wanyu/IteraTeR_v2 huggingface dataset:
Original:  We also simulated evolution of slowly translating codons, finding that slowing translation at certain positions enhances cotranslational folding.
Revised:  We also simulated evolution of slowly translating codons, finding that slower translation speeds at certain positions enhances cotranslational folding.
Edit intent: clarity
Original:  The Flink does not natively support the efficient processing of spatial data streams, which is the requirement of many applications dealing with the spatial data.
Revised:  Flink does not natively support the efficient processing of spatial data streams, which is the requirement of many applications dealing with the spatial data.
Edit intent: coherence
Original:  The Flink does not natively support the efficient processing of spatial data streams, which is the requirement of many applications dealing with the spatial data.
Revised:  The Flink does not natively support efficient processing of spatial data streams, which is the requirement of many applications dealing with the spatial data.
Edit intent: fluency
Original:  The Flink does not natively support the efficient processing of spatial data streams, which is the requirement of many applications dealing with the spatial data.
Revised:  The Flink does not natively support the efficient processing of spatial data streams, which is the requirement of many applications dealing with spatial data.
Edit intent: fluency
Original:  Hence this work presents GeoFlink, which extends the Apache Flink to support spatial data types, index and continuous queries.
Revised:  Hence this work presents GeoFlink, which extends Apache Flink to support spatial data types, index and continuous queries.
Edit intent: clarity
Original:  To enable the efficient processing of continuous spatial queries and for the effective data distribution among the Flink cluster nodes, a gird-based index is introduced.
Revised:  To enable efficient processing of continuous spatial queries and for the effective data distribution among the Flink cluster nodes, a gird-based index is introduced.
Edit intent: fluency
Original:  To enable the efficient processing of continuous spatial queries and for the effective data distribution among the Flink cluster nodes, a gird-based index is introduced.
Revised:  To enable the efficient processing of continuous spatial queries and for the effective data distribution among the Flink cluster nodes, a grid-based index is introduced.
Edit intent: fluency
Original:  The grid index enables the pruning of the spatial objects which cannot be part of a spatial query result and thus can guarantee efficient query processing, similarly it helps in preserving the spatial data proximity, hence resulting in effective data distribution.
Revised:  The grid index enables the pruning of spatial objects which cannot be part of a spatial query result and thus can guarantee efficient query processing, similarly it helps in preserving the spatial data proximity, hence resulting in effective data distribution.
Edit intent: fluency
Original:  The grid index enables the pruning of the spatial objects which cannot be part of a spatial query result and thus can guarantee efficient query processing, similarly it helps in preserving the spatial data proximity, hence resulting in effective data distribution.
Revised:  The grid index enables the pruning of the spatial objects which cannot be part of a spatial query result and thus can guarantee efficient query processing, similarly it helps in preserving spatial data proximity, hence resulting in effective data distribution.
Edit intent: fluency
Original:  An extensive experimental study on real spatial data streams show that GeoFlink achieves several orders of magnitude higher query performance than the ordinary distributed approaches.
Revised:  An extensive experimental study on real spatial data streams show that GeoFlink achieves several orders of magnitude higher query performance than other ordinary distributed approaches.
Edit intent: clarity
Original: Apache Flink is an open-source system for the scalable processing of batch and streaming data.
Revised: Apache Flink is an open-source system for scalable processing of batch and streaming data.
Edit intent: fluency
Original:  Flink does not natively support efficient processing of spatial data streams, which is the requirement of many applications dealing with spatial data.
Revised:  Flink does not natively support efficient processing of spatial data streams, which is a requirement of many applications dealing with spatial data.
Edit intent: fluency
Original:  Besides Flink, other scalable spatial data processing platforms including GeoSpark, Spatial Hadoop, GeoMesa and Parallel Secondo do not support streaming workloads and can only handle static/batch workloads.
Revised:  Besides Flink, other scalable spatial data processing platforms including GeoSpark, Spatial Hadoop, etc. do not support streaming workloads and can only handle static/batch workloads.
Edit intent: coherence
Original:  Hence this work presents GeoFlink, which extends Apache Flink to support spatial data types, index and continuous queries  .
Revised:  To fill this gap, we present GeoFlink, which extends Apache Flink to support spatial data types, index and continuous queries .
Edit intent: clarity
Original:  Hence this work presents GeoFlink, which extends Apache Flink to support spatial data types, index and continuous queries  .
Revised:  Hence this work presents GeoFlink, which extends Apache Flink to support spatial data types, indexes and continuous queries .
Edit intent: fluency
Original:  To enable  efficient processing of continuous spatial queries and for the effective data distribution among the Flink cluster nodes, a grid-based index is introduced.
