wanyu/IteraTeR_v2 huggingface dataset:
Original:  We provide explicit formulas for the model parameters in order to generate random graphs which fulfill a number of requirements on the behavior of the smallest, largest, and average expected degrees and have several desirable properties, including a power-law degree distribution with any prescribed exponent larger than 2, the presence of a giant component and no potentially isolated nodes .
Revised:  We provide explicit formulas for the model parameters in order to generate random graphs that have several desirable properties, including a power-law degree distribution with any prescribed exponent larger than 2, the presence of a giant component and no potentially isolated nodes .
Edit intent: clarity
Original:  We provide explicit formulas for the model parameters in order to generate random graphs which fulfill a number of requirements on the behavior of the smallest, largest, and average expected degrees and have several desirable properties, including a power-law degree distribution with any prescribed exponent larger than 2, the presence of a giant component and no potentially isolated nodes .
Revised:  We provide explicit formulas for the model parameters in order to generate random graphs which fulfill a number of requirements on the behavior of the smallest, largest, and average expected degrees and have several desirable properties, including a power-law degree distribution with any exponent larger than 2, the presence of a giant component and no potentially isolated nodes .
Edit intent: clarity
Original:  We provide explicit formulas for the model parameters in order to generate random graphs which fulfill a number of requirements on the behavior of the smallest, largest, and average expected degrees and have several desirable properties, including a power-law degree distribution with any prescribed exponent larger than 2, the presence of a giant component and no potentially isolated nodes .
Revised:  We provide explicit formulas for the model parameters in order to generate random graphs which fulfill a number of requirements on the behavior of the smallest, largest, and average expected degrees and have several desirable properties, including a power-law degree distribution with any prescribed exponent larger than 2, the presence of a giant component .
Edit intent: clarity
Original: High-resolution digital microscopy provides powerful tools for probing the real-time dynamics of subcellular structures, and adequate record-keeping is necessary to evaluate results, share data, and allow experiments to be repeated.
Revised: The application of microscopy in biomedical research has come a long way since Antonie van Leeuwenhoek discovered unicellular organisms. Countless innovations have positioned light microscopy as a cornerstone of modern biology and a method of choice for connecting omics datasets to their biological and clinical correlates. Still, regardless of how convincing published imaging data looks, it does not always convey meaningful information about the conditions in which it was acquired, processed, and analyzed. Adequate record-keeping is necessary to evaluate results, share data, and allow experiments to be repeated.
Edit intent: coherence
Original: Our study provided a review of the concept extraction literature from January 2009 to June 2019.
Revised: Our study provided a review of the development of clinical concept extraction applications from January 2009 to June 2019.
Edit intent: clarity
Original:  The systematic summarization of concept extraction methodologic development processes illustrated the diversity, complexity, usability, challenges and limitations of both rule-based and statistical traditional machine learning approaches for clinical concept extraction.
Revised:  We hope, through the studying of different approaches with variant clinical context, can enhance the decision making for the development of clinical concept extraction.
Edit intent: coherence
Original: Our study provided a review of the development of clinical concept extraction applications  from January 2009 to June 2019.
Revised: Background Concept extraction, a subdomain of natural language processing (NLP) with a focus on extracting concepts of interest, has been adopted to computationally extract clinical information from text for a wide range of applications ranging from clinical decision support to care quality improvement. Objectives In this literature review, we provide a methodology review of clinical concept extraction, aiming to catalog development processes, available methods and tools, and specific considerations when developing clinical concept extraction applications from January 2009 to June 2019.
Edit intent: coherence
Original:  In this paper, we discuss the use of tiered graph autoencoders together with graph classification for molecular graphs.
Revised:  In this paper, we discuss the use of tiered graph autoencoders together with graph prediction for molecular graphs.
Edit intent: clarity
Original:  We briefly review graph classification and the QM9 dataset for background information, and discuss the use of tiered graph embeddings for graph classification , particularly weighted group pooling.
