wanyu/IteraTeR_v2 huggingface dataset:
Original:  Third, it introduces a new corpus that is both of great interest to current STS research and an exemplary case for the new methodology presented here : the 1995-2017 collection of abstracts presented at ASCO, the largest annual oncology research conference.
Revised:  Third, it introduces a new corpus that is relevant to current STS research and an exemplary case for the new methodology presented here : the 1995-2017 collection of abstracts presented at ASCO, the largest annual oncology research conference.
Edit intent: clarity
Original:  Third, it introduces a new corpus that is both of great interest to current STS research and an exemplary case for the new methodology presented here : the 1995-2017 collection of abstracts presented at ASCO, the largest annual oncology research conference.
Revised:  Third, it introduces a new corpus that is both of great interest to current STS research , and an interesting case for our approach : the 1995-2017 collection of abstracts presented at ASCO, the largest annual oncology research conference.
Edit intent: clarity
Original:  Third, it introduces a new corpus that is both of great interest to current STS research and an exemplary case for the new methodology presented here : the 1995-2017 collection of abstracts presented at ASCO, the largest annual oncology research conference.
Revised:  Third, it introduces a new corpus that is both of great interest to current STS research and an exemplary case for the new methodology presented here : the 1995-2017 collection of abstracts presented at ASCO, the main annual oncology research conference.
Edit intent: clarity
Original:  The results are compared to solutions from a CFD code developed at ONERA, assessing the efficiency and potential of the method, and the impact of the modelling assumptions is assessed through parametric studies.
Revised:  The results are compared to solutions from a CFD code developed at ONERA, assessing the efficiency and potential of the method, and the impact of the modelling assumptions is evaluated through parametric studies.
Edit intent: fluency
Original:  We develop an information theoretical framework and show that molecular time-scales for evolution under natural selection can inform how to monitor a population  to acquire sufficient predictive information for an effective intervention with artificial selection.
Revised:  We develop an information theoretical framework and show that molecular time-scales for evolution under natural selection can inform how to monitor a population in order to acquire sufficient predictive information for an effective intervention with artificial selection.
Edit intent: coherence
Original:  Our formalism opens a new avenue for devising optimal artificial selection for directed evolution of molecular functions.
Revised:  Our formalism opens a new avenue for devising artificial selection methods for directed evolution of molecular functions.
Edit intent: clarity
Original: This paper considers the  subgraphs of an input graph that satisfy a given property and are maximal under inclusion.
Revised: This paper considers the enumeration of subgraphs of an input graph that satisfy a given property and are maximal under inclusion.
Edit intent: clarity
Original:  The main result is a seemingly novel technique, proximity search, to list these subgraphs in polynomial delay each .
Revised:  The main result is a seemingly novel technique, proximity search, to list these subgraphs in polynomial delay .
Edit intent: clarity
Original:  Using known techniques, such as reverse search, the space of sought solutions induces an implicit directed graph called ``solution graph'': however, the latter can give rise to exponential out-degree, thus preventing polynomial delay .
Revised:  Using known techniques, such as reverse search, the space of sought solutions induces an implicit directed graph called ``solution graph'': however, the latter can give rise to exponential out-degree, thus requiring exponential time to be spent on each solution .
Edit intent: clarity
Original:  The novelty of our algorithm in this paper consists in providing a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, so that it still remains strongly connected and guarantees that all solutions can be reported with polynomial delay by a suitable traversal.
Revised:  The novelty of proximity search is a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, so that it still remains strongly connected and guarantees that all solutions can be reported with polynomial delay by a suitable traversal.
Edit intent: clarity
Original:  The novelty of our algorithm in this paper consists in providing a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, so that it still remains strongly connected and guarantees that all solutions can be reported with polynomial delay by a suitable traversal.
Revised:  The novelty of our algorithm in this paper consists in providing a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, but such that it remains strongly connected and guarantees that all solutions can be reported with polynomial delay by a suitable traversal.
Edit intent: coherence
Original:  The novelty of our algorithm in this paper consists in providing a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, so that it still remains strongly connected and guarantees that all solutions can be reported with polynomial delay by a suitable traversal.
Revised:  The novelty of our algorithm in this paper consists in providing a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, so that it still remains strongly connected , so all solutions can be reported with polynomial delay by a suitable traversal.
Edit intent: coherence
Original:  The novelty of our algorithm in this paper consists in providing a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, so that it still remains strongly connected and guarantees that all solutions can be reported with polynomial delay by a suitable traversal.
