wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We propose an adaptive recursive estimation routine for GARCH models using the technique of Variance Targeting Estimation (VTE) to alleviate the convergence difficulties encountered in the usual QML estimation . Finally, empirical results demonstrate a favorable trade-off between  the ability to adapt to time-varying estimates and stability of the estimation routine .
Revised:  We propose an adaptive recursive estimation routine for GARCH models using the technique of Variance Targeting Estimation (VTE) to alleviate the convergence difficulties encountered in the usual QML estimation due to a lack of convexity. Empirical results demonstrate a favorable trade-off between the ability to adapt to time-varying estimates and stability of the estimation routine .
Edit intent: meaning-changed
Original:  In this paper, a speaker identification baseline is improved by re-ranking candidate speakers based on the frequency of previous interactions between speakers and the topology of the criminal network. The proposed method can be applied to conversations involving two or more speakers. We show that our approach outperforms the baseline speaker accuracy by 1.3 \% absolute ( 1.5 \% relative), and the conversation accuracy by 3.7 \% absolute ( 4.7 \% relative) on CSI  data.
Revised:  In this paper, a speaker identification baseline is improved by re-ranking candidate speakers based on the frequency of previous interactions , we improve the speaker identification baseline by 1.2 \% absolute ( 1.5 \% relative), and the conversation accuracy by 3.7 \% absolute ( 4.7 \% relative) on CSI data.
Edit intent: meaning-changed
Original:  We show that our approach outperforms the baseline speaker accuracy by 1.3 \% absolute ( 1.5 \% relative), and the conversation accuracy by 3.7 \% absolute ( 4.7 \% relative) on CSI  data.
Revised:  We show that our approach outperforms the baseline speaker accuracy by 1.3 \% absolute ( 1.3 \% relative), and the conversation accuracy by 3.7 \% absolute ( 4.7 \% relative) on CSI data.
Edit intent: meaning-changed
Original:  We show that our approach outperforms the baseline speaker accuracy by 1.3 \% absolute ( 1.5 \% relative), and the conversation accuracy by 3.7 \% absolute ( 4.7 \% relative) on CSI  data.
Revised:  We show that our approach outperforms the baseline speaker accuracy by 1.3 \% absolute ( 1.5 \% relative), and the conversation accuracy by 2.6 \% absolute ( 4.7 \% relative) on CSI data.
Edit intent: meaning-changed
Original:  We show that our approach outperforms the baseline speaker accuracy by 1.3 \% absolute ( 1.5 \% relative), and the conversation accuracy by 3.7 \% absolute ( 4.7 \% relative) on CSI  data.
Revised:  We show that our approach outperforms the baseline speaker accuracy by 1.3 \% absolute ( 1.5 \% relative), and the conversation accuracy by 3.7 \% absolute ( 3.4 \% relative) on CSI data.
Edit intent: meaning-changed
Original:  We show that our approach outperforms the baseline speaker accuracy by 1.3 \% absolute ( 1.5 \% relative), and the conversation accuracy by 3.7 \% absolute ( 4.7 \% relative) on CSI  data.
Revised:  We show that our approach outperforms the baseline speaker accuracy by 1.3 \% absolute ( 1.5 \% relative), and the conversation accuracy by 3.7 \% absolute ( 4.7 \% relative) on CSI data, and by 1.1\% absolute (1.2\% relative), and 2\% absolute (2.5\% relative) respectively on the ROXANNE simulated data.
