wanyu/IteraTeR_v2 huggingface dataset:
Original:  Benifiting from these works, the paper introduces a write-friendly flash-based disk cache system,  called WLFC (Write Less in  Flash-based Cache) which mainly makes three contributions.
Revised:  Benifiting from these works, the paper introduces a write-friendly flash-based disk cache system, called WLFC (Write Less in the Flash-based Cache) which mainly makes three contributions.
Edit intent: coherence
Original:  Here, an s-diagonal tensor is a special f-diagonal tensor.
Revised:  Here, an s-diagonal tensor . An f-diagonal tensor.
Edit intent: clarity
Original: Contrastive learning has delivered impressive results in many audio-visual representation learning scenarios .
Revised: Contrastive learning has delivered impressive results for various tasks in the self-supervised regime .
Edit intent: clarity
Original:  However, existing approaches optimize for learning either global representations useful for tasks such as classification , or local representations useful for tasks such as audio-visual source localization and separation .
Revised:  However, existing approaches optimize for learning either global representations suitable for tasks such as classification , or local representations useful for tasks such as audio-visual source localization and separation .
Edit intent: clarity
Original:  However, existing approaches optimize for learning either global representations useful for tasks such as classification , or local representations useful for tasks such as audio-visual source localization and separation .
Revised:  However, existing approaches optimize for learning either global representations useful for tasks such as classification or local representations useful for tasks such as audio-visual source localization and separation .
Edit intent: fluency
Original:  However, existing approaches optimize for learning either global representations useful for tasks such as classification , or local representations useful for tasks such as audio-visual source localization and separation .
Revised:  However, existing approaches optimize for learning either global representations useful for tasks such as classification , or local representations for tasks such as audio-visual source localization and separation .
Edit intent: fluency
Original:  However, existing approaches optimize for learning either global representations useful for tasks such as classification , or local representations useful for tasks such as audio-visual source localization and separation .
Revised:  However, existing approaches optimize for learning either global representations useful for tasks such as classification , or local representations useful for tasks such as detection and localization .
Edit intent: clarity
Original:  While they produce satisfactory results in their intended downstream scenarios, they often fail to generalize to tasks that they were not originally designed for.
Revised:  While they produce satisfactory results in the intended downstream scenarios, they often fail to generalize to tasks that they were not originally designed for.
Edit intent: fluency
Original:  In this work, we propose a versatile self-supervised approach to learn audio-visual representations that generalize to both the tasks which require global semantic information (e.g., classification) and the tasks that require  fine-grained spatio-temporal information (e.g.  localization).
Revised:  In this work, we propose to learn video representations that generalize to both the tasks which require global semantic information (e.g., classification) and the tasks that require fine-grained spatio-temporal information (e.g. localization).
Edit intent: clarity
Original:  In this work, we propose a versatile self-supervised approach to learn audio-visual representations that generalize to both the tasks which require global semantic information (e.g., classification) and the tasks that require  fine-grained spatio-temporal information (e.g.  localization).
Revised:  In this work, we propose a versatile self-supervised approach to learn audio-visual representations that generalize to both the tasks which require global semantic information (e.g., classification) and the tasks that require local fine-grained spatio-temporal information (e.g. localization).
Edit intent: clarity
Original:  In this work, we propose a versatile self-supervised approach to learn audio-visual representations that generalize to both the tasks which require global semantic information (e.g., classification) and the tasks that require  fine-grained spatio-temporal information (e.g.  localization).
Revised:  In this work, we propose a versatile self-supervised approach to learn audio-visual representations that generalize to both the tasks which require global semantic information (e.g., classification) and the tasks that require fine-grained spatio-temporal information (e.g. , localization).
Edit intent: fluency
Original:  We achieve this by optimizing two cross-modal contrastive objectives that together encourage our model to learn discriminative global-local visual information given audio signals.
Revised:  We achieve this by optimizing two contrastive objectives that together encourage our model to learn discriminative global-local visual information given audio signals.
Edit intent: clarity
Original:  We achieve this by optimizing two cross-modal contrastive objectives that together encourage our model to learn discriminative global-local visual information given audio signals.
Revised:  We achieve this by optimizing two cross-modal contrastive objectives that together encourage our model to learn global-local visual information given audio signals.
Edit intent: clarity
Original:  To show that our approach learns generalizable video representations, we evaluate it on various downstream scenarios including action/sound classification, lip reading, deepfake detection, and sound source localization .
Revised:  To show that our approach learns generalizable video representations, we evaluate it on various downstream scenarios including action/sound classification, lip reading, deepfake detection, event and sound localization URL
Edit intent: clarity
Original: This paper studies stochastic stability of a class of discrete-time phase-coupled oscillators.
