wanyu/IteraTeR_v2 huggingface dataset:
Original: Deep learning (DL) can be employed as a numerical method to approximate functions.
Revised: Deep learning (DL) can be employed as a numerical method that approximates functions.
Edit intent: fluency
Original:  Results: The model is experimentally validated with data of  Italian regions, European countries and US, obtaining fitting parameters in good agreement with previous literature.
Revised:  Results: The model is experimentally validated with data of all the Italian regions, European countries and US, obtaining fitting parameters in good agreement with previous literature.
Edit intent: clarity
Original:  The strategy's effectiveness critically depends on high between-person variability in  close-range contacts.
Revised:  Effectiveness critically depends on high between-person variability in close-range contacts.
Edit intent: clarity
Original:  The strategy's effectiveness critically depends on high between-person variability in  close-range contacts.
Revised:  The strategy's effectiveness critically depends on high between-person variability in the number of close-range contacts.
Edit intent: clarity
Original:  We analyze population survey data showing that indeed the frequency of close-range contacts across individuals is characterized by a small fraction of people reporting very high frequencies of close-range contacts .
Revised:  We analyze population survey data showing that indeed the distribution of close-range contacts across individuals is characterized by a small fraction of people reporting very high frequencies of close-range contacts .
Edit intent: clarity
Original:  We analyze population survey data showing that indeed the frequency of close-range contacts across individuals is characterized by a small fraction of people reporting very high frequencies of close-range contacts .
Revised:  We analyze population survey data showing that indeed the frequency of close-range contacts across individuals is characterized by a small fraction of individuals reporting very high frequencies of close-range contacts .
Edit intent: clarity
Original:  Simulations show that targeting a small fraction of high-degree individuals dramatically improves containment.
Revised:  Simulations show that targeting hubs dramatically improves containment.
Edit intent: clarity
Original:  Our results further suggest two concrete procedures for identifying high-contact individuals: acquaintance sampling and employment-based target .
Revised:  Our results further suggest two concrete procedures for identifying high-contact individuals: acquaintance sampling and employment-based targeting .
Edit intent: clarity
Original:  Strikingly,  the average duration of contact is mostly invariant in the number of contacts, reinforcing the criticality of hubs.
Revised:  Strikingly, we find that the average duration of contact is mostly invariant in the number of contacts, reinforcing the criticality of hubs.
Edit intent: coherence
Original:  We simulate a population with empirical contact distributions coming out of lockdown . Simulations show that targeting hubs dramatically improves containment.
Revised:  We simulate a population embedded in a network with empirically observed contact frequencies . Simulations show that targeting hubs dramatically improves containment.
Edit intent: clarity
Original:  The computational problems of determining the possible and necessary winners and unique winners have been studied in depth, culminating in a full classification of the class of ``pure'' positional scoring rules into tractable and intractable ones for each problem.
Revised:  The computational problems of determining the possible and necessary winners and unique winners have been studied in depth, culminating in a full classification of the class of "pure" positional scoring rules into tractable and intractable ones for each problem.
Edit intent: fluency
Original:  Tha above problems are all special cases of reasoning about the range of possible positions of a candidate under different tie breakers.
Revised:  The above problems are all special cases of reasoning about the range of possible positions of a candidate under different tie breakers.
Edit intent: fluency
Original: In an election via a positional scoring rule, each candidate receives from each voter a score that is determined only by the position of the candidate in the voter's total ordering of the candidates .
Revised: Various voting rules are based on ranking the candidates by scores induced by aggregating voter preferences .
Edit intent: clarity
Original:   When voter preferences are known in an incomplete manner as partial orders, a candidate can be a possible/necessary (unique) winner based on the possibilities of completing the partial votes.
Revised:  When voter preferences are known in an incomplete manner as partial orders, a candidate can be a possible/necessary winner based on the possibilities of completing the partial votes.
Edit intent: clarity
Original:  The computational problems of determining the possible and necessary winners and unique winners have been studied in depth, culminating in a full classification of the class of "pure" positional scoring rules into tractable and intractable ones for each problem.
Revised:  Past research has studied in depth the computational problems of determining the possible and necessary winners and unique winners have been studied in depth, culminating in a full classification of the class of "pure" positional scoring rules into tractable and intractable ones for each problem.
Edit intent: clarity
Original:  Our main result establishes that the minimal and maximal positions are hard to compute ( NP-hard ) for every positional scoring rule , pure or not.
Revised:  Our main result establishes that the minimal and maximal positions is NP-hard ) for every positional scoring rule , pure or not.
Edit intent: clarity
Original:  We do show, however, that tractability can be retained when reasoning about the top-k and the bottom-k positions for a fixed k.
Revised:  Tractability can be retained when reasoning about the top-k and the bottom-k positions for a fixed k.
