wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This decoder has multiple limitations: 1) allowing to generate syntactically incorrect programs, 2) inefficiently representing small edits, and 3) not being able to generate project-specific identifiers. In this paper, we propose Recoder, a syntax-guided edit decoder with placeholder generation. Recoder is novel in multiple aspects: 1) Recoder generates edits rather than modified code, allowing efficient representation of small edits; 2) Recoder is syntax-guided, with the novel provider/decider architecture to ensure the syntactic correctness of the patched program and accurate generation; 3)Recoder generates placeholders that could be instantiated as project-specific identifiers later. We conduct experiments to evaluate Recoder on 395 bugs from Defects4J v1.2, 420 additional bugs from Defects4J v2.0, 297 bugs from IntroClassJava and 40 bugs from QuixBugs.Our results show that Recoder repairs 53 bugs on Defects4J v1.2, which achieves 26.2\% (11 bugs) improvement over the previous state-of-the-art approach for single-hunk bugs (TBar). Importantly, to our knowledge, Recoder is the first DL-based APR approach that has outperformed the traditional APR approaches on this benchmark .
Revised:  This decoder has multiple limitations: 1) allowing to generate syntactically incorrect programs, 2) inefficiently representing small edits, and key-words, etc., which improves the efficiency of program development and reduces spelling errors in the coding process. Such tools use static analysis on the code and provide candidates for completion arranged in alphabetical order. Code synthesis is implemented from two aspects, one based on input-output samples and the other based on functionality description. In this study, we introduce existing techniques of these two aspects and the corresponding techniques and present some possible future research directions .
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Original: Automatic software development has been a research hot spot in the field of software engineering (SE) in the past decade. In particular, deep learning (DL) has been applied and achieved a lot of progress in various SE tasks.
Revised: Automated Program Repair(APR) helps to improve the efficiency of software development and maintenance. Although a lot of ofAPR approaches have been proposed recently, they have their own limitations. Pattern-based approaches need hand-written patterns which need a lot of progress in various SE tasks.
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Original:  In particular, deep learning (DL) has been applied and achieved a lot of progress in various SE tasks. Among all applications, automatic code generation by machines as a general concept, including code completion and code synthesis, is a common expectation in the field of SE, which may greatly reduce the development burden of the software developers and improves the efficiency and quality of the software development process to a certain extent. Code completion is an important part of modern integrated development environments (IDEs). Code completion technology effectively helps programmers complete code class names, method names, and key-words, etc. , which improves the efficiency of program development and reduces spelling errors in the coding process. Such tools use static analysis on the code and provide candidates for completion arranged in alphabetical order. Code synthesis is implemented from two aspects, one based on input-output samples and the other based on functionality description. In this study, we introduce existing techniques of these two aspects and the corresponding techniques and present some possible future research directions .
Revised:  In particular, deep learning (DL) has been applied and achieved a lot of hard work of programmers. DL-based approaches can generate complex statements when adopting net models. In this paper, we propose a framework to integrate these two kinds of APR approaches. Then, we introduce a novel neural model named Recoder designed for this framework to generate the patches based on the given buggy method. We conducted several experiments to evaluate Recorder on several datasets, Defects4j v1.2, and Defects4j v2.0. Our results show that Recorder achieved 23.1\% improvements over the previous state-of-the-art approaches and repaired 52 bugs on Defects4j v1.2. Furthermore, Recoder achieved 200\% performance on Defects4j v2.0compared with the top two pattern-based APR tools .
Edit intent: meaning-changed
Original: Automated Program Repair(APR) helps to improve the efficiency of software development and maintenance. Although a lot of ofAPR approaches have been proposed recently, they have their own limitations. Pattern-based approaches need hand-written patterns which need a lot of hard work of programmers.
Revised: Automatic software development has been a research hot spot in the field of software engineering (SE) in the past decade. In particular, deep learning (DL) has been applied and achieved a lot of hard work of programmers.
Edit intent: meaning-changed
Original:  Pattern-based approaches need hand-written patterns which need a lot of hard work of programmers. DL-based approaches can generate complex statements when adopting net models. In this paper, we propose a framework to integrate these two kinds of APR approaches. Then, we introduce a novel neural model named Recoder designed for this framework to generate the patches based on the given buggy method. We conducted several experiments to evaluate Recorder on several datasets, Defects4j v1.2, and Defects4j v2.0.Our results show that Recorder achieved 23.1\% improvements over the previous state-of-the-art approaches and repaired 52 bugs on Defects4j v1.2. Furthermore, Recoder achieved 200\% performance on Defects4j v2.0compared with the top two pattern-based APR tools .