Revised: This paper studies stochastic relative phase stability of discrete-time phase-coupled oscillators.
Edit intent: clarity
Original:  We introduce the two new notions of stochastic  and ultimate stochastic phase-cohesiveness using the concepts of Harris and positive Harris recurrent Markov chains . Stochastic phase-cohesiveness of oscillators in two types of networks are studied.
Revised:  We introduce the new notions of stochastic and ultimate stochastic phase-cohesiveness using the concepts of Harris and positive Harris recurrent Markov chains . Stochastic phase-cohesiveness of oscillators in two types of networks are studied.
Edit intent: clarity
Original:  We introduce the two new notions of stochastic  and ultimate stochastic phase-cohesiveness using the concepts of Harris and positive Harris recurrent Markov chains . Stochastic phase-cohesiveness of oscillators in two types of networks are studied.
Revised:  We introduce the two new notions of stochastic phase-cohesiveness and ultimate stochastic phase-cohesiveness using the concepts of Harris and positive Harris recurrent Markov chains . Stochastic phase-cohesiveness of oscillators in two types of networks are studied.
Edit intent: fluency
Original:  We introduce the two new notions of stochastic  and ultimate stochastic phase-cohesiveness using the concepts of Harris and positive Harris recurrent Markov chains . Stochastic phase-cohesiveness of oscillators in two types of networks are studied.
Revised:  We introduce the two new notions of stochastic and ultimate stochastic phase-cohesiveness . Stochastic phase-cohesiveness of oscillators in two types of networks are studied.
Edit intent: coherence
Original:  We introduce the two new notions of stochastic  and ultimate stochastic phase-cohesiveness using the concepts of Harris and positive Harris recurrent Markov chains . Stochastic phase-cohesiveness of oscillators in two types of networks are studied.
Revised:  We introduce the two new notions of stochastic and ultimate stochastic phase-cohesiveness using the concepts of Harris and positive Harris recurrent Markov chains . Stochastic phase-cohesiveness in two types of networks are studied.
Edit intent: clarity
Original:  We introduce the two new notions of stochastic  and ultimate stochastic phase-cohesiveness using the concepts of Harris and positive Harris recurrent Markov chains . Stochastic phase-cohesiveness of oscillators in two types of networks are studied.
Revised:  We introduce the two new notions of stochastic and ultimate stochastic phase-cohesiveness using the concepts of Harris and positive Harris recurrent Markov chains . Stochastic phase-cohesiveness of oscillators in two types of networks is studied.
Edit intent: fluency
Original:  First, oscillators in a network with an underlying connected topology subject to both multiplicative and additive stochastic uncertainties are considered.
Revised:  First, we consider oscillators over underlying undirected graphs subject to both multiplicative and additive stochastic uncertainties are considered.
Edit intent: clarity
Original:  Second, we study a special case of the former problem by assuming that the multiplicative uncertainties are governed by the Bernoulli process representing the well known Erd \H o%DIFDELCMD < }%%% s R\'enyi network  .
Revised:  Second, we study a special case of the former problem by assuming that the multiplicative uncertainties are governed by the Bernoulli process representing the \H o%DIFDELCMD < }%%% s R\'enyi network .
Edit intent: clarity
Original:  Second, we study a special case of the former problem by assuming that the multiplicative uncertainties are governed by the Bernoulli process representing the well known Erd \H o%DIFDELCMD < }%%% s R\'enyi network  .
Revised:  Second, we study a special case of the former problem by assuming that the multiplicative uncertainties are governed by the Bernoulli process representing the well known Erd %DIFDELCMD < }%%% s R\'enyi network .
Edit intent: clarity
Original:  The new paradigm of ``learned index'' has significantly changed the way of designing index structures in DBMS.
Revised:  The new paradigm of "learned index" has significantly changed the way of designing index structures in DBMS.
Edit intent: fluency
Original:  Therefore, vaccination and social distancing has to be coordinated.
Revised:  Therefore, vaccination and social distancing have to be coordinated.
Edit intent: fluency
Original:  For real-life decision making  we investigate the impact of the planning horizon on the optimal vaccination/social distancing strategy.
Revised:  For real-life decision making , we investigate the impact of the planning horizon on the optimal vaccination/social distancing strategy.
Edit intent: fluency
Original:  We find that in order to reduce social distancing in the long run  without overburdening the intensive care units it is essential to vaccinate the people with the highest contact rates first.