Revised:  To enable the efficient processing of continuous spatial queries and for the effective data distribution among the Flink cluster nodes, a grid-based index is introduced.
Edit intent: fluency
Original:  To enable  efficient processing of continuous spatial queries and for the effective data distribution among the Flink cluster nodes, a grid-based index is introduced.
Revised:  To enable efficient processing of continuous spatial queries and for the effective data distribution across Flink cluster nodes, a grid-based index is introduced.
Edit intent: fluency
Original:  To enable  efficient processing of continuous spatial queries and for the effective data distribution among the Flink cluster nodes, a grid-based index is introduced.
Revised:  To enable efficient processing of continuous spatial queries and for the effective data distribution among the Flink cluster nodes, a gird-based index is introduced.
Edit intent: fluency
Original:  The grid index enables the pruning of spatial objects which cannot be part of a spatial query result and thus can guarantee efficient query processing, similarly it helps in preserving spatial data proximity, hence resulting in effective data distribution.
Edit intent: coherence
Original:  An extensive experimental study on real spatial data streams show that GeoFlink achieves several orders of magnitude higher query performance than other ordinary distributed approaches .
Revised:  An extensive experimental study on real spatial data streams shows that GeoFlink achieves several orders of magnitude higher query performance than other ordinary distributed approaches .
Edit intent: fluency
Original:  An extensive experimental study on real spatial data streams show that GeoFlink achieves several orders of magnitude higher query performance than other ordinary distributed approaches .
Revised:  An extensive experimental study on real spatial data streams show that GeoFlink achieves significantly higher query throughput than ordinary Flink processing .
Edit intent: clarity
Original: One major bottleneck in the ongoing Covid-19 pandemic is the limited number of critical care beds.
Revised: One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds.
Edit intent: fluency
Original:  To derive future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth rates.
Revised:  To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth rates.
Edit intent: clarity
Original:  Our model is then used to predict future ICU load assuming  a continued exponential phase with varying growth rates (0-15\%) .
Revised:  Our model is then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0-15\%) .
Edit intent: fluency
Original: Operational cellular networks rely on closed and inflexible infrastructure tightly controlled by a handful of vendors.
Revised: Current cellular networks rely on closed and inflexible infrastructure tightly controlled by a handful of vendors.
Edit intent: clarity
Original:  As a consequence, configuration requires vendor support and lengthy manual operations, which prevent Telco Operators (TOs) from unlocking the full network potential and from being able to perform fine grained performance optimization, especially on a per-user basis.
Revised:  Their configuration requires vendor support and lengthy manual operations, which prevent Telco Operators (TOs) from unlocking the full network potential and from being able to perform fine grained performance optimization, especially on a per-user basis.
Edit intent: clarity
Original:  As a consequence, configuration requires vendor support and lengthy manual operations, which prevent Telco Operators (TOs) from unlocking the full network potential and from being able to perform fine grained performance optimization, especially on a per-user basis.
Revised:  As a consequence, configuration requires vendor support and lengthy manual operations, which prevent Telco Operators (TOs) from unlocking the full network potential and from performing fine grained performance optimization, especially on a per-user basis.
Edit intent: clarity
Original:  To address this key issue , this paper introduces CellOS, a fully automated optimization and management framework for cellular networks that requires negligible intervention ("zero-touch").
Revised:  To address these key issues , this paper introduces CellOS, a fully automated optimization and management framework for cellular networks that requires negligible intervention ("zero-touch").
Edit intent: fluency
Original:  CellOS leverages softwarization and automatic optimization principles to bridge the gap between Software-Defined Networking (SDN) and cross-layer optimization.
Revised:  CellOS leverages softwarization and automatic optimization principles to bridge Software-Defined Networking (SDN) and cross-layer optimization.
Edit intent: clarity
Original:  (ii) allows TOs to define high-level control objectives to dictate the desired network behavior without requiring knowledge of optimization techniques, and (iii) automatically generates and executes distributed control programs for the simultaneous optimization of heterogeneous control objectives on different network slices.
Revised:  (ii) allows TOs to define high-level control objectives to dictate the desired network behavior without requiring knowledge of optimization techniques, and (iii) automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on different network slices.
Edit intent: fluency
Original:  (ii) allows TOs to define high-level control objectives to dictate the desired network behavior without requiring knowledge of optimization techniques, and (iii) automatically generates and executes distributed control programs for the simultaneous optimization of heterogeneous control objectives on different network slices.
Revised:  (ii) allows TOs to define high-level control objectives to dictate the desired network behavior without requiring knowledge of optimization techniques, and (iii) automatically generates and executes distributed control programs for the simultaneous optimization of heterogeneous control objectives on multiple network slices.
