wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Detectability of SARS-CoV-2 infection is one of the factors that affects levels of mortality from COVID-19 . Regions of the Russian Federation with relatively low detectability of  SARS-CoV-2 infection (e.g. those regions for which  case-fatality ratio is below the median value of 1.2\% for the case-fatality ratio in different regions of the Russian Federation on Sep. 17 , 2020 [ 3 ])
Revised:  Detectability of SARS-CoV-2 infection is one of the factors that affects levels of mortality from COVID-19 . Regions of the Russian Federation with relatively low detectability of SARS-CoV-2 infection (e.g. those regions for which case-fatality ratio is below the median value of 1.2\% for the case-fatality ratio in different regions of the Russian Federation on Oct. 1 , 2020 [ 3 ])
Edit intent: meaning-changed
Original:  Detectability of SARS-CoV-2 infection is one of the factors that affects levels of mortality from COVID-19 . Regions of the Russian Federation with relatively low detectability of  SARS-CoV-2 infection (e.g. those regions for which  case-fatality ratio is below the median value of 1.2\% for the case-fatality ratio in different regions of the Russian Federation on Sep. 17 , 2020 [ 3 ])
Revised:  Detectability of SARS-CoV-2 infection is one of the factors that affects levels of mortality from COVID-19 . Regions of the Russian Federation with relatively low detectability of SARS-CoV-2 infection (e.g. those regions for which case-fatality ratio is below the median value of 1.2\% for the case-fatality ratio in different regions of the Russian Federation on Sep. 17 , 2020 [ 4 ])
Edit intent: meaning-changed
Original: Sparse polynomial chaos expansions ( PCEs ) are an efficient and widely used surrogate modeling method in uncertainty quantification  .
Revised: Sparse polynomial chaos expansions ( PCEs ) are an efficient and widely used surrogate modeling method in uncertainty quantification for engineering problems with computationally expensive models. To make use of the available information in the most efficient way, several approaches for so-called basis-adaptive sparse PCE have been proposed to determine the set of polynomial regressors ("basis") for PCE adaptively .
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Original:  We investigate the synergies between sparse regression solvers and basis adaptivity schemes, and provide recommendations on which of them are most promising for specific classes of problems.
Revised:  We investigate the synergies between sparse regression solvers and basis adaptivity schemes, we find that virtually all basis-adaptive schemes outperform a static choice of basis. Three sparse solvers, namely Bayesian compressive sensing and two variants of subspace pursuit, perform especially well. Aggregating our results by model dimensionality and experimental design size, we identify combinations of methods that are most promising for specific classes of problems.
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Original:  We investigate the synergies between sparse regression solvers and basis adaptivity schemes, and provide recommendations on which of them are most promising for specific classes of problems. Furthermore, we explore the performance of a novel cross-validation-based solver and basis adaptivity selection scheme , which consistently provides close-to-optimal results  .
Revised:  We investigate the synergies between sparse regression solvers and basis adaptivity schemes, and provide recommendations on which of them are most promising for the specific problem class. Additionally, we introduce a novel solver and basis adaptivity selection scheme , which consistently provides close-to-optimal results .
Edit intent: clarity
Original:  Furthermore, we explore the performance of a novel cross-validation-based solver and basis adaptivity selection scheme , which consistently provides close-to-optimal results  .
Revised:  Furthermore, we explore the performance of a novel cross-validation-based solver and basis adaptivity selection scheme , which consistently provides close-to-optimal results in terms of accuracy, and significantly more robust solutions, while being more general than the case-by-case recommendations obtained by the benchmark .
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Original:  However, these algorithms typically impose other requirements which challenge biological plausibility: for example, requiring complex and precise connectivity schemes (predictive coding) , or multiple sequential backwards phases with information being stored across phases (equilibrium-prop) .
Revised:  However, these algorithms typically impose other requirements which challenge biological plausibility: for example, requiring complex and precise connectivity schemes , or multiple sequential backwards phases with information being stored across phases (equilibrium-prop) .
Edit intent: clarity
Original:  However, these algorithms typically impose other requirements which challenge biological plausibility: for example, requiring complex and precise connectivity schemes (predictive coding) , or multiple sequential backwards phases with information being stored across phases (equilibrium-prop) .
Revised:  However, these algorithms typically impose other requirements which challenge biological plausibility: for example, requiring complex and precise connectivity schemes (predictive coding) , or multiple sequential backwards phases with information being stored across phases .
Edit intent: clarity
Original:  Our algorithm converges robustly and exactly to the correct backpropagation gradients, requires only a single type of computational unit, utilises only a single backwards phase, and can perform credit assignment on arbitrary computation graphs.