Revised:  We briefly review graph prediction and the QM9 dataset for background information, and discuss the use of tiered graph embeddings for graph classification , particularly weighted group pooling.
Edit intent: clarity
Original:  We briefly review graph classification and the QM9 dataset for background information, and discuss the use of tiered graph embeddings for graph classification , particularly weighted group pooling.
Revised:  We briefly review graph classification and the QM9 dataset for background information, and discuss the use of tiered graph embeddings for graph prediction , particularly weighted group pooling.
Edit intent: clarity
Original: Implicit in the RNA world hypothesis is that prebiotic RNA synthesis, despite occurring in an environment without biochemical catalysts, produced the long RNA polymers  essential to the formation of life.
Revised: Implicit in the RNA world hypothesis is that prebiotic RNA synthesis, despite occurring in an environment without biochemical catalysts, produced the long RNA polymers which are essential to the formation of life.
Edit intent: coherence
Original:  Finally , we derive a lower bound on the error introduced by truncation and compare this lower bound to the actual error found in our simulation  .
Revised:  Afterwards , we derive a lower bound on the error introduced by truncation and compare this lower bound to the actual error found in our simulation .
Edit intent: fluency
Original:  The model, namedKRED , consists of three layers: (1) an entity representation layer;
Revised:  The model, named , consists of three layers: (1) an entity representation layer;
Edit intent: clarity
Original:  The context embedding layer is designed to distinguish dynamic context of different entities such as frequency, category and position.
Revised:  The context embedding layer is applied to annotate the dynamic context of different entities such as frequency, category and position.
Edit intent: clarity
Original:  The information distillation layer will aggregate the entity embeddings under the guidance of the original document vector, transforming the document vector into a new one.
Revised:  Finally, an information distillation layer will aggregate the entity embeddings under the guidance of the original document vector, transforming the document vector into a new one.
Edit intent: coherence
Original:  The information distillation layer will aggregate the entity embeddings under the guidance of the original document vector, transforming the document vector into a new one.
Revised:  The information distillation layer aggregates the entity embeddings under the guidance of the original document vector, transforming the document vector into a new one.
Edit intent: clarity
Original:  The information distillation layer will aggregate the entity embeddings under the guidance of the original document vector, transforming the document vector into a new one.
Revised:  The information distillation layer will aggregate the entity embeddings under the guidance of the original document representation and transforms the document vector into a new one.
Edit intent: clarity
Original:  The results demonstrate that our proposed model greatly benefits a variety of news recommendation tasks, including personalized news recommendation, article category classification, article popularity prediction and local news detection .
Revised:  The results demonstrate that our proposed model greatly benefits a variety of news recommendation applications .
Edit intent: clarity
Original:  The optimization process is conducted under a multi-task framework  . Experiments on a real-world news dataset demonstrate that KRED greatly benefits a variety of news recommendation applications.
Revised:  We advocate to optimize the model with a multi-task framework . Experiments on a real-world news dataset demonstrate that KRED greatly benefits a variety of news recommendation applications.
Edit intent: clarity
Original:  As the first exploration in end-to-end SQA models, our results matched the performance of conventional approaches that fed with output text from ASR and only slightly fell behind pre-trained language models, showing the potential of end-to-end SQA models.
Revised:  Our model can outperform conventional approaches on the dataset which contains both correctly recognized answers and incorrectly recognized answers. Our experimental results show the potential of end-to-end SQA models.
Edit intent: coherence
Original: While  end-to-end models for spoken language understanding tasks have been explored recently, there is still no end-to-end model for spoken question answering (SQA) tasks, which would be catastrophically influenced by speech recognition errors.
Revised: While various end-to-end models for spoken language understanding tasks have been explored recently, there is still no end-to-end model for spoken question answering (SQA) tasks, which would be catastrophically influenced by speech recognition errors.