Revised:  Pattern-based approaches need hand-written patterns which need a lot of progress in various SE tasks. Among all applications, automatic code generation by machines as a general concept, including code completion and code synthesis, is a common expectation in the field of SE, which may greatly reduce the development burden of the software developers and improves the efficiency and quality of the software development process to a certain extent. Code completion is an important part of modern integrated development environments (IDEs). Code completion technology effectively helps programmers complete code class names, method names, and key-words, etc., which improves the efficiency of program development and reduces spelling errors in the coding process. Such tools use static analysis on the code and provide candidates for completion arranged in alphabetical order. Code synthesis is implemented from two aspects, one based on input-output samples and the other based on functionality description. In this study, we introduce existing techniques of these two aspects and the corresponding DL techniques, and present some possible future research directions .
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Original:  These consist of: historical country-level annual yields, crop calendars, crop cover, NDVI, temperature, rainfall, and evapotransporation.abstract 
Revised:  These consist of: historical country-level annual yields, crop calendars, crop cover, NDVI, temperature, rainfall, and evapotransporation.abstract \%-10\% for predictions 9 months into the year, and 7\%-14\% for predictions 3 months into the year. The model outputs daily forecasts for the final yield of the current year. It is trained using approximately 4 million data points for each crop-country pair. These consist of: historical country-level annual yields, crop calendars, crop cover, NDVI, temperature, rainfall, and evapotransporation.
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Original: Efficient deployment of Deep Neural Networks (DNNs) on edge devices (i. e., FPGAs and mobile platforms) is very challenging, especially under a recent witness of the increasing DNN model size and complexity. Although various optimization approaches have been proven to be effective in many DNNs on edge devices, most state-of-the-art work focuses on ad-hoc optimizations, and there lacks a thorough study to comprehensively reveal the potentials and constraints of different edge deviceswhen considering different optimizations .
Revised: Recent research demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector multiplication -- the intensive and key computation in deep neural networks (DNNs). However, hardware failure, such as stuck-at-fault defects, is one of the main concerns that impedes the ReRAM devices to be a feasible solution for real implementations. The existing solutions to address this issue usually require an optimization to be conducted for each individual device, which is impractical for mass-produced products (e.g., IoT devices) .
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Original:  In this paper, we qualitatively and quantitatively compare the energy-efficiency of FPGA-based and mobile-based DNN executions, and provide detailed analysis .
Revised:  In this paper, we rethink the value of weight pruning in ReRAM-based DNN design from the perspective of model fault tolerance. And a differential mapping scheme is proposed to improve the fault tolerance under a high stuck-on fault rate. Our method can tolerate almost an order of magnitude higher failure rate than the traditional two-column method in representative DNN tasks. More importantly, our method does not require extra hardware cost compared to the traditional two-column mapping scheme. The improvement is universal and does not require the optimization process for each individual device .
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Original:  However, for versatile robots nowadays that need to learn diverse tasks, providing and learning the multi-task demonstrations all at once are both difficult . To solve this problem, in this work we study how to realize continual imitation learning ability that empowers robots to continually learn new tasks one by one, thus reducing the burden of multi-task IL and accelerating the process of new task learning at the same time.
Revised:  However, for versatile robots nowadays that need to learn diverse tasks, providing and learning the multi-task demonstrations to be provided at once for acquiring diverse skills, which is difficult in real world. In this work we study how to realize continual imitation learning ability that empowers robots to continually learn new tasks one by one, thus reducing the burden of multi-task IL and accelerating the process of new task learning at the same time.
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Original:  We propose a novel trajectory generation model that employs both a generative adversarial network and a dynamics prediction model to generate pseudo trajectories from all learned tasks in the new task learning process to achieve continual imitation learning ability .
Revised:  We propose a novel trajectory generation model that employs both a generative adversarial network and a dynamics-aware prediction model to generate pseudo trajectories from all learned tasks in the new task learning process to achieve continual imitation learning ability .
Edit intent: clarity
Original:  The model learns to predict environmental state transitions by self-exploration and generating motor actions by sampling stochastic internal states z . Habitual behavior, which is obtained from the prior distribution of z , is acquired by reinforcement learning.
Revised:  The model learns to predict environmental state transitions by self-exploration and generating motor actions by sampling stochastic internal states z . Habitual behavior, which is obtained from the prior distribution of z , is acquired by reinforcement learning.
Edit intent: fluency
Original: In transportation, communication, social and other real networks, there are some critical edges play an important role in the delivery of traffic flow and information packets. Identifying critical edges in complex networks is of great significance in both theoretical studies and practical applications  . Considering the overlap of communities in the neighborhood of edges, a novel and effective index named subgraph overlap (SO) is proposed in this paper.