Revised:  Our algorithm converges rapidly and robustly to the correct backpropagation gradients, requires only a single type of computational unit, utilises only a single backwards phase, and can perform credit assignment on arbitrary computation graphs.
Edit intent: clarity
Original:  Our algorithm converges robustly and exactly to the correct backpropagation gradients, requires only a single type of computational unit, utilises only a single backwards phase, and can perform credit assignment on arbitrary computation graphs.
Revised:  Our algorithm converges robustly and exactly to the correct backpropagation gradients, requires only a single type of computational unit, utilises only a single parallel backwards relaxation phase, and can perform credit assignment on arbitrary computation graphs.
Edit intent: meaning-changed
Original:  Our algorithm converges robustly and exactly to the correct backpropagation gradients, requires only a single type of computational unit, utilises only a single backwards phase, and can perform credit assignment on arbitrary computation graphs.
Revised:  Our algorithm converges robustly and exactly to the correct backpropagation gradients, requires only a single type of computational unit, utilises only a single backwards phase, and can operate on arbitrary computation graphs.
Edit intent: clarity
Original:  We illustrate these properties by training deep neural networks on visual classification tasks, and we describe simplifications to the algorithm which remove further obstacles to neurobiological implementation (for example, the weight-transport problem, and the use of nonlinear derivatives), while preserving performance.
Revised:  We illustrate these properties by training deep neural networks on visual classification tasks, and describe simplifications to the algorithm which remove further obstacles to neurobiological implementation (for example, the weight-transport problem, and the use of nonlinear derivatives), while preserving performance.
Edit intent: fluency
Original:  In this paper we propose AthenaTest, an approach that aims at generating unit test cases by learning from real-world , developer-written test cases. Our approach relies on a state-of-the-art sequence-to-sequence transformer model which is able to write useful test cases for a given method under test (i. e., focal method).
Revised:  In this paper we propose AthenaTest, an approach that aims at generating unit test cases by learning from real-world , developer-written testcases. We formulate unit test case generation as a sequence-to-sequence transformer model which is able to write useful test cases for a given method under test (i. e., focal method).
Edit intent: clarity
Original:  Our approach relies on a state-of-the-art sequence-to-sequence transformer model which is able to write useful test cases for a given method under test (i. e., focal method).
Revised:  Our approach relies on a state-of-the-art sequence-to-sequence learning task, adopting a two-step training procedure consisting of denoising pretraining on a large unsupervised Java corpus, and supervised finetuning for a downstream translation task of generating unit tests. We investigate the impact of natural language and source code pretraining, as well as the focal context information surrounding the focal method. Both techniques provide improvements in terms of validation loss, with pretraining yielding 25\% relative improvement and focal context providing additional 11.1\% improvement.
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Original:  We also introduce methods2test - the largest publicly available supervised parallel corpus of unit test case methods and corresponding focal methods in Java, which comprises 630k test cases mined from 70k open-source repositories hosted on GitHub.
Revised:  We also introduce Methods2Test, the largest publicly available supervised parallel corpus of unit test case methods and corresponding focal methods in Java, which comprises 630k test cases mined from 70k open-source repositories hosted on GitHub.
Edit intent: fluency
Original:  We also introduce methods2test - the largest publicly available supervised parallel corpus of unit test case methods and corresponding focal methods in Java, which comprises 630k test cases mined from 70k open-source repositories hosted on GitHub.
Revised:  We also introduce methods2test - the largest publicly available supervised parallel corpus of unit test case methods and corresponding focal methods in Java, which comprises 780K test cases mined from 70k open-source repositories hosted on GitHub.
Edit intent: meaning-changed
Original:  We also introduce methods2test - the largest publicly available supervised parallel corpus of unit test case methods and corresponding focal methods in Java, which comprises 630k test cases mined from 70k open-source repositories hosted on GitHub.
Revised:  We also introduce methods2test - the largest publicly available supervised parallel corpus of unit test case methods and corresponding focal methods in Java, which comprises 630k test cases mined from 91K open-source repositories hosted on GitHub.
Edit intent: meaning-changed
Original:  We also introduce methods2test - the largest publicly available supervised parallel corpus of unit test case methods and corresponding focal methods in Java, which comprises 630k test cases mined from 70k open-source repositories hosted on GitHub.
Revised:  We also introduce methods2test - the largest publicly available supervised parallel corpus of unit test case methods and corresponding focal methods in Java, which comprises 630k test cases mined from 70k open-source repositories from GitHub.