Edit intent: clarity
Original: While  end-to-end models for spoken language understanding tasks have been explored recently, there is still no end-to-end model for spoken question answering (SQA) tasks, which would be catastrophically influenced by speech recognition errors.
Revised: While end-to-end models for spoken language understanding tasks have been explored recently, there is still no end-to-end spoken question answering (SQA) tasks, which would be catastrophically influenced by speech recognition errors.
Edit intent: fluency
Original: We analyze the convergence rate of the Randomized Newton Method (RNM)
Revised: We analyze the convergence rate of the randomized Newton-like method
Edit intent: clarity
Original:  for smooth and convex objectives, which uses random coordinate blocks of a Hessian-over-approximation matrix \mathbf{M \bM  . The convergence analysis of RNM is challenging because of its complex dependence on the structure of \mathbf{M \bM . However, we show that when the coordinate blocks are sampled with probability proportional to their determinant, the convergence rate depends solely on the eigenvalue distribution of matrix \mathbf{M \bM , and has an analytically tractable form.
Revised:  for smooth and convex objectives, which uses random coordinate blocks of a Hessian-over-approximation matrix \bM . The convergence analysis of RNM is challenging because of its complex dependence on the structure of \mathbf{M \bM . However, we show that when the coordinate blocks are sampled with probability proportional to their determinant, the convergence rate depends solely on the eigenvalue distribution of matrix \mathbf{M \bM , and has an analytically tractable form.
Edit intent: fluency
Original:  for smooth and convex objectives, which uses random coordinate blocks of a Hessian-over-approximation matrix \mathbf{M \bM  . The convergence analysis of RNM is challenging because of its complex dependence on the structure of \mathbf{M \bM . However, we show that when the coordinate blocks are sampled with probability proportional to their determinant, the convergence rate depends solely on the eigenvalue distribution of matrix \mathbf{M \bM , and has an analytically tractable form.
Revised:  for smooth and convex objectives, which uses random coordinate blocks of a Hessian-over-approximation matrix \mathbf{M \bM . The convergence analysis of the algorithm is challenging because of its complex dependence on the structure of \mathbf{M \bM . However, we show that when the coordinate blocks are sampled with probability proportional to their determinant, the convergence rate depends solely on the eigenvalue distribution of matrix \mathbf{M \bM , and has an analytically tractable form.
Edit intent: clarity
Original:  for smooth and convex objectives, which uses random coordinate blocks of a Hessian-over-approximation matrix \mathbf{M \bM  . The convergence analysis of RNM is challenging because of its complex dependence on the structure of \mathbf{M \bM . However, we show that when the coordinate blocks are sampled with probability proportional to their determinant, the convergence rate depends solely on the eigenvalue distribution of matrix \mathbf{M \bM , and has an analytically tractable form.
Revised:  for smooth and convex objectives, which uses random coordinate blocks of a Hessian-over-approximation matrix \mathbf{M \bM . The convergence analysis of RNM is challenging because of its complex dependence on the structure of \bM . However, we show that when the coordinate blocks are sampled with probability proportional to their determinant, the convergence rate depends solely on the eigenvalue distribution of matrix \mathbf{M \bM , and has an analytically tractable form.
Edit intent: fluency
Original:  for smooth and convex objectives, which uses random coordinate blocks of a Hessian-over-approximation matrix \mathbf{M \bM  . The convergence analysis of RNM is challenging because of its complex dependence on the structure of \mathbf{M \bM . However, we show that when the coordinate blocks are sampled with probability proportional to their determinant, the convergence rate depends solely on the eigenvalue distribution of matrix \mathbf{M \bM , and has an analytically tractable form.
Revised:  for smooth and convex objectives, which uses random coordinate blocks of a Hessian-over-approximation matrix \mathbf{M \bM . The convergence analysis of RNM is challenging because of its complex dependence on the structure of \mathbf{M \bM . However, we show that when the coordinate blocks are sampled with probability proportional to their determinant, the convergence rate depends solely on the eigenvalue distribution of matrix \bM , and has an analytically tractable form.