Edit intent: coherence
Original:  We do show, however, that tractability can be retained when reasoning about the top-k and the bottom-k positions for a fixed k.
Revised:  We do show, however, that tractability can be retained when reasoning about the top-k positions for a fixed k.
Edit intent: clarity
Original:  Sham is designed to be well suited as both a compilation target for other DSLs embedded in Racket as well as a language for transparently replacing DSL support code with faster versions.
Revised:  Sham is well suited as both a compilation target for other DSLs embedded in Racket as well as a language for transparently replacing DSL support code with faster versions.
Edit intent: clarity
Original:  Sham is designed to be well suited as both a compilation target for other DSLs embedded in Racket as well as a language for transparently replacing DSL support code with faster versions.
Revised:  Sham is designed to be well suited as both a compilation target for other embedded DSLs and for transparently replacing DSL support code with faster versions.
Edit intent: clarity
Original: Centralized/Cloud Radio Access Network (C-RAN) comes into prominence to reduce the rising energy consumptions and maintenance difficulties of  next-generation networks.
Revised: Centralized/Cloud Radio Access Network (C-RAN) comes into prominence to reduce the rising energy consumptions and maintenance difficulties of the next-generation networks.
Edit intent: fluency
Original:  However, C-RAN has strict delay requirements, and it needs a large fronthaul bandwidth.
Revised:  However, C-RAN has strict delay requirements, and it needs large fronthaul bandwidth.
Edit intent: fluency
Original:  Our experimental results show that our solution surpasses classical disjoint approaches for any diversity in a city population and the geographical location of this city .
Revised:  Our experimental results show that our solution surpasses classical disjoint approaches for all studied cases .
Edit intent: clarity
Original:  To the best of our knowledge, the improved complexity is exponentially better than best known result of O(5^n) in 21%DIFDELCMD < ] %%% for complementing B\"uchi automata with a finite degree of ambiguity .
Revised:  To the best of our knowledge, the improved complexity is exponentially better than best known result of O(5^n) in %DIFDELCMD < ] %%% for complementing B\"uchi automata with a finite degree of ambiguity .
Edit intent: clarity
Original:  To the best of our knowledge, the improved complexity is exponentially better than best known result of O(5^n) in 21%DIFDELCMD < ] %%% for complementing B\"uchi automata with a finite degree of ambiguity .
Revised:  To the best of our knowledge, the improved complexity is exponentially better than best known result of O(5^n) in 21%DIFDELCMD < ] %%% .
Edit intent: coherence
Original: Capturing and processing video is increasingly common as cameras and networks improve and become cheaper .
Revised: Capturing and processing video is increasingly common as cameras become cheaper to deploy .
Edit intent: clarity
Original:  At the same time, algorithms for rich scene understanding and object detection have progressed greatly in the last decade.
Revised:  At the same time, rich video understanding methods have progressed greatly in the last decade.
Edit intent: clarity
Original:  Because state of the art vision algorithms to interpret scenes and recognize objects are slow and expensive, our ability to process even simple ad-hoc selection queries (` find 100 example traffic lights in dashboard camera video') over this accumulated data lags far behind our ability to collect it.
Revised:  Because state-of-the-art object detection methods are slow and expensive, our ability to process even simple ad-hoc selection queries (` find 100 example traffic lights in dashboard camera video') over this accumulated data lags far behind our ability to collect it.
Edit intent: clarity
Original:  Because state of the art vision algorithms to interpret scenes and recognize objects are slow and expensive, our ability to process even simple ad-hoc selection queries (` find 100 example traffic lights in dashboard camera video') over this accumulated data lags far behind our ability to collect it.
Revised:  Because state of the art vision algorithms to interpret scenes and recognize objects are slow and expensive, our ability to process even simple ad-hoc object search queries (' find 100 example traffic lights in dashboard camera video') over this accumulated data lags far behind our ability to collect it.
Edit intent: clarity
Original:  Because state of the art vision algorithms to interpret scenes and recognize objects are slow and expensive, our ability to process even simple ad-hoc selection queries (` find 100 example traffic lights in dashboard camera video') over this accumulated data lags far behind our ability to collect it.
Revised:  Because state of the art vision algorithms to interpret scenes and recognize objects are slow and expensive, our ability to process even simple ad-hoc selection queries (` find 100 traffic lights in dashboard camera video') over this accumulated data lags far behind our ability to collect it.
Edit intent: clarity
Original:  Because state of the art vision algorithms to interpret scenes and recognize objects are slow and expensive, our ability to process even simple ad-hoc selection queries (` find 100 example traffic lights in dashboard camera video') over this accumulated data lags far behind our ability to collect it.