Edit intent: clarity
Original:  We use this dataset to train a transformer model to translate focal methods into the corresponding test cases. We evaluate the ability of our model in generating test cases using natural language processing as well as code-specific criteria. First, we assess the quality of the translation compared to the target test case, then we analyze properties of the test case such as syntactic correctness and number and variety of testing APIs (e. g., asserts).
Revised:  We evaluate AthenaTest on five defects4j projects, generating 25K passing test cases covering 43.7\% of the focal methods with only 30 attempts.
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Original:  We execute the test cases, collect test coverage information, and compare them with test cases generated by EvoSuite and GPT-3 . Finally, we survey professional developers on their preference in terms of readability, understandability, and testing effectiveness of the generated test cases .
Revised:  We execute the test cases, collect test coverage information, and compare them with test cases generated by EvoSuite and GPT-3 , finding that our approach outperforms GPT-3 and has comparable coverage w.r.t. EvoSuite. Finally, we survey professional developers on their preference in terms of readability, understandability, and testing effectiveness of the generated test cases .
Edit intent: meaning-changed
Original:  We execute the test cases, collect test coverage information, and compare them with test cases generated by EvoSuite and GPT-3 . Finally, we survey professional developers on their preference in terms of readability, understandability, and testing effectiveness of the generated test cases .
Revised:  We execute the test cases, collect test coverage information, and compare them with test cases generated by EvoSuite and GPT-3 . Finally, we survey professional developers on their preference in terms of readability, understandability, and testing effectiveness of the generated tests, showing overwhelmingly preference towards AthenaTest .
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Original:  A major portion of this work has focused on allocation settings where items must be allocated fairly to agents with diverse preferences. In comparison, fairness in two-sided settings where agents have to be matched to other agents, with preferences on both sides, has received much less attention. In this paper , we explore the problem of finding a stable many-to-one matching  while satisfying fairness among the agents , having cardinal utilities.
Revised:  This paper initiates the study of finding a stable many-to-one matching while satisfying fairness among the agents , having cardinal utilities.
Edit intent: clarity
Original:  In this paper , we explore the problem of finding a stable many-to-one matching  while satisfying fairness among the agents , having cardinal utilities. In particular, we focus on leximin optimal fairness and seek leximin optimal many-to-one stable matchings.
Revised:  In this paper , we explore the problem of finding a stable many-to-one matching while satisfying fairness among the agents on either side. Specifically, motivated by several real-world settings, we focus on leximin optimal fairness and seek leximin optimal many-to-one stable matchings.
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Original:  In the special case of ranked utilities where all agents on each side have the same  rankings over the agents on the other side (but not necessarily the same utilities) , we provide a characterisation for the space of stable matchings.
Revised:  We first consider the special case of ranked utilities where all agents on each side have the same rankings over the agents on the other side (but not necessarily the same utilities) , we provide a characterisation for the space of stable matchings.
Edit intent: coherence
Original:  In the special case of ranked utilities where all agents on each side have the same  rankings over the agents on the other side (but not necessarily the same utilities) , we provide a characterisation for the space of stable matchings.
Revised:  In the special case of ranked valuations where all agents on each side have the same rankings over the agents on the other side (but not necessarily the same utilities) , we provide a characterisation for the space of stable matchings.
Edit intent: meaning-changed
Original:  In the special case of ranked utilities where all agents on each side have the same  rankings over the agents on the other side (but not necessarily the same utilities) , we provide a characterisation for the space of stable matchings.
Revised:  In the special case of ranked utilities where all agents on each side have the same preference orders or rankings over the agents on the other side (but not necessarily the same utilities) , we provide a characterisation for the space of stable matchings.
Edit intent: meaning-changed
Original:  In the special case of ranked utilities where all agents on each side have the same  rankings over the agents on the other side (but not necessarily the same utilities) , we provide a characterisation for the space of stable matchings.
Revised:  In the special case of ranked utilities where all agents on each side have the same rankings over the agents on the other side (but not necessarily the same valuations). For this special case , we provide a characterisation for the space of stable matchings.
Edit intent: clarity
Original:  In the special case of ranked utilities where all agents on each side have the same  rankings over the agents on the other side (but not necessarily the same utilities) , we provide a characterisation for the space of stable matchings.
Revised:  In the special case of ranked utilities where all agents on each side have the same rankings over the agents on the other side (but not necessarily the same utilities) , we provide a complete characterisation of the space of stable matchings.
Edit intent: clarity
Original:  In fact, when additivity is the only assumption on the utilities , we show that, unless P=NP, no polynomial time approximation is possible.
Revised:  In fact, when additivity is the only assumption on the utilities , we show that, unless P=NP, no efficient polynomial factor approximation is possible. When additivity is relaxed to submodularity, we find that not even an exponential approximation is possible.
