valued fields .
Revised: For a bounded subset S of d\times d complex matrices, the Berger-Wang theorem and Bochi's inequality allow to approximate the joint spectral radius of a subset of d\times d matrices .
Edit intent: coherence
Original: There are demographic biases in the SOTA CNN used for FR. Our BFW dataset serves as a proxy to measure bias across ethnicity and gender subgroups, allowing us to characterize FR performances per subgroup.
Revised: There are demographic biases in current models used for facial recognition (FR). Our Balanced Faces In the Wild (BFW) dataset serves as a proxy to measure bias across ethnicity and gender subgroups, allowing us to characterize FR performances per subgroup.
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Original:  We prove theoretically that our framework , which easily adopts existing RL algorithms, learns to construct a shaping-reward function that is tailored to the task and ensures convergence to higher performing policiesfor the given task .
Revised:  We prove that ROSA , which easily adopts existing RL algorithms, learns to construct a shaping-reward function that is tailored to the task and ensures convergence to higher performing policiesfor the given task .
Edit intent: meaning-changed
Original:  We prove theoretically that our framework , which easily adopts existing RL algorithms, learns to construct a shaping-reward function that is tailored to the task and ensures convergence to higher performing policiesfor the given task .
Revised:  We prove theoretically that our framework , which easily adopts existing RL algorithms, learns to construct a shaping-reward function that is tailored to the task thus ensuring efficient convergence to high performance policies .
Edit intent: clarity
Original:  We demonstrate the superior performance of our method against state-of-the-art RS algorithms in Cartpole and the challenging console games Gravitar, Solaris and Super Mario .
Revised:  We demonstrate ROSA's congenial properties in three carefully designed experiments and show its superior performance against state-of-the-art RS algorithms in Cartpole and the challenging console games Gravitar, Solaris and Super Mario .
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Original:  Noise creates "baits" that attract p-hackers who are uninformed about the true cause behind the outcome but engage in data mining , inducing them to report verifiably wrong results.
Revised:  Noise creates "baits" that attract p-hackers , who engage in data mining with no prior information about the true cause behind the outcome but engage in data mining , inducing them to report verifiably wrong results.
Edit intent: clarity
Original:  Dissemination noise is a tool already in use by statistical agencies (e.g.,  US Census Bureau)  to protect privacy, and we show this existing practice can be repurposed to improve research credibility.
Revised:  Dissemination noise is a tool already in use by statistical agencies (e.g., the US Census Bureau) to protect privacy, and we show this existing practice can be repurposed to improve research credibility.
Edit intent: fluency
Original:  Dissemination noise is a tool already in use by statistical agencies (e.g.,  US Census Bureau)  to protect privacy, and we show this existing practice can be repurposed to improve research credibility.
Revised:  Dissemination noise is a tool already in use by statistical agencies (e.g., US Census Bureau) currently use to protect privacy, and we show this existing practice can be repurposed to improve research credibility.
Edit intent: meaning-changed
Original:  Given the extreme paucity of datasets in this space and the difficulty involved in acquiring real data from warehouses, we additionally release a flexible synthetic dataset generation pipeline WareSynth which allows users to control the generation process and tailor the dataset according to contingent application.
Revised:  Given the extreme paucity of datasets in this space and the difficulty involved in acquiring real data from warehouses, we additionally release a flexible synthetic dataset generation pipeline WareSynth which allows users to control the generation process and tailor the dataset according to contingent application.
Edit intent: fluency
Original:  Here, we are given a colored point set , where the points are covered by (unknown) k clusters, which are monochromatic (i.e., all the points covered by the same cluster, have the same color).
Revised:  Here, we are given a colored point set where the points are covered by (unknown) k clusters, which are monochromatic (i.e., all the points covered by the same cluster, have the same color).
Edit intent: fluency
Original:  We show that  one can correctly deduce the color of all the points (i.e., compute a monochromatic clustering of the points) using a polylogarithmic number of queries , if the number of clusters is a constant .
Revised:  We show that if the number of clusters is a constant, then one can correctly deduce the color of all the points (i.e., compute a monochromatic clustering of the points) using a polylogarithmic number of queries , if the number of clusters is a constant .
Edit intent: meaning-changed
Original:  We show that  one can correctly deduce the color of all the points (i.e., compute a monochromatic clustering of the points) using a polylogarithmic number of queries , if the number of clusters is a constant .