Revised:  We find that in order to reduce social distancing in the long run , without overburdening the intensive care units it is essential to vaccinate the people with the highest contact rates first.
Edit intent: fluency
Original:  We find that in order to reduce social distancing in the long run  without overburdening the intensive care units it is essential to vaccinate the people with the highest contact rates first.
Revised:  We find that in order to reduce social distancing in the long run without overburdening the healthcare system, it is essential to vaccinate the people with the highest contact rates first.
Edit intent: clarity
Original: The UNESCO World Heritage List (WHL) is to identify the exceptionally valuable cultural and natural heritage to be preserved for mankind as a whole .
Revised: The UNESCO World Heritage List (WHL) includes the exceptionally valuable cultural and natural heritage to be preserved for mankind as a whole .
Edit intent: clarity
Original: The UNESCO World Heritage List (WHL) is to identify the exceptionally valuable cultural and natural heritage to be preserved for mankind as a whole .
Revised: The UNESCO World Heritage List (WHL) is to identify the exceptionally valuable cultural and natural heritage to be preserved for mankind .
Edit intent: clarity
Original:  Evaluating and justifying the Outstanding Universal Value (OUV) of each nomination in WHL is essentially important for a property to be inscribed , and yet a complex task  even for experts since the criteria are not mutually exclusive.
Revised:  Evaluating and justifying the Outstanding Universal Value (OUV) is essential for each site inscribed in the WHL , and yet a complex task even for experts since the criteria are not mutually exclusive.
Edit intent: clarity
Original:  Evaluating and justifying the Outstanding Universal Value (OUV) of each nomination in WHL is essentially important for a property to be inscribed , and yet a complex task  even for experts since the criteria are not mutually exclusive.
Revised:  Evaluating and justifying the Outstanding Universal Value (OUV) of each nomination in WHL is essentially important for a property to be inscribed , and yet a complex task , even for experts since the criteria are not mutually exclusive.
Edit intent: fluency
Original:  Evaluating and justifying the Outstanding Universal Value (OUV) of each nomination in WHL is essentially important for a property to be inscribed , and yet a complex task  even for experts since the criteria are not mutually exclusive.
Revised:  Evaluating and justifying the Outstanding Universal Value (OUV) of each nomination in WHL is essentially important for a property to be inscribed , and yet a complex task even for experts , since the selection criteria of OUV are not mutually exclusive.
Edit intent: clarity
Original:  This study applies state-of-the-art NLP models to build a classifier on a new real-world dataset containing official OUVjustification statements ,
Revised:  This study applies state-of-the-art NLP models to build a classifier on a new dataset containing Statements of OUV ,
Edit intent: clarity
Original:  seeking an explainable , scalable, and less biased automation tool to facilitate the nomination, evaluation,  and monitoring processes of World Heritage properties . Label smoothing is innovatively adapted to transform the task smoothly between multi-class and multi-label classification by adding prior inter-class relationship knowledge into the labels, improving the performance of most baselines .
Revised:  seeking an explainable and scalable automation tool to facilitate the nomination, evaluation, and monitoring processes of World Heritage properties . Label smoothing is innovatively adapted to transform the task smoothly between multi-class and multi-label classification by adding prior inter-class relationship knowledge into the labels, improving the performance of most baselines .
Edit intent: clarity
Original:  seeking an explainable , scalable, and less biased automation tool to facilitate the nomination, evaluation,  and monitoring processes of World Heritage properties . Label smoothing is innovatively adapted to transform the task smoothly between multi-class and multi-label classification by adding prior inter-class relationship knowledge into the labels, improving the performance of most baselines .
Revised:  seeking an explainable , scalable, and less biased automation tool to facilitate the nomination, evaluation, research, and monitoring processes of World Heritage properties . Label smoothing is innovatively adapted to transform the task smoothly between multi-class and multi-label classification by adding prior inter-class relationship knowledge into the labels, improving the performance of most baselines .
Edit intent: clarity
Original:  seeking an explainable , scalable, and less biased automation tool to facilitate the nomination, evaluation,  and monitoring processes of World Heritage properties . Label smoothing is innovatively adapted to transform the task smoothly between multi-class and multi-label classification by adding prior inter-class relationship knowledge into the labels, improving the performance of most baselines .
Revised:  seeking an explainable , scalable, and less biased automation tool to facilitate the nomination, evaluation, and monitoring processes of World Heritage sites . Label smoothing is innovatively adapted to transform the task smoothly between multi-class and multi-label classification by adding prior inter-class relationship knowledge into the labels, improving the performance of most baselines .