Edit intent: clarity
Original:  Extensive evaluation demonstrates that CellOS outperforms existing solutions on key performance metrics, including throughput (up to 86\%  ), energy efficiency (up to 84\%) and fairness (up to 29\%).
Revised:  Our investigation shows that CellOS outperforms existing solutions on key performance metrics, including throughput (up to 86\% ), energy efficiency (up to 84\%) and fairness (up to 29\%).
Edit intent: clarity
Original:  Extensive evaluation demonstrates that CellOS outperforms existing solutions on key performance metrics, including throughput (up to 86\%  ), energy efficiency (up to 84\%) and fairness (up to 29\%).
Revised:  Extensive evaluation demonstrates that CellOS outperforms existing solutions on key metrics, including throughput (up to 86\% ), energy efficiency (up to 84\%) and fairness (up to 29\%).
Edit intent: clarity
Original: As a strong complementary of additive manufacturing, multi-directional 3D printing has the capability of decreasing or eliminating the need for support structures.
Revised: Multi-directional 3D printing has the capability of decreasing or eliminating the need for support structures.
Edit intent: clarity
Original:  Different printing directions are employed in different regions so that a model can be fabricated with tremendously less supports (or even no support in many cases).
Revised:  Different printing directions are employed in different regions to fabricate a model with tremendously less supports (or even no support in many cases).
Edit intent: coherence
Original:  Different printing directions are employed in different regions so that a model can be fabricated with tremendously less supports (or even no support in many cases).
Revised:  Different printing directions are employed in different regions so that a model can be fabricated with tremendously less support (or even no support in many cases).
Edit intent: fluency
Original: To obtain optimized decomposition, a large beam width needs to be used in the search algorithm, which therefore leads to a very time-consuming computation.
Revised: To obtain optimized decomposition, a large beam width needs to be used in the search algorithm, leading to a very time-consuming computation.
Edit intent: coherence
Original:  In this paper, we propose a learning framework that can accelerate the beam-guided search by using only 1/2 of the original beam width to obtain results with similar quality.
Revised:  In this paper, we propose a learning framework that can accelerate the beam-guided search by using a smaller number of the original beam width to obtain results with similar quality.
Edit intent: clarity
Original:  Specifically, we train a classifier for each pair of candidate clipping planes based on six newly proposed feature metrics from the results of beam-guided search with large beam width .
Revised:  Specifically, we use the results of beam-guided search with large beam width to train a scoring function for candidate clipping planes based on six newly proposed feature metrics from the results of beam-guided search with large beam width .
Edit intent: clarity
Original:  Specifically, we train a classifier for each pair of candidate clipping planes based on six newly proposed feature metrics from the results of beam-guided search with large beam width .
Revised:  Specifically, we train a classifier for each pair of candidate clipping planes based on six newly proposed feature metrics .
Edit intent: clarity
Original:  With the help of these feature metrics, both the current and the sequence-dependent information are captured by the classifier to score candidates of clipping.
Revised:  With the help of these feature metrics, both the current and the sequence-dependent information are captured by the neural network to score candidates of clipping.
Edit intent: clarity
Original:  We test and demonstrate the performance of our accelerated decomposition on a large dataset of models for 3D printing.
Revised:  We test and demonstrate our accelerated decomposition on a large dataset of models for 3D printing.
Edit intent: clarity
Original:  Our result suggests that  a higher percentage of symptomatic infected in a region leads to a large number of reduction in notified cases and deaths due to different lock-down scenario .
Revised:  Our result suggests that lockdown will be effective in those locations where a higher percentage of symptomatic infected in a region leads to a large number of reduction in notified cases and deaths due to different lock-down scenario .
Edit intent: clarity
Original:  These building blocks are similar to traditional database operators , but at a finer granularity , so we call them sub-operators.
Revised:  These building blocks are similar to traditional database operators but at a finer granularity , so we call them sub-operators.
Edit intent: fluency
Original:  Modularis is an order of magnitude faster on SQL-style analytics compared to a commonly used framework for generic data processing (Presto) and on par with a commercial cluster database ( MemSQL) .
Revised:  Modularis is an order of magnitude faster on SQL-style analytics compared to a commonly used framework for generic data processing (Presto) , a commercial cluster database ( MemSQL) .
Edit intent: coherence
Original: This paperanalyzes the generalization of speech enhancement algorithms based on deep neural networks (DNNs) with respect to (1) the chosen features, (2) the size and diversity of the training data, and (3 ) different network architectures .
Revised: In this paper, we address the generalization of speech enhancement algorithms based on deep neural networks (DNNs) with respect to (1) the chosen features, (2) the size and diversity of the training data, and (3 ) different network architectures .
