wanyu/IteraTeR_v2 huggingface dataset:
Original:  We demonstrate experimentally that this method scales well to any map size, providing a better quality heuristic, thus more efficient A* search, for fastest path queries between points at all distances - especially small and medium range - relative to other known heuristics .
Revised:  We demonstrate experimentally that this method scales well to any map size, providing a better quality heuristic, thus more efficient A* search, for fastest-path queries between points at all distances - especially small and medium range - relative to other known heuristics .
Edit intent: fluency
Original:  We present a novel method to compute an effective admissible heuristic for the fastest-path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths.
Revised:  We present a method to compute an effective admissible heuristic for the fastest-path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths.
Edit intent: clarity
Original:  We present a novel method to compute an effective admissible heuristic for the fastest-path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths.
Revised:  We present a novel method to compute an effective heuristic for the fastest-path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths.
Edit intent: clarity
Original:  We present a novel method to compute an effective admissible heuristic for the fastest-path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths.
Revised:  We present a novel method to compute an effective admissible heuristic for the fastest path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths.
Edit intent: clarity
Original:  Our basic method - called the Hierarchical Separator Heuristic (HSH) - is based on a hierarchical set of linear separators of the map represented by a binary tree, where all the separators are parallel lines in a specific direction .
Revised:  Our method is based on a hierarchical set of linear separators of the map represented by a binary tree, where all the separators are parallel lines in a specific direction .
Edit intent: coherence
Original:  Our basic method - called the Hierarchical Separator Heuristic (HSH) - is based on a hierarchical set of linear separators of the map represented by a binary tree, where all the separators are parallel lines in a specific direction .
Revised:  Our basic method - called the Hierarchical Separator Heuristic (HSH) - is based on a hierarchical set of linear separators of the map represented by two binary trees .
Edit intent: clarity
Original:  A preprocessing step computes a short vector of values per road junction based on the separator tree , which is then stored with the map and used to efficiently compute the heuristic at the online query stage.
Revised:  A preprocessing step computes a short vector of values per road junction based on the separator trees , which is then stored with the map and used to efficiently compute the heuristic at the online query stage.
Edit intent: fluency
Original:  We demonstrate experimentally that this method scales well to any map size, providing a high-quality heuristic, thus very efficient A* search, for fastest-path queries between points at all distances - especially small and medium range .
Revised:  We demonstrate experimentally that this method scales well to any map size, providing a better quality heuristic, thus very efficient A* search, for fastest-path queries between points at all distances - especially small and medium range .
Edit intent: clarity
Original:  We demonstrate experimentally that this method scales well to any map size, providing a high-quality heuristic, thus very efficient A* search, for fastest-path queries between points at all distances - especially small and medium range .
Revised:  We demonstrate experimentally that this method scales well to any map size, providing a high-quality heuristic, thus very efficient A* search, for fastest path queries between points at all distances - especially small and medium range .
Edit intent: fluency
Original: Due to the ever-growing concerns on the air pollution and energy security, many cities have started to update their taxi fleets with electric ones.
Revised: Due to the ever-growing concerns on air pollution and energy security, many cities have started to update their taxi fleets with electric ones.
Edit intent: fluency
Original: We introduce a Learning Direct Optimization  method for the refinement of a latent variable model that describes input image x.
Revised: We develop a Learning Direct Optimization method for the refinement of a latent variable model that describes input image x.
Edit intent: clarity
Original:  Our goal is to explain a single image x with a 3D computer graphics model having scene graph latent variables z (such as object appearance, camera position).
Revised:  Our goal is to explain a single image x with an interpretable 3D computer graphics model having scene graph latent variables z (such as object appearance, camera position).
Edit intent: clarity
Original:  We apply the LiDO to a realistic synthetic dataset, and show that the method  transfers to work well with real images.
Revised:  We apply LiDO to a realistic synthetic dataset, and show that the method transfers to work well with real images.
Edit intent: coherence
Original: In recent years, Log-Structured Merge-trees (LSM-trees) have been widely adopted for use in the storage layer of modern NoSQL systems.
Revised: Recently, the Log-Structured Merge-trees (LSM-trees) have been widely adopted for use in the storage layer of modern NoSQL systems.
Edit intent: clarity
Original: In recent years, Log-Structured Merge-trees (LSM-trees) have been widely adopted for use in the storage layer of modern NoSQL systems.
Revised: In recent years, Log-Structured Merge-tree (LSM-tree) has been widely adopted for use in the storage layer of modern NoSQL systems.
Edit intent: fluency
Original:  Because of this, there have been a large number of research efforts, from both the database community and the  systems community, that try to improve various aspects of LSM-trees.