Edit intent: meaning-changed
Original:  Our results demonstrate  that the actual number of cases are unlikely to be more than 25 times in New York, 34 times in Illinois, 33 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Revised:  Our results demonstrate with high confidence that the actual number of cases are unlikely to be more than 25 times in New York, 34 times in Illinois, 33 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Edit intent: meaning-changed
Original:  Our results demonstrate  that the actual number of cases are unlikely to be more than 25 times in New York, 34 times in Illinois, 33 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Revised:  Our results demonstrate that the actual number of cases cannot be more than 25 times in New York, 34 times in Illinois, 33 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Edit intent: clarity
Original:  Our results demonstrate  that the actual number of cases are unlikely to be more than 25 times in New York, 34 times in Illinois, 33 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Revised:  Our results demonstrate that the actual number of cases are unlikely to be more than 35 times in New York, 34 times in Illinois, 33 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Edit intent: meaning-changed
Original:  Our results demonstrate  that the actual number of cases are unlikely to be more than 25 times in New York, 34 times in Illinois, 33 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Revised:  Our results demonstrate that the actual number of cases are unlikely to be more than 25 times in New York, 40 times in Illinois, 33 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Edit intent: meaning-changed
Original:  Our results demonstrate  that the actual number of cases are unlikely to be more than 25 times in New York, 34 times in Illinois, 33 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Revised:  Our results demonstrate that the actual number of cases are unlikely to be more than 25 times in New York, 34 times in Illinois, 38 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Edit intent: meaning-changed
Original:  Our results demonstrate  that the actual number of cases are unlikely to be more than 25 times in New York, 34 times in Illinois, 33 times in Massachusetts and 17 times in New Jersey, than the reported cases.
Revised:  Our results demonstrate that the actual number of cases are unlikely to be more than 25 times in New York, 34 times in Illinois, 33 times in Massachusetts and 29 times in New Jersey, than the reported cases.
Edit intent: meaning-changed
Original:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with d \sigma + O(d) space.
Revised:  In this paper, we consider the sliding window version of the problem: For a sliding window of length at most d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with d \sigma + O(d) space.
Edit intent: clarity
Original:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with d \sigma + O(d) space.
Revised:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we present two versions of an algorithm which maintains the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with d \sigma + O(d) space.
Edit intent: clarity
Original:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with d \sigma + O(d) space.
Revised:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. j ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with d \sigma + O(d) space.
Edit intent: meaning-changed
Original:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with d \sigma + O(d) space.
Revised:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, where 1 \leq j-i+1 \leq d. The first version works in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with d \sigma + O(d) space.
Edit intent: meaning-changed
Original:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with d \sigma + O(d) space.
Revised:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the second one works in O(n + d\sigma) time with d \sigma + O(d) space.
Edit intent: clarity
Original:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with d \sigma + O(d) space.
Revised:  In this paper, we consider the sliding window version of the problem: For a fixed window length d, we propose two algorithms to maintain the palindromic tree of size O(d) for every sliding window S[i.. i+d-1 ] over S, one running in O(n\log\sigma') time with O(d) space where \sigma' \leq d is the maximum number of distinct characters in the windows, and the other running in O(n + d\sigma) time with (d+2) \sigma + O(d) space.
Edit intent: meaning-changed
Original:  While the experimental research on the development of these devices has progressed remarkably, their simulation is still challenging due to the need for accurate and efficient modeling of multiphysics processes and intricate device geometries  .
Revised:  However, this improvement comes at the cost of increased complexity for the design and simulation of these devices has progressed remarkably, their simulation is still challenging due to the need for accurate and efficient modeling of multiphysics processes and intricate device geometries .
Edit intent: meaning-changed
Original:  While the experimental research on the development of these devices has progressed remarkably, their simulation is still challenging due to the need for accurate and efficient modeling of multiphysics processes and intricate device geometries  .
Revised:  While the experimental research on the development of these devices has progressed remarkably, their simulation is still challenging due to the need for accurate and efficient modeling of multiphysics processes and intricate device geometries of nanostructured PCDs is challenging due to the high computational cost resulting from multiple characteristic scales in time and space .
Edit intent: meaning-changed
Original:  The scheme considers two physical stages of the device and models them using two coupled systems , i.e., a system of Poisson and drift-diffusion equations describing the nonequilibrium steady state, and a system of Maxwell and drift-diffusion equations describing the transient stage.
Revised:  The scheme considers two physical stages of the device and models them using two coupled systems : a system of Poisson and drift-diffusion equations describing the nonequilibrium steady state, and a system of Maxwell and drift-diffusion equations describing the transient stage.
Edit intent: coherence
Original:  The resulting DG-based unit-cell scheme is significantly faster than the DG scheme that takes into account the whole device . Additionally, the proposed scheme is used for the first-ever numerical demonstration of optical- and radiation-field screening effects on PCD response. The optical-field screening is found to play a more dominant role in the saturation of PCD output at high levels of optical pump power .
Revised:  The resulting DG-based unit-cell scheme is significantly faster than the DG scheme that takes into account the whole device , while it significantly reduces the computational cost .