Edit intent: meaning-changed
Original: In recent years, the use of deep learning in language models , text auto-completion, and text generation has made tremendous progress and gained much attention from the research community. Some products and research projects claim that they can generate text that can be interpreted as human-writing, enabling new possibilities in many application areas.
Revised: In recent years, the use of deep learning in language models , text auto-completion, and text generation has made tremendous progress and gained much attention . Some research projects claim that they can generate text that can be interpreted as human-writing, enabling new possibilities in many application areas.
Edit intent: clarity
Original:  In this paper , we compare different neural network (NN) architectures like AWD-LSTMs, AWD-QRNNs, and Transformer , while using transfer learning , and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks.
Revised:  This paper compares different neural network (NN) architectures like AWD-LSTMs, AWD-QRNNs, and Transformer , while using transfer learning , and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks.
Edit intent: coherence
Original:  In this paper , we compare different neural network (NN) architectures like AWD-LSTMs, AWD-QRNNs, and Transformer , while using transfer learning , and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks.
Revised:  In this paper , we compare different neural network architectures like AWD-LSTMs, AWD-QRNNs, and Transformer , while using transfer learning , and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks.
Edit intent: clarity
Original:  In this paper , we compare different neural network (NN) architectures like AWD-LSTMs, AWD-QRNNs, and Transformer , while using transfer learning , and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks.
Revised:  In this paper , we compare different neural network (NN) architectures like AWD-LSTMs, AWD-QRNNs, and Transformer while using transfer learning , and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks.
Edit intent: fluency
Original:  In this paper , we compare different neural network (NN) architectures like AWD-LSTMs, AWD-QRNNs, and Transformer , while using transfer learning , and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks.
Revised:  In this paper , we compare different neural network (NN) architectures like AWD-LSTMs, AWD-QRNNs, and Transformer , while using transfer learning and different tokenizations to see how they behave in building language models using a Python dataset for code generation and filling mask tasks.
Edit intent: fluency
Original:  Considering the results, we discuss the different strengths and weaknesses of each approach and technique and what lacks do we find to evaluate the language models or apply them in a real programming context while including humans-in-the-loop .
Revised:  Considering the results, we discuss each approach's different strengths and weaknesses of each approach and technique and what lacks do we find to evaluate the language models or apply them in a real programming context while including humans-in-the-loop .
Edit intent: clarity
Original:  Considering the results, we discuss the different strengths and weaknesses of each approach and technique and what lacks do we find to evaluate the language models or apply them in a real programming context while including humans-in-the-loop .
Revised:  Considering the results, we discuss the different strengths and weaknesses and what gaps we find to evaluate the language models or apply them in a real programming context while including humans-in-the-loop .
Edit intent: clarity
Original:  Considering the results, we discuss the different strengths and weaknesses of each approach and technique and what lacks do we find to evaluate the language models or apply them in a real programming context while including humans-in-the-loop .
Revised:  Considering the results, we discuss the different strengths and weaknesses of each approach and technique and what lacks do we find to evaluate the language models or apply them in a real programming context .
Edit intent: coherence
Original:  A user's request at a time slot is satisfied if the requested file is already hosted on at least one of the caches connected to the user at that time slot. Our objective is to design an efficient online caching policy with minimal regret.
Revised:  A user's request at a time slot is satisfied if the requested file is already hosted on at least one of the caches connected to the user . The objective is to design an efficient online caching policy with minimal regret.
Edit intent: clarity
Original:  Our objective is to design an efficient online caching policy with minimal regret. In this paper, we proposeLeadCache,\texttt{  an online caching policy based on the \textsf{Follow the Perturbed Leader (FTPL) paradigm.
Revised:  Our objective is to LeadCache,\texttt{ an online caching policy based on the \textsf{Follow the Perturbed Leader (FTPL) paradigm.
Edit intent: coherence
Original:  In this paper, we proposeLeadCache,\texttt{  an online caching policy based on the \textsf{Follow the Perturbed Leader (FTPL) paradigm.
Revised:  In this paper, we proposepredict and optimally store the files on the caches to maximize the total number of cache hits. We propose\texttt{ an online caching policy based on the \textsf{Follow the Perturbed Leader (FTPL) paradigm.
Edit intent: meaning-changed
Original:  In this paper, we proposeLeadCache,\texttt{  an online caching policy based on the \textsf{Follow the Perturbed Leader (FTPL) paradigm.
Revised:  In this paper, we proposeLeadCache,\texttt{LeadCache an online caching policy based on the \textsf{Follow the Perturbed Leader (FTPL) paradigm.
Edit intent: fluency