Revised:  The novelty of our algorithm in this paper consists in providing a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, so that it still remains strongly connected and guarantees that all solutions can be enumerated in polynomial delay by a suitable traversal.
Edit intent: clarity
Original:  however,  this graph can have exponential out-degree, thus requiring exponential time to be spent on each solution.
Revised:  however, nodes in this graph can have exponential out-degree, thus requiring exponential time to be spent on each solution.
Edit intent: clarity
Original:  The novelty of proximity search is a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, but such that it remains strongly connected, so  all solutions can be enumerated in polynomial delay by a traversal.
Revised:  The novelty of proximity search is a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, but that remain strongly connected, so all solutions can be enumerated in polynomial delay by a traversal.
Edit intent: clarity
Original:  The novelty of proximity search is a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, but such that it remains strongly connected, so  all solutions can be enumerated in polynomial delay by a traversal.
Revised:  The novelty of proximity search is a technique for generating a better solution graph, significantly reducing the out-degree with respect to existing approaches, but such that it remains strongly connected, so that all solutions can be enumerated in polynomial delay by a traversal.
Edit intent: fluency
Original:  A drawback of this approach is the space required to keep track of visited solutions, which can be exponential: we further propose a technique to induce a parent-child relationship  and achieve polynomial space when suitable conditions are met.
Revised:  A drawback of this approach is the space required to keep track of visited solutions, which can be exponential: we further propose a technique to induce a parent-child relationship among solutions and achieve polynomial space when suitable conditions are met.
Edit intent: clarity
Original:  The BIRL framework has a few key features , such as : easily extendable, performance evaluation, parallel experimenting , simple visualisations, experiment's time-out limit, pause/resume experiments.
Revised:  The key features of the BIRL framework are : easily extendable, performance evaluation, parallel experimenting , simple visualisations, experiment's time-out limit, pause/resume experiments.
Edit intent: clarity
Original:  The BIRL framework has a few key features , such as : easily extendable, performance evaluation, parallel experimenting , simple visualisations, experiment's time-out limit, pause/resume experiments.
Revised:  The BIRL framework has a few key features , such as : easily extendable, performance evaluation, parallel experimentation , simple visualisations, experiment's time-out limit, pause/resume experiments.
Edit intent: fluency
Original:  In this paper , we present mixed-methods aiming at bio-medical imaging and experimental result on CIMA dataset.
Revised:  Moreover , we present mixed-methods aiming at bio-medical imaging and experimental result on CIMA dataset.
Edit intent: coherence
Original: We study robustness properties of inexact gradient descent for strongly convex functions, as well as for the larger class of functions with sector-bounded gradients, under a relative error model.
Revised: We study robustness properties of some iterative gradient-based methods for strongly convex functions, as well as for the larger class of functions with sector-bounded gradients, under a relative error model.
Edit intent: clarity
Original: When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than start with a randomly initialized one -- due to lacking enough training data , performing lifelong learning where the system has to learn a new task while being previously trained for other tasks, or wishing to encode priors in the network via preset weights .
Revised: When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights. Adaptation can be useful in cases when training data is scarce, when a single learner needs to perform multiple tasks, or wishing to encode priors in the network via preset weights .
Edit intent: clarity
Original: When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than start with a randomly initialized one -- due to lacking enough training data , performing lifelong learning where the system has to learn a new task while being previously trained for other tasks, or wishing to encode priors in the network via preset weights .
Revised: When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than start with a randomly initialized one -- due to lacking enough training data , performing lifelong learning where the system has to learn a new task while being previously trained for other tasks, or when one wishes to encode priors in the network via preset weights .
Edit intent: clarity
Original: When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than start with a randomly initialized one -- due to lacking enough training data , performing lifelong learning where the system has to learn a new task while being previously trained for other tasks, or wishing to encode priors in the network via preset weights .
Revised: When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than start with a randomly initialized one -- due to lacking enough training data , performing lifelong learning where the system has to learn a new task while being previously trained for other tasks, or wishing to encode priors in the network .
Edit intent: clarity
Original:  In this paper, we propose a straightforward alternative: Side-Tuning . Side-tuning adapts a pre-trained network by training a lightweight "side" network that is fused with the (unchanged) pre-trained network using summation.
Revised:  In this paper, we propose a straightforward alternative: side-tuning . Side-tuning adapts a pre-trained network by training a lightweight "side" network that is fused with the (unchanged) pre-trained network using summation.