Edit intent: clarity
Original: This paperanalyzes the generalization of speech enhancement algorithms based on deep neural networks (DNNs) with respect to (1) the chosen features, (2) the size and diversity of the training data, and (3 ) different network architectures .
Revised: This paperanalyzes the generalization of speech enhancement algorithms based on deep neural networks (DNNs) with respect to (1) the size and diversity of the training data, and (3 ) different network architectures .
Edit intent: clarity
Original: This paperanalyzes the generalization of speech enhancement algorithms based on deep neural networks (DNNs) with respect to (1) the chosen features, (2) the size and diversity of the training data, and (3 ) different network architectures .
Revised: This paperanalyzes the generalization of speech enhancement algorithms based on deep neural networks (DNNs) with respect to (1) the chosen features, (2) the size and diversity of the training data, (2 ) different network architectures .
Edit intent: fluency
Original:  On the other hand, via experimental results and an analysis using t-distributed stochastic neighbor embedding (t-SNE) we show that SNR-NAT features are robust even if the size and the diversity of the input data are limited.
Revised:  Furthermore, we show via experimental results and an analysis using t-distributed stochastic neighbor embedding (t-SNE) we show that SNR-NAT features are robust even if the size and the diversity of the input data are limited.
Edit intent: coherence
Original:  On the other hand, via experimental results and an analysis using t-distributed stochastic neighbor embedding (t-SNE) we show that SNR-NAT features are robust even if the size and the diversity of the input data are limited.
Revised:  On the other hand, via experimental results and an analysis using t-distributed stochastic neighbor embedding (t-SNE) that the proposed SNR-NAT features are robust even if the size and the diversity of the input data are limited.
Edit intent: clarity
Original: The current tsunami of deep learning (the hyper-vitamined return of artificial neural networks) applies not only to traditional statistical machine learning tasks: prediction and classification (e.g., for weather prediction and pattern recognition), but has already conquered other areas, such as translation.
Revised: The current wave of deep learning (the hyper-vitamined return of artificial neural networks) applies not only to traditional statistical machine learning tasks: prediction and classification (e.g., for weather prediction and pattern recognition), but has already conquered other areas, such as translation.
Edit intent: clarity
Original:  A growing area of application is the generation of creative content : in particular the case of music, the topic of this paper.
Revised:  A growing area of application is the generation of creative content , notably the case of music, the topic of this paper.
Edit intent: clarity
Original:  This article provides a survey of music generation based on deep learning techniques.
Revised:  This paper provides a tutorial on music generation based on deep learning techniques.
Edit intent: clarity
Original:  After a short introduction to the topic illustrated by a recent exemple, the article analyses some early works from the late 1980s using artificial neural networks for music generation and how their pioneering contributions foreshadowed current techniques.
Revised:  After a short introduction to the topic illustrated by a recent exemple, the paper analyzes some early works from the late 1980s using artificial neural networks for music generation and how their pioneering contributions foreshadowed current techniques.
Edit intent: fluency
Original:  After a short introduction to the topic illustrated by a recent exemple, the article analyses some early works from the late 1980s using artificial neural networks for music generation and how their pioneering contributions foreshadowed current techniques.
Revised:  After a short introduction to the topic illustrated by a recent exemple, the article analyses some early works from the late 1980s using artificial neural networks for music generation and how their pioneering contributions have prefigured current techniques.
Edit intent: clarity
Original:  We motivate our particular choice by demonstrating (i) how commonly understood and expected properties of an architecture can be suitably defined or derived within our formalization, and (ii) how our concept is fully compatible with real life (business) architectures . Based on our fundamental definitions we further develop a rigorous notion of architectural similarity based on the notion of "homomorphisms " between architectures .
Revised:  We motivate our particular choice by demonstrating (i) how commonly understood and expected properties of an architecture can be suitably defined or derived within our formalization, (ii) how our concept is fully compatible with real life (business) architectures . Based on our fundamental definitions we further develop a rigorous notion of architectural similarity based on the notion of "homomorphisms " between architectures .
Edit intent: fluency
Original:  We motivate our particular choice by demonstrating (i) how commonly understood and expected properties of an architecture can be suitably defined or derived within our formalization, and (ii) how our concept is fully compatible with real life (business) architectures . Based on our fundamental definitions we further develop a rigorous notion of architectural similarity based on the notion of "homomorphisms " between architectures .
Revised:  We motivate our particular choice by demonstrating (i) how commonly understood and expected properties of an architecture can be suitably defined or derived within our formalization, and (ii) how our concept is fully compatible with real life (business) architectures . Based on our fundamental definitions we further develop a rigorous notion of architectural similarity based on the notion of "homomorphisms " between architectures .
Edit intent: fluency