Edit intent: clarity
Original:  seeking an explainable , scalable, and less biased automation tool to facilitate the nomination, evaluation,  and monitoring processes of World Heritage properties . Label smoothing is innovatively adapted to transform the task smoothly between multi-class and multi-label classification by adding prior inter-class relationship knowledge into the labels, improving the performance of most baselines .
Revised:  seeking an explainable , scalable, and less biased automation tool to facilitate the nomination, evaluation, and monitoring processes of World Heritage properties . Label smoothing is innovatively adapted to improve the model performance by adding prior inter-class relationship knowledge into the labels, improving the performance of most baselines .
Edit intent: clarity
Original: In this paper, we propose SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model that improves similarity for speakers unseen in training.
Revised: In this paper, we propose SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model that improves similarity for speakers unseen during training.
Edit intent: clarity
Original:  Our model is able to converge in training, using only 11 speakers, reaching state-of-the-art results for similarity with new speakers, as well as high speech quality.
Revised:  Our model converges using only 11 speakers, reaching state-of-the-art results for similarity with new speakers, as well as high speech quality.
Edit intent: clarity
Original:  DESIGN : Retrospective cohort, analysed using a matched nested case-control study design.
Revised:  Design : Retrospective cohort, analysed using a matched nested case-control study design.
Edit intent: coherence
Original:  The higher severity may be specific to older age groups.
Revised:  The higher severity may be specific to adults above the age of 30.
Edit intent: clarity
Original:  Main Outcome Measures : Hospitalisation between 1 and 14 days after the first positive SARS-CoV-2 test.
Revised:  Main outcome measures : Hospitalisation between 1 and 14 days after the first positive SARS-CoV-2 test.
Edit intent: fluency
Original:  Recent theoretical studies have shown that self-adjusting mechanisms can provably outperform the best static parameters in evolutionary algorithms on discrete problems.
Revised:  Recent theoretical studies have shown that self-adjusting mechanisms can provably outperform the best static parameters in EAs on discrete problems.
Edit intent: clarity
Original:  However, the majority of these studies concerned elitist algorithms and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist algorithms .
Revised:  However, the majority of these studies concerned elitist EAs and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist algorithms .
Edit intent: clarity
Original:  However, the majority of these studies concerned elitist algorithms and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist algorithms .
Revised:  However, the majority of these studies concerned elitist algorithms and we do not have a clear answer on whether the same mechanisms can be applied for non-elitist EAs .
Edit intent: clarity
Original:  We show that the answer crucially depends on the success rate s (i. , e. a one-(s+1)-th success rule).
Revised:  We show that the answer crucially depends on the success rate s (i. e. a one-(s+1)-th success rule).
Edit intent: fluency
Original:  We propose a novel hybrid model for the matching policy that is a composition of a fixed rule-based function and a trainable component that can be trained from market data with no prior knowledge or expert supervision.
Revised:  We propose a novel hybrid learning model is proposed for determining the matching policy that is a composition of a fixed rule-based function and a trainable component that can be trained from market data with no prior knowledge or expert supervision.
Edit intent: clarity
Original:  We propose a novel hybrid model for the matching policy that is a composition of a fixed rule-based function and a trainable component that can be trained from market data with no prior knowledge or expert supervision.
Revised:  We propose a novel hybrid model for the matching policy that is a composition of a fixed rule-based function and a trainable component that can be trained by matching data with no prior knowledge or expert supervision.
Edit intent: clarity
Original:  The hybrid model we propose enables the learning algorithm to find an effective matching policy and simultaneously satisfy the load servicing constraints.
Revised:  The proposed hybrid learning model enables the learning algorithm to find an effective matching policy and simultaneously satisfy the load servicing constraints.
Edit intent: clarity
Original:  We show through extensive simulations that the learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival (MA) , Match to the Highest (MH) and Match to the Earliest Deadline First (MED) .
Revised:  The simulations show that the proposed learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival (MA) , Match to the Highest (MH) and Match to the Earliest Deadline First (MED) .
Edit intent: clarity
Original:  We show through extensive simulations that the learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival (MA) , Match to the Highest (MH) and Match to the Earliest Deadline First (MED) .
Revised:  We show through extensive simulations that the learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival , Match to the Highest (MH) and Match to the Earliest Deadline First (MED) .
Edit intent: fluency
Original:  We show through extensive simulations that the learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival (MA) , Match to the Highest (MH) and Match to the Earliest Deadline First (MED) .
Revised:  We show through extensive simulations that the learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival (MA) , Match to the Highest , and Match to the Earliest Deadline First (MED) .
Edit intent: coherence