Edit intent: fluency
Original:  In this paper, we propose a straightforward alternative: Side-Tuning . Side-tuning adapts a pre-trained network by training a lightweight "side" network that is fused with the (unchanged) pre-trained network using summation.
Revised:  In this paper, we propose a straightforward alternative: Side-Tuning . Side-tuning adapts a pre-trained network by training a lightweight "side" network that is fused with the (unchanged) pre-trained network via summation.
Edit intent: clarity
Original:  This simple method works as well as or better than existing solutions while it resolves some of the basic issues with fine-tuning, fixed features, and several other common baselines .
Revised:  This simple method works as well as or better than existing solutions and it resolves some of the basic issues with fine-tuning, fixed features, and several other common baselines .
Edit intent: coherence
Original:  This simple method works as well as or better than existing solutions while it resolves some of the basic issues with fine-tuning, fixed features, and several other common baselines .
Revised:  This simple method works as well as or better than existing solutions while it resolves some of the basic issues with fine-tuning, fixed features, and other common approaches .
Edit intent: clarity
Original:  In particular, side-tuning is less prone to overfitting when little training data is available, yields better results than using a fixed feature extractor, and does not suffer from catastrophic forgetting in lifelong learning.
Revised:  In particular, side-tuning is less prone to overfitting when little training data is available, yields better results than using a fixed feature extractor, and does not suffer from catastrophic forgetting in incremental learning.
Edit intent: clarity
Original:  We demonstrate the performance of side-tuning under a diverse set of scenarios, including lifelong learning (iCIFAR, Taskonomy ), reinforcement learning, imitation learning (visual navigation in Habitat), NLP question-answering (SQuAD v2), and single-task transfer learning (Taskonomy), with consistently promising results.
Revised:  We demonstrate the performance of side-tuning under a diverse set of scenarios, including lifelong learning (iCIFAR, iTaskonomy ), reinforcement learning, imitation learning (visual navigation in Habitat), NLP question-answering (SQuAD v2), and single-task transfer learning (Taskonomy), with consistently promising results.
Edit intent: clarity
Original: This document  ] contains the computer generated part of the proof of the main result in [12], giving a complete and general list of tree representations corresponding to exceptional modules over the path algebra of the canonically oriented Euclidean quiver \mathbb{E}_6.
Revised: This document 12] contains the computer generated part of the proof of the main result in [12], giving a complete and general list of tree representations corresponding to exceptional modules over the path algebra of the canonically oriented Euclidean quiver \mathbb{E}_6.
Edit intent: fluency
Original:  The proofs (involving induction and symbolic computation with block matrices) were partially generated by a purposefully developed computer software, outputting in a detailed step-by-step fashion as if written "by hand".
Revised:  The proof (involving induction and symbolic computation with block matrices) were partially generated by a purposefully developed computer software, outputting in a detailed step-by-step fashion as if written "by hand".
Edit intent: fluency
Original:  The proofs (involving induction and symbolic computation with block matrices) were partially generated by a purposefully developed computer software, outputting in a detailed step-by-step fashion as if written "by hand".
Revised:  The proofs (involving induction and symbolic computation with block matrices) was partially generated by a purposefully developed computer software, outputting in a detailed step-by-step fashion as if written "by hand".
Edit intent: fluency
Original:  Tree representations are exhibited using matrices involving only the elements 0 and 1, and all representations enlisted in this document remain valid over any base field (since the symbolic block matrix computations present inhere hold in any field) .
Revised:  Tree representations are exhibited using matrices involving only the elements 0 and 1, and all representations enlisted in this document remain valid over any base field .
Edit intent: coherence
Original: This document (together with ]  the ancillary file e6_proof.pdf ) is an\mathbb{E} appendix to the paper [12] .
Revised: This document (together with ] ( the ancillary file e6_proof.pdf ) is an\mathbb{E} appendix to the paper [12] .
Edit intent: fluency
Original:  The ancillary file contains\mathbb{D} the computer generated part of the proof of the main result in [   12], giving a complete and general list of tree representations corresponding to exceptional modules over the path algebra of the canonically oriented Euclidean quiver \mathbb{E}_6 .
Revised:  The ancillary file contains\mathbb{D} the computer generated part of the proofs of the main result in [ 12], giving a complete and general list of tree representations corresponding to exceptional modules over the path algebra of the canonically oriented Euclidean quiver \mathbb{E}_6 .