Revised:  We show that one can correctly deduce the color of all the points (i.e., compute a monochromatic clustering of the points) using a polylogarithmic number of queries .
Edit intent: clarity
Original:  We derive the stability condition according to Babuska's theory from a small inf-sup condition, similar to the famous Ladyzhenskaya-Babuska-Brezzi (LBB) condition, and the other standard assumptions in Brezzi's theory under the resulting combined norm.
Revised:  We derive the stability condition according to Babuska's theory from a small inf-sup condition, similar to the famous Ladyzhenskaya-Babuska-Brezzi (LBB) condition, and the other standard assumptions in Brezzi's theory , in a combined abstract norm.
Edit intent: clarity
Original:  The proposed framework allows to split the norms into proper seminormsand not only results in{\it   simpler (shorter) proofs of many stability results but also guides the construction of parameter robust norm-equivalent preconditioners.
Revised:  The proposed framework allows to split the norms into proper seminormsand not only results in{\it fitted simpler (shorter) proofs of many stability results but also guides the construction of parameter robust norm-equivalent preconditioners.
Edit intent: fluency
Original:  The proposed framework allows to split the norms into proper seminormsand not only results in{\it   simpler (shorter) proofs of many stability results but also guides the construction of parameter robust norm-equivalent preconditioners.
Revised:  The proposed framework allows to split the norms into proper seminormsand not only results in{\it norms that are composed from proper seminorms. This abstract framework not only allows for simpler (shorter) proofs of many stability results but also guides the construction of parameter robust norm-equivalent preconditioners.
Edit intent: meaning-changed
Original:  The proposed framework allows to split the norms into proper seminormsand not only results in{\it   simpler (shorter) proofs of many stability results but also guides the construction of parameter robust norm-equivalent preconditioners.
Revised:  The proposed framework allows to split the norms into proper seminormsand not only results in{\it simpler (shorter) proofs of many stability results but also guides the design of parameter-robust norm-equivalent preconditioners.
Edit intent: clarity
Original:  Although some measures such as Inception Score, Fr\'echet Inception Distance, Precision-Recall, and Perceptual Path Length are relatively more popular, GAN evaluation is not a settled issue and there is still room for improvement.
Revised:  Although some measures such as Inception Score, Frechet Inception Distance, Precision-Recall, and Perceptual Path Length are relatively more popular, GAN evaluation is not a settled issue and there is still room for improvement.
Edit intent: fluency
Original: For the large sparse generalized absolute value equations (GAVEs) , the shift splitting for the linear part's coefficient matrix is utilized to establish a new modified Newton-type ( NMN ) iteration method  .
Revised: A shift splitting modified Newton-type (SSMN) iteration method is introduced for solving large sparse generalized absolute value equations (GAVEs) , the shift splitting for the linear part's coefficient matrix is utilized to establish a new modified Newton-type ( NMN ) iteration method .
Edit intent: meaning-changed
Original: For the large sparse generalized absolute value equations (GAVEs) , the shift splitting for the linear part's coefficient matrix is utilized to establish a new modified Newton-type ( NMN ) iteration method  .
Revised: For the large sparse generalized absolute value equations (GAVEs) . The SSMN method is established by replacing the regularized splitting of the coefficient matrix of the linear part, which is employed in the modified Newton-type ( NMN ) iteration method .
Edit intent: meaning-changed
Original: For the large sparse generalized absolute value equations (GAVEs) , the shift splitting for the linear part's coefficient matrix is utilized to establish a new modified Newton-type ( NMN ) iteration method  .
Revised: For the large sparse generalized absolute value equations (GAVEs) , the shift splitting for the linear part's coefficient matrix is utilized to establish a new modified Newton-type ( NMN ) iteration method , with the shift splitting of the matrix .
Edit intent: clarity
Original:  According to both two numerical examples, the NMN iteration method is an effective approach to solve the GAVEs, especially when the coefficient matrix is indefinite.
Revised:  According to both two numerical examples, we find that the SSMN and MN methods complement each other. The optimal performances of the two methods depend on the definiteness of the coefficient matrix of the linear part. The MN method is more efficient when the coefficient matrix is positive definite, whereas the SSMN method has a better performance when the coefficient matrix is indefinite.