Revised:  Because of this, there have been a large number of research efforts, from both the database community and the operating systems community, that try to improve various aspects of LSM-trees.
Edit intent: clarity
Original:  In this paper, we provide a survey of recent LSM efforts so that readers can learn the state of the art in LSM-based storage techniques.
Revised:  In this paper, we provide a survey of recent LSM efforts so that readers can learn the state-of-the-art in LSM-based storage techniques.
Edit intent: fluency
Original:  We provide a general taxonomy to classify the literature of LSM improvements , survey the efforts in detail, and discuss their strengths and trade-offs.
Revised:  We provide a general taxonomy to classify the literature of LSM-trees , survey the efforts in detail, and discuss their strengths and trade-offs.
Edit intent: clarity
Original:  We further survey several representative LSM-based open-source NoSQL systems and we discuss some potential future research directions resulting from the survey.
Revised:  We further survey several representative LSM-based open-source NoSQL systems and discuss some potential future research directions resulting from the survey.
Edit intent: fluency
Original: A new iterative method for solving large scale symmetric nonlinear eigenvalue problems is presented.
Revised: A new iterative method for solving large-scale symmetric nonlinear eigenvalue problems is presented.
Edit intent: fluency
Original:  We show how, under  specific assumption on the starting vector, this method can be carried out in finite arithmetic and how exploiting the problem structure leads to improvements in terms of computation time.
Revised:  We show how, under a specific assumption on the starting vector, this method can be carried out in finite arithmetic and how exploiting the problem structure leads to improvements in terms of computation time.
Edit intent: fluency
Original: A new iterative method for solving large-scale symmetric nonlinear eigenvalue problems is presented.
Revised: A new iterative method for solving large scale symmetric nonlinear eigenvalue problems is presented.
Edit intent: fluency
Original:  We show how, under a specific assumption on the starting vector, this method can be carried out in finite arithmetic and how exploiting the problem structure leads to improvements in terms of computation time  .
Revised:  We show how, under specific assumption on the starting vector, this method can be carried out in finite arithmetic and how exploiting the problem structure leads to improvements in terms of computation time .
Edit intent: fluency
Original:  We show how, under a specific assumption on the starting vector, this method can be carried out in finite arithmetic and how exploiting the problem structure leads to improvements in terms of computation time  .
Revised:  We show how, under a specific assumption on the starting vector, this method can be carried out in finite arithmetic and how the exploitation of the problem structure leads to improvements in terms of computation time .
Edit intent: clarity
Original: Differential evolution is the conventional algorithm with the fastest convergence speed, but it may be trapped  local optimal solution easily , so many researchers devote themselves into improve DE.
Revised: Differential evolution (DE) is a conventional algorithm with the fastest convergence speed, but it may be trapped local optimal solution easily , so many researchers devote themselves into improve DE.
Edit intent: clarity
Original: Differential evolution is the conventional algorithm with the fastest convergence speed, but it may be trapped  local optimal solution easily , so many researchers devote themselves into improve DE.
Revised: Differential evolution is the conventional algorithm with fast convergence speed. However, DE may be trapped local optimal solution easily , so many researchers devote themselves into improve DE.
Edit intent: coherence
Original: Differential evolution is the conventional algorithm with the fastest convergence speed, but it may be trapped  local optimal solution easily , so many researchers devote themselves into improve DE.
Revised: Differential evolution is the conventional algorithm with the fastest convergence speed, but it may be trapped in local optimal solution easily , so many researchers devote themselves into improve DE.
Edit intent: fluency
Original: Differential evolution is the conventional algorithm with the fastest convergence speed, but it may be trapped  local optimal solution easily , so many researchers devote themselves into improve DE.
Revised: Differential evolution is the conventional algorithm with the fastest convergence speed, but it may be trapped local optimal solution easily . Many researchers devote themselves into improve DE.
Edit intent: coherence
Original:  Whale swarm algorithm (WSA) is a new algorithm with niching strategy we proposed previously, it is featured with simple mutation strategyand powerful global search capability, but for functions with high dimensions, it converges slower than conventional algorithms . Based on this fact, we proposed a new DE algorithm , called DE with nearest better option (NbDE).
Revised:  Whale swarm algorithm (WSA) is a new algorithm with niching strategy we proposed previously, it is featured with simple mutation strategyand powerful global search capability, but for functions with high dimensions, it converges slower than conventional algorithms . Based on this fact, we propose a new DE algorithm , called DE with nearest better option (NbDE).
Edit intent: fluency
Original:  Whale swarm algorithm (WSA) is a new algorithm with niching strategy we proposed previously, it is featured with simple mutation strategyand powerful global search capability, but for functions with high dimensions, it converges slower than conventional algorithms . Based on this fact, we proposed a new DE algorithm , called DE with nearest better option (NbDE).