Edit intent: fluency
Original:  The ancillary file contains\mathbb{D} the computer generated part of the proof of the main result in [   12], giving a complete and general list of tree representations corresponding to exceptional modules over the path algebra of the canonically oriented Euclidean quiver \mathbb{E}_6 .
Revised:  The ancillary file contains\mathbb{D} the computer generated part of the proof of the main results in [ 12], giving a complete and general list of tree representations corresponding to exceptional modules over the path algebra of the canonically oriented Euclidean quiver \mathbb{E}_6 .
Edit intent: fluency
Original:  The ancillary file contains\mathbb{D} the computer generated part of the proof of the main result in [   12], giving a complete and general list of tree representations corresponding to exceptional modules over the path algebra of the canonically oriented Euclidean quiver \mathbb{E}_6 .
Revised:  The ancillary file contains\mathbb{D} the computer generated part of the proof of the main result in [ respectively 12], giving a complete and general list of tree representations corresponding to exceptional modules over the path algebra of the canonically oriented Euclidean quiver \mathbb{E}_6 .
Edit intent: fluency
Original:  The proof\widetilde{\mathbb{D}} (involving induction and symbolic computation with block matrices) was partially generated by a purposefully developed computer software, outputting in a detailed step-by-step fashion as if written "by hand".
Revised:  The proof\widetilde{\mathbb{D}} (involving induction and symbolic computation with block matrices) were partially generated by a purposefully developed computer software, outputting in a detailed step-by-step fashion as if written "by hand".
Edit intent: fluency
Original:  Tree representations are exhibited using matrices involving only the elements 0 and 1, and all representations enlisted in the ancillary document remain valid over any base field ]   .
Revised:  Tree representations are exhibited using matrices involving only the elements 0 and 1, and all representations enlisted in the ancillary document remain valid over any base field ] and .
Edit intent: clarity
Original:  Given a simulation of the Risk board game, we consider (a) talent to be one of three rule-based strategies  ;
Revised:  Given a simulation of the Risk board game, we consider (a) Talent to be one of three rule-based strategies ;
Edit intent: fluency
Original:  Given a simulation of the Risk board game, we consider (a) talent to be one of three rule-based strategies  ;
Revised:  Given a simulation of the Risk board game, we consider (a) talent to be one of three rule-based strategies used by players ;
Edit intent: clarity
Original:  (b) context as the setting of each run  with opponents' strategies, goals and luck;
Revised:  (b) Context as the setting of each run with opponents' strategies, goals and luck;
Edit intent: fluency
Original:  (b) context as the setting of each run  with opponents' strategies, goals and luck;
Revised:  (b) context as the setting of each run of the game with opponents' strategies, goals and luck;
Edit intent: clarity
Original:  and (c) perspective as the objective of each player.
Revised:  and (c) Perspective as the objective of each player.
Edit intent: fluency
Original:  Finally, we also find that  resilience and opportunity might be examined within the simulation provided .
Revised:  Finally, we also find that resilience and opportunity .
Edit intent: clarity
Original:  A major advancement in natural-language understanding has been the use of pre-trained token embeddings;
Revised:  A major advancement in natural-language understanding has come with the development of pre-trained token embeddings;
Edit intent: clarity
Original:  Future work on source-code embedding can benefit from reusing our benchmark, and  comparing against CuBERT models as a strong baseline.
Revised:  Future work on source-code embedding can benefit from reusing our benchmark, and from comparing against CuBERT models as a strong baseline.
Edit intent: fluency
Original:  Following on our previous study (Enoch Jaumard, 2018), where we used Integer Linear Programming, and proposed a Column Generation algorithm based on a Lightpath decomposition, which proved to be the most efficient so far, we now consider the traditional Configuration decomposition that has been studied in other works in the past.
Revised:  Following on our previous study Jaumard, 2018), where we used Integer Linear Programming, and proposed a Column Generation algorithm based on a Lightpath decomposition, which proved to be the most efficient so far, we now consider the traditional Configuration decomposition that has been studied in other works in the past.
Edit intent: clarity
Original:  Following on our previous study (Enoch Jaumard, 2018), where we used Integer Linear Programming, and proposed a Column Generation algorithm based on a Lightpath decomposition, which proved to be the most efficient so far, we now consider the traditional Configuration decomposition that has been studied in other works in the past.
Revised:  Following on our previous study (Enoch , where we used Integer Linear Programming, and proposed a Column Generation algorithm based on a Lightpath decomposition, which proved to be the most efficient so far, we now consider the traditional Configuration decomposition that has been studied in other works in the past.
Edit intent: coherence