Revised:  Because state of the art vision algorithms to interpret scenes and recognize objects are slow and expensive, our ability to process even simple ad-hoc selection queries (` find 100 example traffic lights in dashcam video') over this accumulated data lags far behind our ability to collect it.
Edit intent: fluency
Original:  Sampling image frames from the videos is a reasonable default strategy for these types of queries queries , however, the ideal sampling rate is both data and query dependent.
Revised:  Sampling image frames from the videos is a reasonable default strategy for these types of queries , however, the ideal sampling rate is both data and query dependent.
Edit intent: fluency
Original:  We introduce ExSample  a low cost framework for ad-hoc, unindexed video search which quickly processes selection queries by adapting the amount and location of sampled frames to the data and the query being processed.
Revised:  We introduce ExSample , a low cost framework for ad-hoc, unindexed video search which quickly processes selection queries by adapting the amount and location of sampled frames to the data and the query being processed.
Edit intent: fluency
Original:  We introduce ExSample  a low cost framework for ad-hoc, unindexed video search which quickly processes selection queries by adapting the amount and location of sampled frames to the data and the query being processed.
Revised:  We introduce ExSample a low cost framework for object search over unindexed video that quickly processes search queries by adapting the amount and location of sampled frames to the data and the query being processed.
Edit intent: clarity
Original:  We introduce ExSample  a low cost framework for ad-hoc, unindexed video search which quickly processes selection queries by adapting the amount and location of sampled frames to the data and the query being processed.
Revised:  We introduce ExSample a low cost framework for ad-hoc, unindexed video search which quickly processes selection queries by adapting the amount and location of sampled frames to the particular data and query being processed.
Edit intent: clarity
Original:  ExSample prioritizes which frames within a video repository are processed in order to quickly identify portions of the video that contain objects of interest.
Revised:  ExSample prioritizes the processing of frames in a video repository are processed in order to quickly identify portions of the video that contain objects of interest.
Edit intent: clarity
Original:  ExSample prioritizes which frames within a video repository are processed in order to quickly identify portions of the video that contain objects of interest.
Revised:  ExSample prioritizes which frames within a video repository so that processing is focused in portions of video that most likely contain objects of interest.
Edit intent: clarity
Original:  ExSample continually re-prioritizes which regions of video to sample from based on feedback from previous samples .
Revised:  It continually re-prioritizes which regions of video to sample from based on feedback from previous samples .
Edit intent: fluency
Original:  ExSample continually re-prioritizes which regions of video to sample from based on feedback from previous samples .
Revised:  ExSample continually re-prioritizes processing based on feedback from previous samples .
Edit intent: clarity
Original:  ExSample continually re-prioritizes which regions of video to sample from based on feedback from previous samples .
Revised:  ExSample continually re-prioritizes which regions of video to sample from based on feedback from previously processed frames .
Edit intent: clarity
Original:  On large, real-world video datasets ExSample reduces processing time by up to 4x over an efficient random sampling baseline and by several orders of magnitude versus state-of-the-art methods which train specialized models for each query . ExSample is  a key component in building cost-efficient video data management systems.
Revised:  On large, real-world datasets, ExSample reduces processing time by up to 4x over an efficient random sampling baseline and by several orders of magnitude versus state-of-the-art methods which train specialized models for each query . ExSample is a key component in building cost-efficient video data management systems.
Edit intent: fluency
Original:  On large, real-world video datasets ExSample reduces processing time by up to 4x over an efficient random sampling baseline and by several orders of magnitude versus state-of-the-art methods which train specialized models for each query . ExSample is  a key component in building cost-efficient video data management systems.
Revised:  On large, real-world video datasets ExSample reduces processing time by up to 4x over an efficient random sampling baseline and by several orders of magnitude over state-of-the-art methods which train specialized models for each query . ExSample is a key component in building cost-efficient video data management systems.
Edit intent: clarity
Original:  On large, real-world video datasets ExSample reduces processing time by up to 4x over an efficient random sampling baseline and by several orders of magnitude versus state-of-the-art methods which train specialized models for each query . ExSample is  a key component in building cost-efficient video data management systems.
Revised:  On large, real-world video datasets ExSample reduces processing time by up to 4x over an efficient random sampling baseline and by several orders of magnitude versus state-of-the-art methods which train specialized models for each query . ExSample is thus a key component in building cost-efficient video data management systems.
Edit intent: coherence
Original:  We investigate how to encode table content considering the  structure and input length limit of BERT.
Revised:  We investigate how to encode table content considering the table structure and input length limit of BERT.
Edit intent: fluency
Original:  In addition, this paper provides the Matlab code of SEMDOT for educational and academic purposes.
Edit intent: coherence
Original:  The basic framework of a new element-based continuum algorithm has been proposed in this paper.
Revised:  The basic framework of a new element-based continuum algorithm is proposed in this paper.