Edit intent: fluency
Original:  We show that determinantal sampling allows us to reason about the optimal subset size of blocks in terms of the spectrum of \mathbf{M \bM . Additionally, we provide a numerical evaluation of our analysis, demonstrating cases where determinantal sampling is superior or on par with uniform sampling.
Revised:  We show that determinantal sampling allows us to reason about the optimal subset size of blocks in terms of the spectrum of \bM . Additionally, we provide a numerical evaluation of our analysis, demonstrating cases where determinantal sampling is superior or on par with uniform sampling.
Edit intent: coherence
Original: This paper introduces Rumble, an engine that executes JSONiq queries on large, heterogenous and nested collections of JSON objects, leveraging the parallel capabilities of Spark so as to provide a high degree of data independence.
Revised: This paper introduces Rumble, an engine that executes JSONiq queries on large, heterogeneous and nested collections of JSON objects, leveraging the parallel capabilities of Spark so as to provide a high degree of data independence.
Edit intent: fluency
Original:  The JSONiq code is concise in comparison to Spark's host languages while seamlessly supporting the nested, heterogeneous datasets that Spark SQL does not.
Revised:  The JSONiq code is concise in comparison to Spark's host languages while seamlessly supporting the nested, heterogeneous data sets that Spark SQL does not.
Edit intent: clarity
Original:  This demonstrates that a language such as JSONiq is a simple and viable approach to large-scale querying of denormalized, heterogeneous, arborescent datasets , in the same way as SQL can be leveraged for structured datasets .
Revised:  This demonstrates that a language such as JSONiq is a simple and viable approach to large-scale querying of denormalized, heterogeneous, arborescent data sets , in the same way as SQL can be leveraged for structured datasets .
Edit intent: clarity
Original:  This demonstrates that a language such as JSONiq is a simple and viable approach to large-scale querying of denormalized, heterogeneous, arborescent datasets , in the same way as SQL can be leveraged for structured datasets .
Revised:  This demonstrates that a language such as JSONiq is a simple and viable approach to large-scale querying of denormalized, heterogeneous, arborescent datasets , in the same way as SQL can be leveraged for structured data sets .
Edit intent: clarity
Original:  The results also illustrate that Codd's concept of data independence makes as much sense for heterogeneous, nested datasets as it does on highly structured tables.
Revised:  The results also illustrate that Codd's concept of data independence makes as much sense for heterogeneous, nested data sets as it does on highly structured tables.
Edit intent: clarity
Original: This paper introduces Rumble, an engine that executes JSONiq queries on large, heterogeneous  and nested collections of JSON objects , leveraging the parallel capabilities of Spark so as to provide a high degree of data independence.
Revised: This paper introduces Rumble, a query execution engine for large, heterogeneous and nested collections of JSON objects , leveraging the parallel capabilities of Spark so as to provide a high degree of data independence.
Edit intent: clarity
Original: This paper introduces Rumble, an engine that executes JSONiq queries on large, heterogeneous  and nested collections of JSON objects , leveraging the parallel capabilities of Spark so as to provide a high degree of data independence.
Revised: This paper introduces Rumble, an engine that executes JSONiq queries on large, heterogeneous , and nested collections of JSON objects , leveraging the parallel capabilities of Spark so as to provide a high degree of data independence.
Edit intent: fluency
Original: We consider the Nash equilibrium seeking problem for a group of noncooperative agents, in a partial-decision information scenario .
Revised: We address the Nash equilibrium seeking problem for a group of noncooperative agents, in a partial-decision information scenario .
Edit intent: clarity
Original: We consider the Nash equilibrium seeking problem for a group of noncooperative agents, in a partial-decision information scenario .
Revised: We consider the Nash equilibrium problem in a partial-decision information scenario .
Edit intent: clarity
Original:  Then, we design a novel fully distributed, single-layer, fixed-step algorithm, which is a suitably preconditioned proximal-point iteration .