Revised: In transportation, communication, social and other real networks, there are some critical edges act a pivotal part in controlling the flow of information and maintaining the integrity of the structure. Due to the importance of critical edges in complex networks is of great significance in both theoretical studies and practical applications . Considering the overlap of communities in the neighborhood of edges, a novel and effective index named subgraph overlap (SO) is proposed in this paper.
Edit intent: clarity
Original:  Identifying critical edges in complex networks is of great significance in both theoretical studies and practical applications  . Considering the overlap of communities in the neighborhood of edges, a novel and effective index named subgraph overlap (SO) is proposed in this paper.
Revised:  Identifying critical edges in theoretical studies and practical applications . Considering the overlap of communities in the neighborhood of edges, a novel and effective index named subgraph overlap (SO) is proposed in this paper.
Edit intent: clarity
Original:  Identifying critical edges in complex networks is of great significance in both theoretical studies and practical applications  . Considering the overlap of communities in the neighborhood of edges, a novel and effective index named subgraph overlap (SO) is proposed in this paper.
Revised:  Identifying critical edges in complex networks is of great significance in both theoretical studies and practical applications , the identification of critical edges gradually become a hot topic in current researches . Considering the overlap of communities in the neighborhood of edges, a novel and effective index named subgraph overlap (SO) is proposed in this paper.
Edit intent: meaning-changed
Original:  Identifying critical edges in complex networks is of great significance in both theoretical studies and practical applications  . Considering the overlap of communities in the neighborhood of edges, a novel and effective index named subgraph overlap (SO) is proposed in this paper.
Revised:  Identifying critical edges in complex networks is of great significance in both theoretical studies and practical applications . Considering the overlap of communities in the neighborhood of edges, a novel and effective metric named subgraph overlap (SO) is proposed in this paper.
Edit intent: clarity
Original:  Identifying critical edges in complex networks is of great significance in both theoretical studies and practical applications  . Considering the overlap of communities in the neighborhood of edges, a novel and effective index named subgraph overlap (SO) is proposed in this paper. Experimental results on one synthetic and eight real networks show that SO outperforms five benchmark methods in identifying critical edges which are crucial in maintaining the integrity of the structure and functions of networks.
Revised:  Identifying critical edges in complex networks is of great significance in both theoretical studies and practical applications . Considering the overlap of communities in the neighborhood of edges, a novel and effective index named subgraph overlap (SO) is proposed to quantifying the significance of edges. The experimental results show that SO outperforms five benchmark methods in identifying critical edges which are crucial in maintaining the integrity of the structure and functions of networks.
Edit intent: clarity
Original:  Earlier metrics does not work sufficiently well under the challenging cross-domain cross-task transfer settings, but recent OTCE score achieves a noteworthy performance using auxiliary tasks. A simplified version named OT-based NCE score sacrifices accuracy to be more efficient, but it can be further improved. Consequently ,
Revised:  Earlier metrics does not work sufficiently well under the challenging cross-domain cross-task settings. The recently proposed OTCE score solves this problem by considering both domain and task differences, with the help of transfer experiences on auxiliary tasks ,
Edit intent: clarity
Original:   we propose a practical transferability metric called JC-NCE score to further improve the cross-domain cross-task transferability estimation performance, which is more efficient than the OTCE score and more accurate than the OT-based NCE score . Specifically, we build the joint correspondences between source and target data via solving an optimal transport problem with  considering both the sample distance and label distance, and then compute the transferability score as the negative conditional entropy  .
Revised:  we propose a practical transferability metric called JC-NCE score that dramatically improves the robustness of the task difference estimation in OTCE, thus removing the need for auxiliary tasks . Specifically, we build the joint correspondences between source and target data via solving an optimal transport problem with considering both the sample distance and label distance, and then compute the transferability score as the negative conditional entropy .
Edit intent: clarity
Original:  which is more efficient than the OTCE score and more accurate than the OT-based NCE score . Specifically, we build the joint correspondences between source and target data via solving an optimal transport problem with  considering both the sample distance and label distance, and then compute the transferability score as the negative conditional entropy  .
Revised:  which is more efficient than the OTCE score and more accurate than the OT-based NCE score . Specifically, we build the joint correspondences between source and target data via solving an optimal transport problem with a ground cost considering both the sample distance and label distance, and then compute the transferability score as the negative conditional entropy .
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Original:  which is more efficient than the OTCE score and more accurate than the OT-based NCE score . Specifically, we build the joint correspondences between source and target data via solving an optimal transport problem with  considering both the sample distance and label distance, and then compute the transferability score as the negative conditional entropy  .