Revised:  Whale swarm algorithm (WSA) is a new algorithm with niching strategy we proposed previously, it is featured with simple mutation strategyand powerful global search capability, but for functions with high dimensions, it converges slower than conventional algorithms . Based on this fact, we proposed a new DE algorithm called DE with nearest better option (NbDE).
Edit intent: fluency
Original:  Whale swarm algorithm (WSA) is a new algorithm with niching strategy we proposed previously, it is featured with simple mutation strategyand powerful global search capability, but for functions with high dimensions, it converges slower than conventional algorithms . Based on this fact, we proposed a new DE algorithm , called DE with nearest better option (NbDE).
Revised:  Whale swarm algorithm (WSA) is a new algorithm with niching strategy we proposed previously, it is featured with simple mutation strategyand powerful global search capability, but for functions with high dimensions, it converges slower than conventional algorithms . Based on this fact, we proposed a new DE algorithm , called DE with better option (NbDE).
Edit intent: clarity
Original:  Whale swarm algorithm (WSA) is a new algorithm with niching strategy we proposed previously, it is featured with simple mutation strategyand powerful global search capability, but for functions with high dimensions, it converges slower than conventional algorithms . Based on this fact, we proposed a new DE algorithm , called DE with nearest better option (NbDE).
Revised:  Whale swarm algorithm (WSA) is a new algorithm with niching strategy we proposed previously, it is featured with simple mutation strategyand powerful global search capability, but for functions with high dimensions, it converges slower than conventional algorithms . Based on this fact, we proposed a new DE algorithm , called DE with nearest Better and Nearest option (NbDE).
Edit intent: coherence
Original:  In order to evaluate the performance of NbDE, we compare NbDE with several meta-heuristic algorithms in nine classical benchmark  functions with different dimensions.
Revised:  In order to evaluate the performance of NbDE, NbDE is compared with several meta-heuristic algorithms in nine classical benchmark functions with different dimensions.
Edit intent: clarity
Original:  In order to evaluate the performance of NbDE, we compare NbDE with several meta-heuristic algorithms in nine classical benchmark  functions with different dimensions.
Revised:  In order to evaluate the performance of NbDE, we compare NbDE with several meta-heuristic algorithms on nine classical benchmark functions with different dimensions.
Edit intent: fluency
Original:  The result have shown that NbDE outperforms other algorithms in convergence speed and accuracy.
Revised:  The results show that NbDE outperforms other algorithms in convergence speed and accuracy.
Edit intent: clarity
Original:  RNNs are particularly well suited for machine learning problems in which context is important, such as speech recognition or language translation.
Revised:  RNNs are particularly well suited for machine learning problems in which context is important, such as speech recognition and language translation.
Edit intent: fluency
Original:  We compare our design with a state-of-the-art GPGPU and find 21.8x better performance with 70x lower energy.
Revised:  We compare our design with a state-of-the-art GPGPU and find21.8x higher performance with70x lower energy
Edit intent: clarity
Original:  The proposed algorithm is suitable to implement in an Internet-of-Things context with minimal demands on infrastructure.
Revised:  The proposed algorithm is suitable to implement in an IoT context with minimal demands on infrastructure.
Edit intent: clarity
Original:  We also present simulation results to check the efficacy of the algorithm .
Revised:  We also present simulation results to check the efficacy of the algorithms .
Edit intent: fluency
Original: Biomolecular machines transduce free energy from one form to another to fulfill many important roles inside cells, with free energy dissipation required to achieve directed progress.
Revised: Biomolecular machines transduce free energy from one form to another to fulfill many important roles inside cells, with dissipation required to achieve directed progress.
Edit intent: clarity
Original:  We investigate how to break time-reversal symmetry at a given dissipation cost for sawtooth potentials, which have frequently been used to model molecular machines as ratchets.
Revised:  We investigate how to break time-reversal symmetry at a given dissipation cost by using deterministic protocols to drive systems over sawtooth potentials, which have frequently been used to model molecular machines as ratchets.
Edit intent: clarity
Original:  For each word in the text, we use additional indexes to store information about nearby words  at distances from the given word of less than or equal to MaxDistance, which is a parameter.
Revised:  For each word in a text, we use additional indexes to store information about nearby words at distances from the given word of less than or equal to MaxDistance, which is a parameter.
Edit intent: fluency
Original:  For each word in the text, we use additional indexes to store information about nearby words  at distances from the given word of less than or equal to MaxDistance, which is a parameter.
Revised:  For each word in the text, we use additional indexes to store information about nearby words that are at distances from the given word of less than or equal to MaxDistance, which is a parameter.
Edit intent: clarity
Original:  For each word in the text, we use additional indexes to store information about nearby words  at distances from the given word of less than or equal to MaxDistance, which is a parameter.