Revised:  Then, we demonstrate that the resulting inclusion can be solved in a fully-distributed way via a proximal-point iteration .
Edit intent: clarity
Original:  Then, we design a novel fully distributed, single-layer, fixed-step algorithm, which is a suitably preconditioned proximal-point iteration .
Revised:  Then, we design a novel fully distributed, single-layer, fixed-step algorithm, which is a suitably preconditioned proximal-point method, thanks to the use of a novel preconditioning matrix .
Edit intent: clarity
Original:  We prove its convergence to a Nash equilibrium with geometric rate, by leveraging restricted monotonicity properties, under strong monotonicity and Lipschitz continuity of the game mapping.
Revised:  We prove its convergence to a Nash equilibrium , under strong monotonicity and Lipschitz continuity of the game mapping.
Edit intent: clarity
Original:  Remarkably , we show that our algorithm outperforms known gradient-based schemes, both in terms of theoretical convergence rate and in practice according to our numerical experience .
Revised:  Furthermore , we show that our algorithm outperforms known gradient-based schemes, both in terms of theoretical convergence rate and in practice according to our numerical experience .
Edit intent: coherence
Original:  Remarkably , we show that our algorithm outperforms known gradient-based schemes, both in terms of theoretical convergence rate and in practice according to our numerical experience .
Revised:  Remarkably , we show that our algorithm outperforms known gradient-based schemes, both in terms of theoretical convergence rate and in practice , via numerical simulations .
Edit intent: clarity
Original:   We prove linear convergence of our algorithm to a Nash equilibrium , under strong monotonicity and Lipschitz continuity of the game mapping .
Revised:  Under strong monotonicity and Lipschitz continuity of the game mapping, We prove linear convergence of our algorithm to a Nash equilibrium , under strong monotonicity and Lipschitz continuity of the game mapping .
Edit intent: fluency
Original:   We prove linear convergence of our algorithm to a Nash equilibrium , under strong monotonicity and Lipschitz continuity of the game mapping .
Revised:  We prove linear convergence of our algorithm to a Nash equilibrium .
Edit intent: clarity
Original:  Therefore, we propose to adopt a self-supervised learning technique, which is able to estimate (relative) pitch without any form of supervision.
Revised:  Therefore, we propose to adopt a self-supervised learning technique, which is able to estimate pitch without any form of supervision.
Edit intent: clarity
Original:  Our results show that the proposed method is able to estimate pitch at a level of accuracy comparable to fully supervised models, both on clean and noisy audio samples, yet it does not require access to large labeled datasets 
Revised:  Our results show that the proposed method is able to estimate pitch at a level of accuracy comparable to fully supervised models, both on clean and noisy audio samples, although it does not require access to large labeled datasets 
Edit intent: coherence
Original:  Our results show that the proposed method is able to estimate pitch at a level of accuracy comparable to fully supervised models, both on clean and noisy audio samples, yet it does not require access to large labeled datasets 
Revised:  Our results show that the proposed method is able to estimate pitch at a level of accuracy comparable to fully supervised models, both on clean and noisy audio samples, yet it does not require access to large labeled datasets .
Edit intent: fluency
Original:  In this paper we refine the hierarchy of nondeterministic nominal automata, by developing the theory of residual  automata.
Revised:  In this paper we refine the hierarchy of nondeterministic nominal automata, by developing the theory of residual nominal automata.
Edit intent: fluency
Original:  In particular, we show that they admit canonical minimal representatives, and  the universality problem becomes decidable.
Revised:  In particular, we show that they admit canonical minimal representatives, and that the universality problem becomes decidable.
Edit intent: fluency
Original: We propose two application-layer coding schemes for delay-constrained point-to-point packet communications over frequency bands with restrictions on the transmitter's maximum duty-cycle.
Revised: We propose two application-layer coding schemes for delay-constrained point-to-point packet communications with restrictions on the transmitter's maximum duty-cycle.
Edit intent: clarity