Revised:  which is more efficient than the OTCE score and more accurate than the OT-based NCE score . Specifically, we build the joint correspondences between source and target data via solving an optimal transport problem with considering both the sample distance and label distance, and then compute the transferability score as the negative conditional entropy of the matched labels .
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Original:  Extensive validations under the intra-dataset and inter-dataset transfer settings demonstrate that our JC-NCE score outperforms the OT-based NCE score with about 7\% and 12\% gains, respectively .
Revised:  Extensive validations under the intra-dataset and inter-dataset transfer settings demonstrate that our JC-NCE score outperforms the auxiliary-task free version of OTCE for 7\% and 12\% gains, respectively .
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Original:  Extensive validations under the intra-dataset and inter-dataset transfer settings demonstrate that our JC-NCE score outperforms the OT-based NCE score with about 7\% and 12\% gains, respectively .
Revised:  Extensive validations under the intra-dataset and inter-dataset transfer settings demonstrate that our JC-NCE score outperforms the OT-based NCE score with about 7\% and 12\% , respectively, and is also more robust than other existing transferability metrics on average .
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Original: In the context of flow visualization a triple decomposition of the velocity gradient was proposed by Kolat in 2007 who demonstrated the technique in 2D , which was later realized in 3D by Nagata et al. in 2020.
Revised: In the context of flow visualization a triple decomposition of the velocity gradient was proposed by Kolat in 2007 who demonstrated the technique in two dimensions , which was later realized in 3D by Nagata et al. in 2020.
Edit intent: clarity
Original: In the context of flow visualization a triple decomposition of the velocity gradient was proposed by Kolat in 2007 who demonstrated the technique in 2D , which was later realized in 3D by Nagata et al. in 2020.
Revised: In the context of flow visualization a triple decomposition of the velocity gradient was proposed by Kolat in 2007 who demonstrated the technique in 2D , which was later realized in three dimensions by Nagata et al. in 2020.
Edit intent: clarity
Original: In the context of flow visualization a triple decomposition of the velocity gradient  was proposed by Kolat in 2007 who demonstrated the technique in two dimensions, which was later realized in three dimensions by Nagata et al. in 2020.
Revised: In the context of flow visualization a triple decomposition of the velocity gradient into irrotational straining flow, shear flow and rigid body rotational flow was proposed by Kolat in 2007 who demonstrated the technique in two dimensions, which was later realized in three dimensions by Nagata et al. in 2020.
Edit intent: meaning-changed
Original: In the context of flow visualization a triple decomposition of the velocity gradient  was proposed by Kolat in 2007 who demonstrated the technique in two dimensions, which was later realized in three dimensions by Nagata et al. in 2020.
Revised: In the context of flow visualization a triple decomposition of the velocity gradient was proposed by Kolar in 2007 who demonstrated the technique in two dimensions, which was later realized in three dimensions by Nagata et al. in 2020.
Edit intent: clarity
Original: In the context of flow visualization a triple decomposition of the velocity gradient  was proposed by Kolat in 2007 who demonstrated the technique in two dimensions, which was later realized in three dimensions by Nagata et al. in 2020.
Revised: In the context of flow visualization a triple decomposition of the velocity gradient was proposed by Kolat in 2007 V. Kolar, International journal of heat and fluid flow, 28, 638, (2007)
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Original:   The triple decomposition opens for a refined energy stability analysis of the Navier-Stokes equations, with implications for the mathematical analysis of the structure, computability and regularity of turbulent flow.
Revised:  , which has recently received renewed interest. The triple decomposition opens for a refined energy stability analysis of the Navier-Stokes equations, with implications for the mathematical analysis of the structure, computability and regularity of turbulent flow.
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Original:  We here perform an energy stability analysis of turbulent incompressible flow, which suggests a scenario where  any exponentially unstable irrotational  flow structures rapidly evolve towards linearly unstable shear flow and stable rigid body rotational flow , which dissipates to heat .
Revised:  We here perform an energy stability analysis of turbulent incompressible flow, which suggests a scenario where at macroscopic scales any exponentially unstable irrotational flow structures rapidly evolve towards linearly unstable shear flow and stable rigid body rotational flow , which dissipates to heat .
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Original:  We here perform an energy stability analysis of turbulent incompressible flow, which suggests a scenario where  any exponentially unstable irrotational  flow structures rapidly evolve towards linearly unstable shear flow and stable rigid body rotational flow , which dissipates to heat .
Revised:  We here perform an energy stability analysis of turbulent incompressible flow, which suggests a scenario where any exponentially unstable irrotational straining flow structures rapidly evolve towards linearly unstable shear flow and stable rigid body rotational flow , which dissipates to heat .
Edit intent: meaning-changed