Revised:  For each word in the text, we use additional indexes to store information about nearby words at distances from the given word of less than or equal to the MaxDistance parameter.
Edit intent: clarity
Original:  We had shown that additional indexes with three-component keys can be used to improve the average query execution time  up to 94.7 times if the queries consist of high-frequency used words.
Revised:  We showed that additional indexes with three-component keys can be used to improve the average query execution time up to 94.7 times if the queries consist of high-frequency used words.
Edit intent: coherence
Original:  We had shown that additional indexes with three-component keys can be used to improve the average query execution time  up to 94.7 times if the queries consist of high-frequency used words.
Revised:  We had shown that additional indexes with three-component keys can be used to improve the average query execution time by up to 94.7 times if the queries consist of high-frequency used words.
Edit intent: fluency
Original:  We had shown that additional indexes with three-component keys can be used to improve the average query execution time  up to 94.7 times if the queries consist of high-frequency used words.
Revised:  We had shown that additional indexes with three-component keys can be used to improve the average query execution time up to 94.7 times if the queries consist of high-frequency occurring words.
Edit intent: clarity
Original:  In particular, the random hyperplane rounding method of Goemans and Williamson , has been extensively studied for more than two decades, resulting in various extensions to the original technique and beautiful algorithms for a wide range of applications.
Revised:  In particular, the random hyperplane rounding method of Goemans and Williamson has been extensively studied for more than two decades, resulting in various extensions to the original technique and beautiful algorithms for a wide range of applications.
Edit intent: fluency
Original:  Despite the fact that this approach yields tight approximation guarantees for some problems, e.g., Max-Cut, for many others, e.g., Max-Sat and Max-DiCut, the tight approximation ratio is still unknown.
Revised:  Despite the fact that this approach yields tight approximation guarantees for some problems, e.g., Max-Cut, for many others, e.g., Max-SAT and Max-DiCut, the tight approximation ratio is still unknown.
Edit intent: fluency
Original:  One of the main reasons for this is the fact that very few techniques for rounding semi-definite relaxations are known.
Revised:  One of the main reasons for this is the fact that very few techniques for rounding semidefinite relaxations are known.
Edit intent: fluency
Original:  Focusing on constraint satisfaction problems, we apply our method to several classical problems, including Max-Cut, Max-2SAT, and Max-DiCut , and derive new algorithms that are competitive with the best known results.
Revised:  Focusing on constraint satisfaction problems, we apply our method to several classical problems, including Max-Cut, Max-2SAT, and MaxDiCut , and derive new algorithms that are competitive with the best known results.
Edit intent: fluency
Original: The physics-based simulation game Angry Birds has been heavily researched by the AI community over the past five years, and has been the subject of a popular AI competition that is being held annually as part of a leading AI conference.
Revised: The physics-based simulation game Angry Birds has been heavily researched by the AI community over the past five years, and has been the subject of a popular AI competition that is currently held annually as part of a leading AI conference.
Edit intent: clarity
Original:  By using a combination of several gadgets within this game's environment, we are able to demonstrate that the decision problem of solving general levels for different versions of Angry Birds is either NP-hard, PSPACE-hard, PSPACE-complete or EXPTIME-complete . Proof of NP-hardness is by reduction from 3-SAT, whilst proof of PSPACE-hardness is by reduction from True Quantified Boolean Formula (TQBF).
Revised:  By using a combination of several gadgets within this game's environment, we are able to demonstrate that the decision problem of solving general levels for different versions of Angry Birds is either NP-hard, PSPACE-hard, PSPACE-complete or EXPTIME-hard . Proof of NP-hardness is by reduction from 3-SAT, whilst proof of PSPACE-hardness is by reduction from True Quantified Boolean Formula (TQBF).
Edit intent: fluency
Original:  A particular aspect of longevity addressed in this paper is the difference in average  duration of life for female and male population, called  lifespan differential.
Revised:  A particular aspect of longevity addressed in this paper is the difference in average observed duration of life for female and male population, called lifespan differential.
Edit intent: clarity
Original: Building on the model proposed in Lillicrap et. al. we show that deep networks can be trained using biologically plausible Hebbian rules yielding similar performance to ordinary back-propagation  .
Revised: We show that deep networks can be trained using biologically plausible Hebbian rules yielding similar performance to ordinary back-propagation .
Edit intent: clarity
Original: Building on the model proposed in Lillicrap et. al. we show that deep networks can be trained using biologically plausible Hebbian rules yielding similar performance to ordinary back-propagation  .
Revised: Building on the model proposed in Lillicrap et. al. we show that deep networks can be trained using Hebbian updates yielding similar performance to ordinary back-propagation .
Edit intent: clarity