Edit intent: clarity
Original:  The named Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT)  algorithm uses elemental volume fractions which depend on the densities of grid points as design variables rather than elemental densities.
Revised:  Named Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm uses elemental volume fractions which depend on the densities of grid points as design variables rather than elemental densities.
Edit intent: coherence
Original:  The named Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT)  algorithm uses elemental volume fractions which depend on the densities of grid points as design variables rather than elemental densities.
Revised:  The named Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) , the algorithm uses elemental volume fractions which depend on the densities of grid points as design variables rather than elemental densities.
Edit intent: clarity
Original:  SEMDOT is not only capable of obtaining optimized topologies with smooth and clear boundaries but also generally shows a better performance compared to other topology optimization methods.
Revised:  Several numerical examples are studied to demonstrate the application and effectiveness of SEMDOT. In these examples, SEMDOT proved to be capable of obtaining optimized topologies with smooth and clear boundaries but also generally shows a better performance compared to other topology optimization methods.
Edit intent: coherence
Original:  SEMDOT is not only capable of obtaining optimized topologies with smooth and clear boundaries but also generally shows a better performance compared to other topology optimization methods.
Revised:  SEMDOT is not only capable of obtaining optimized topologies with smooth and clear boundaries showing better or comparable performance compared to other topology optimization methods.
Edit intent: clarity
Original:  Then, the benefits of introducing two filters in this algorithm are shown.
Revised:  Then, the benefits of introducing multiple filtering steps in this algorithm are shown.
Edit intent: clarity
Original:  Finally, comparisons are conducted to exhibit the differences of SEMDOT with some well-established element-based algorithms  .
Revised:  Finally, comparisons are conducted to exhibit the differences between SEMDOT and some well-established element-based algorithms .
Edit intent: clarity
Original: In this paper, we investigate the combination of synthesis techniques and learning techniques to obtain safe and near optimal schedulers for a preemptible task scheduling problem.
Revised: In this paper, we investigate the combination of synthesis techniques and learning techniques to obtain safe and near-optimal schedulers for a preemptible task scheduling problem.
Edit intent: fluency
Original:  In this paper, we propose an extension of multi-teacher distillation methods to joint ctc-atention end-to-end ASR systems.
Revised:  In this paper, we propose an extension of multi-teacher distillation methods to joint CTC-attention end-to-end ASR systems.
Edit intent: fluency
Original:  The core intuition behind both is to integrate the error rate metric to the teacher selection rather than solely focusing on the observed losses.
Revised:  The core intuition behind them is to integrate the error rate metric to the teacher selection rather than solely focusing on the observed losses.
Edit intent: clarity
Original:  We evaluated these strategies under a selection of training procedures on the TIMIT phoneme recognition task and observed promising error rate for these strategies compared to a common baseline .
Revised:  We evaluated these strategies under a selection of training procedures on the TIMIT phoneme recognition task and observed promising error rates for these strategies compared to a common baseline .
Edit intent: fluency
Original:  We evaluated these strategies under a selection of training procedures on the TIMIT phoneme recognition task and observed promising error rate for these strategies compared to a common baseline .
Revised:  We evaluated these strategies under a selection of training procedures on the TIMIT phoneme recognition task and observed promising error rate for these strategies compared to common baselines .
Edit intent: fluency
Original:  Indeed, the best obtained phoneme error rate of 16.4 \% represents a state-of-the-art score for end-to-end ASR systems .
Revised:  Indeed, the best obtained phoneme error rate of 16.4 \% represents a state-of-the-art score .
Edit intent: clarity
Original:  This way, we directly distillate and optimize the student toward the relevant metric for speech recognition.
Revised:  In this way, we directly distillate and optimize the student toward the relevant metric for speech recognition.
Edit intent: coherence
Original:  This way, we directly distillate and optimize the student toward the relevant metric for speech recognition.
Revised:  This way, we directly distill and optimize the student toward the relevant metric for speech recognition.
Edit intent: fluency
Original:  We evaluated these strategies under a selection of training procedures on the TIMITphoneme recognition task and observed promising error rates for these strategies compared to common baselines.
Revised:  We evaluate these strategies under a selection of training procedures on the TIMITphoneme recognition task and observed promising error rates for these strategies compared to common baselines.
Edit intent: fluency
Original: Common end-to-end models like CTC or encoder-decoder-attention models use characters or subword units like BPEas the output labels.
Revised: Following the rationale of end-to-end models like CTC or encoder-decoder-attention models use characters or subword units like BPEas the output labels.
Edit intent: clarity
Original:  We perform the experiments on Switchboard 300h and we can show that our phoneme-based models are competitive to the grapheme-based models .
Revised:  We perform the experiments on Switchboard 300h and we can show that our phoneme-based modeling is competitive to grapheme-based models .
Edit intent: clarity