Edit intent: meaning-changed
Original: Recent unsupervised machine translation (UMT) systems usually employ three main principles: initialization, language modeling and iterative back-translation, though they may apply these principles differently. This work introduces another component to this framework : Multi-Agent Cross-translated Diversification (MACD). The method trains multiple UMT agents and then translates monolingual data back and forth using non-duplicative agents to acquire synthetic parallel data for supervised MT. MACD is applicable to all previous UMT approaches.
Revised: Recent unsupervised machine translation (UMT) systems usually employ three main principles: initialization, language modeling and iterative back-translation, though they may apply them differently. Crucially, iterative back-translation and denoising auto-encoding for language modeling provide data diversity to train the UMT systems. However, the gains from these diversification processes has seemed to plateau. We introduce a novel component to the standard UMT framework called Cross-model Back-translated Distillation (CBD), that is aimed to induce another level of data diversification that existing principles lack. CBD is applicable to all previous UMT approaches.
Edit intent: meaning-changed
Original:  In particular, in WMT'14 English-French, WMT'16 German-English and English-Romanian, MACD outperforms cross-lingual masked language model pretraining by 2.3, 2.2 and 1.6 BLEU, respectively.
Revised:  In particular, in WMT'14 English-French, WMT'16 German-English and English-Romanian, CBD outperforms cross-lingual masked language model pretraining by 2.3, 2.2 and 1.6 BLEU, respectively.
Edit intent: meaning-changed
Original:  In particular, in WMT'14 English-French, WMT'16 German-English and English-Romanian, MACD outperforms cross-lingual masked language model pretraining by 2.3, 2.2 and 1.6 BLEU, respectively.
Revised:  In particular, in WMT'14 English-French, WMT'16 German-English and English-Romanian, MACD outperforms cross-lingual masked language model (XLM) by 2.3, 2.2 and 1.6 BLEU, respectively.
Edit intent: meaning-changed
Original:  In our experiments, it boosts the performance of the standard UMT methods by 1.5-2.0 BLEU. In particular, in WMT'14 English-French, WMT'16 German-English and English-Romanian , CBD outperforms cross-lingual masked language model (XLM) by 2.3, 2.2 and 1.6 BLEU , respectively.
Revised:  In our experiments, CBD achieves the state of the art in the WMT'14 English-French, WMT'16 German-English and English-Romanian , CBD outperforms cross-lingual masked language model (XLM) by 2.3, 2.2 and 1.6 BLEU , respectively.
Edit intent: meaning-changed
Original:  In particular, in WMT'14 English-French, WMT'16 German-English and English-Romanian , CBD outperforms cross-lingual masked language model (XLM) by 2.3, 2.2 and 1.6 BLEU , respectively.
Revised:  In particular, in WMT'14 English-French, WMT'16 English-German and English-Romanian , CBD outperforms cross-lingual masked language model (XLM) by 2.3, 2.2 and 1.6 BLEU , respectively.
Edit intent: fluency
Original:  In particular, in WMT'14 English-French, WMT'16 German-English and English-Romanian , CBD outperforms cross-lingual masked language model (XLM) by 2.3, 2.2 and 1.6 BLEU , respectively.
Revised:  In particular, in WMT'14 English-French, WMT'16 German-English and English-Romanian bilingual unsupervised translation tasks, with 38.2, 30.1, and 36.3 BLEU respectively.
Edit intent: meaning-changed
Original:  For the 19 prefectures included in this study that had daily maximum confirmed cases exceeding ten, the confirmed cases increased and decreased exponentially in the spread and decay durations in most of the prefectures.
Revised:  For the 16 prefectures included in this study that had daily maximum confirmed cases exceeding ten, the confirmed cases increased and decreased exponentially in the spread and decay durations in most of the prefectures.
Edit intent: meaning-changed
Original:  For the 19 prefectures included in this study that had daily maximum confirmed cases exceeding ten, the confirmed cases increased and decreased exponentially in the spread and decay durations in most of the prefectures.
Revised:  For the 19 prefectures included in this study that had daily maximum confirmed cases exceeding ten, the number of daily confirmed cases follow bell-shape or log-normal distribution in most prefectures.