Edit intent: meaning-changed
Original: Previous works on expressive text-to-speech (TTS) have a limitation on robustness and speed when training and inferring. Such drawbacks mostly come from autoregressive decoding, which makes the succeeding step vulnerable to preceding error. To overcome this weakness , we propose STYLER, a novel expressive text-to-speech model with parallelized architecture.
Revised: Previous works on expressive text-to-speech (TTS) have been tackled on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, and controllability. Although several approaches resolve some limitations, none of them has resolved all weaknesses at once. In this paper , we propose STYLER, a novel expressive text-to-speech model with parallelized architecture.
Edit intent: clarity
Original:  To overcome this weakness , we propose STYLER, a novel expressive text-to-speech model with parallelized architecture. Expelling autoregressive decoding and introducing speech decomposition for encoding enables speech synthesis more robust even with high style transfer performance .
Revised:  To overcome this weakness , we propose STYLER, a novel expressive text-to-speech model with robust speech synthesis and high speed. Excluding autoregressive decoding and introducing speech decomposition for encoding enables speech synthesis more robust even with high style transfer performance .
Edit intent: clarity
Original:  Moreover, our novel noise modeling approach from audio using domain adversarial training and Residual Decoding enabled style transferwithout transferring noise . Our experiments prove the naturalness and expressiveness of our model from comparison with other parallel TTS models . Together we investigate our model 's robustness and speed by comparison with the expressive TTS model with autoregressive decoding .
Revised:  Moreover, our novel noise modeling approach from audio using domain adversarial training and Residual Decoding enables noise-robust style transfer, decomposing the noise without any additional label. Our extensive and various experiments demonstrate STYLER's effectiveness in the aspects of speed, robustness, expressiveness, and controllability by comparison with existing neural TTS models and ablation studies. Synthesis samples of our model and experiment results are provided via our demo page .
Edit intent: meaning-changed
Original:  In this paper, we propose STYLER, an expressive and controllable text-to-speech model with robust speech synthesisand high speed. Excluding autoregressive decoding and introducing a novel audio-text aligning method called Mel Calibrator leads speech synthesis more robust on long, unseen data.
Revised:  In this paper, we propose STYLER, an expressive and controllable TTS framework with high-speed and robust synthesis. Our novel audio-text aligning method called Mel Calibrator leads speech synthesis more robust on long, unseen data.
Edit intent: clarity
Original:  Disentangled style factor modeling under supervision enlarges the controllability of synthesizing speech with fruitful expressivity. Moreover, our novel noise modeling pipeline using domain adversarial training and Residual Decoding enables noise-robust style transfer, decomposing the noise without any additional label.
Revised:  Disentangled style factor modeling under supervision enlarges the controllability in synthesizing process leading to expressive TTS. On top of it, a novel noise modeling pipeline using domain adversarial training and Residual Decoding enables noise-robust style transfer, decomposing the noise without any additional label.
Edit intent: clarity
Original:  Our extensive and various experiments demonstrate STYLER's effectiveness in the aspects of speed , robustness, expressiveness, and controllability by comparison with existing neural TTS models and ablation studies . Synthesis samples of our model and experiment results are provided via our demo page  .
Revised:  Various experiments demonstrate that STYLER is more effective in speed and robustness than expressive TTS with autoregressive decoding and more expressive and controllable than reading style non-autoregressive TTS . Synthesis samples of our model and experiment results are provided via our demo page .
Edit intent: clarity
Original:  Our extensive and various experiments demonstrate STYLER's effectiveness in the aspects of speed , robustness, expressiveness, and controllability by comparison with existing neural TTS models and ablation studies . Synthesis samples of our model and experiment results are provided via our demo page  .
Revised:  Our extensive and various experiments demonstrate STYLER's effectiveness in the aspects of speed , robustness, expressiveness, and controllability by comparison with existing neural TTS models and ablation studies . Synthesis samples and experiment results are provided via our demo page .
Edit intent: clarity
Original:  Our extensive and various experiments demonstrate STYLER's effectiveness in the aspects of speed , robustness, expressiveness, and controllability by comparison with existing neural TTS models and ablation studies . Synthesis samples of our model and experiment results are provided via our demo page  .
Revised:  Our extensive and various experiments demonstrate STYLER's effectiveness in the aspects of speed , robustness, expressiveness, and controllability by comparison with existing neural TTS models and ablation studies . Synthesis samples of our model and experiment results are provided via our demo page , and code is available publicly .
Edit intent: meaning-changed