Edit intent: meaning-changed
Original:  A good correlation was observed between the spread and decay durations (R2=0.37) . However, some exceptions were observed in prefectures that are adjacent to primary prefectures (e.g., Tokyo, Osaka, Aichi, and Fukuoka) and in areas where people returned from foreign countries, which were defined origins of cluster infections . Excluding these prefectures, population density is shown to be the primary factor affecting spread and decay patterns, with correlations of 0.52 and 0.76, respectively.
Revised:  A good correlation was observed between the spread and decay durations . However, some exceptions were observed in prefectures that are adjacent to primary prefectures (e.g., Tokyo, Osaka, Aichi, and Fukuoka) and in areas where people returned from foreign countries, which were defined origins of cluster infections . Excluding these prefectures, population density is shown to be the primary factor affecting spread and decay patterns, with correlations of 0.52 and 0.76, respectively.
Edit intent: clarity
Original:  A good correlation was observed between the spread and decay durations (R2=0.37) . However, some exceptions were observed in prefectures that are adjacent to primary prefectures (e.g., Tokyo, Osaka, Aichi, and Fukuoka) and in areas where people returned from foreign countries, which were defined origins of cluster infections . Excluding these prefectures, population density is shown to be the primary factor affecting spread and decay patterns, with correlations of 0.52 and 0.76, respectively.
Revised:  A good correlation was observed between the spread and decay durations (R2=0.37) . However, some exceptions were observed in areas where travelers returned from foreign countries, which were defined origins of cluster infections . Excluding these prefectures, population density is shown to be the primary factor affecting spread and decay patterns, with correlations of 0.52 and 0.76, respectively.
Edit intent: clarity
Original:  A good correlation was observed between the spread and decay durations (R2=0.37) . However, some exceptions were observed in prefectures that are adjacent to primary prefectures (e.g., Tokyo, Osaka, Aichi, and Fukuoka) and in areas where people returned from foreign countries, which were defined origins of cluster infections . Excluding these prefectures, population density is shown to be the primary factor affecting spread and decay patterns, with correlations of 0.52 and 0.76, respectively.
Revised:  A good correlation was observed between the spread and decay durations (R2=0.37) . However, some exceptions were observed in prefectures that are adjacent to primary prefectures (e.g., Tokyo, Osaka, Aichi, and Fukuoka) and in areas where people returned from foreign countries, which were defined as the origins of infection clusters . Excluding these prefectures, population density is shown to be the primary factor affecting spread and decay patterns, with correlations of 0.52 and 0.76, respectively.
Edit intent: fluency
Original:  A good correlation was observed between the spread and decay durations (R2=0.37) . However, some exceptions were observed in prefectures that are adjacent to primary prefectures (e.g., Tokyo, Osaka, Aichi, and Fukuoka) and in areas where people returned from foreign countries, which were defined origins of cluster infections . Excluding these prefectures, population density is shown to be the primary factor affecting spread and decay patterns, with correlations of 0.52 and 0.76, respectively.
Revised:  A good correlation was observed between the spread and decay durations (R2=0.37) . However, some exceptions were observed in prefectures that are adjacent to primary prefectures (e.g., Tokyo, Osaka, Aichi, and Fukuoka) and in areas where people returned from foreign countries, which were defined origins of cluster infections . Excluding these prefectures, the population density was shown to be the primary factor affecting spread and decay patterns, with correlations of 0.52 and 0.76, respectively.
Edit intent: fluency
Original:  A good correlation was observed between the spread and decay durations (R2=0.37) . However, some exceptions were observed in prefectures that are adjacent to primary prefectures (e.g., Tokyo, Osaka, Aichi, and Fukuoka) and in areas where people returned from foreign countries, which were defined origins of cluster infections . Excluding these prefectures, population density is shown to be the primary factor affecting spread and decay patterns, with correlations of 0.52 and 0.76, respectively.
Revised:  A good correlation was observed between the spread and decay durations (R2=0.37) . However, some exceptions were observed in prefectures that are adjacent to primary prefectures (e.g., Tokyo, Osaka, Aichi, and Fukuoka) and in areas where people returned from foreign countries, which were defined origins of cluster infections . Excluding these prefectures, population density is shown to be a major factor affecting the spread and decay patterns, with correlations of 0.52 and 0.76, respectively.
Edit intent: clarity