wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Inspired by the success of our recently proposed LinearFold algorithm that computes the MFE structure in linear time, we propose a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base pairing probabilities . LinearPartition is 256x faster than Vienna RNAfold for a sequence with length 15,780, and 2,771x faster than CONTRAfold for a sequence with length 32,753 ( 2.5 days vs. 1.3 minutes ).
Revised:  Inspired by the success of our recently proposed LinearFold algorithm that computes the MFE structure in linear time, we propose a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base pairing probabilities . LinearPartition is 256x faster than Vienna RNAfold and CONTRAfold (e.g., 2.5 days vs. 1.3 minutes ).
Edit intent: clarity
Original:  Inspired by the success of our recently proposed LinearFold algorithm that computes the MFE structure in linear time, we propose a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base pairing probabilities . LinearPartition is 256x faster than Vienna RNAfold for a sequence with length 15,780, and 2,771x faster than CONTRAfold for a sequence with length 32,753 ( 2.5 days vs. 1.3 minutes ). Interestingly, although LinearPartition is approximate, the resulting base pairing probabilities are  better correlated with the ground truth structures and lead to a small accuracy improvement  on families with the longest length sequences (16S and 23S rRNA) when used for downstream structure prediction , as well as a substantial improvement on long-distance base pairs (500+ nt apart).
Revised:  Inspired by the success of our recently proposed LinearFold algorithm that computes the MFE structure in linear time, we propose a similar linear-time heuristic algorithm, LinearPartition, to approximate the partition function and base pairing probabilities . LinearPartition is 256x faster than Vienna RNAfold for a sequence with length 15,780, and 2,771x faster than CONTRAfold for a sequence with length 32,753 ( 2.5 days vs. 1.3 minutes on a sequence with length 32,753 nt). More interestingly, the resulting base pairing probabilities are better correlated with the ground truth structures and lead to a small accuracy improvement on families with the longest length sequences (16S and 23S rRNA) when used for downstream structure prediction , as well as a substantial improvement on long-distance base pairs (500+ nt apart).
Edit intent: clarity
Original:  Interestingly, although LinearPartition is approximate, the resulting base pairing probabilities are  better correlated with the ground truth structures and lead to a small accuracy improvement  on families with the longest length sequences (16S and 23S rRNA) when used for downstream structure prediction , as well as a substantial improvement on long-distance base pairs (500+ nt apart).
Revised:  Interestingly, although LinearPartition is approximate, the resulting base pairing probabilities are even better correlated with the ground truth structures and lead to a small accuracy improvement on families with the longest length sequences (16S and 23S rRNA) when used for downstream structure prediction , as well as a substantial improvement on long-distance base pairs (500+ nt apart).
Edit intent: meaning-changed
Original:  Interestingly, although LinearPartition is approximate, the resulting base pairing probabilities are  better correlated with the ground truth structures and lead to a small accuracy improvement  on families with the longest length sequences (16S and 23S rRNA) when used for downstream structure prediction , as well as a substantial improvement on long-distance base pairs (500+ nt apart).
Revised:  Interestingly, although LinearPartition is approximate, the resulting base pairing probabilities are better correlated with the ground truth structures . LinearPartition also leads to a small accuracy improvement on families with the longest length sequences (16S and 23S rRNA) when used for downstream structure prediction , as well as a substantial improvement on long-distance base pairs (500+ nt apart).
Edit intent: coherence
Original:  Interestingly, although LinearPartition is approximate, the resulting base pairing probabilities are  better correlated with the ground truth structures and lead to a small accuracy improvement  on families with the longest length sequences (16S and 23S rRNA) when used for downstream structure prediction , as well as a substantial improvement on long-distance base pairs (500+ nt apart).
Revised:  Interestingly, although LinearPartition is approximate, the resulting base pairing probabilities are better correlated with the ground truth structures and lead to a small accuracy improvement when used for downstream structure prediction on families with the longest length sequences (16S and 23S rRNA) when used for downstream structure prediction , as well as a substantial improvement on long-distance base pairs (500+ nt apart).
Edit intent: fluency
Original:  Interestingly, although LinearPartition is approximate, the resulting base pairing probabilities are  better correlated with the ground truth structures and lead to a small accuracy improvement  on families with the longest length sequences (16S and 23S rRNA) when used for downstream structure prediction , as well as a substantial improvement on long-distance base pairs (500+ nt apart).
Revised:  Interestingly, although LinearPartition is approximate, the resulting base pairing probabilities are better correlated with the ground truth structures and lead to a small accuracy improvement on families with the longest length sequences (16S and 23S rRNA) , as well as a substantial improvement on long-distance base pairs (500+ nt apart).
Edit intent: clarity
Original:  This note shows how to adapt the main technique used for rental harmony with miserly tenants, using a variant of Sperner's lemma, to rental harmony with quasilinear tenants .
Revised:  This note shows how to adapt the main technique used for rental harmony with miserly tenants, using a variant of Sperner's lemma, to a much more general class of preferences, that contains both miserly and quasilinear tenants as special cases .
Edit intent: meaning-changed
Original:  In this paper, we propose LayoutLM to jointly model the interaction between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents.
Revised:  In this paper, we propose the LayoutLM to jointly model the interaction between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents.
Edit intent: clarity
Original:  To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training , leading to significant performance improvement in downstream tasks for document image understanding.
Revised:  To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training . It achieves new state-of-the-art results in several downstream tasks, including receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42). The code and pre-trained LayoutLM models will be available soon at URL
Edit intent: meaning-changed
Original:  It achieves new state-of-the-art results in several downstream tasks, including  receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42).
Revised:  It achieves new state-of-the-art results in several downstream tasks, including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42).
Edit intent: meaning-changed
Original:  In this paper, we propose the LayoutLM to jointly model the interaction between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents.
Revised:  In this paper, we propose the LayoutLM to jointly model the interaction between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents.
Edit intent: fluency
Original:  In this paper, we propose the LayoutLM to jointly model the interaction between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents.
Revised:  In this paper, we propose the LayoutLM to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents.
Edit intent: fluency
Original: Controlling an evolving population is an important task in modern molecular genetics, including in directed evolution to improve the activity of molecules and enzymes,  breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens.
Revised: Controlling an evolving population is an important task in modern molecular genetics, including directed evolution for improving the activity of molecules and enzymes, breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens.
Edit intent: clarity
Original: Controlling an evolving population is an important task in modern molecular genetics, including in directed evolution to improve the activity of molecules and enzymes,  breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens.
Revised: Controlling an evolving population is an important task in modern molecular genetics, including in directed evolution to improve the activity of molecules and enzymes, in breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens.
Edit intent: fluency
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. The main use-cases are (a) compare your (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experiment SOTA methods on your custom dataset (which should contain landmark annotation).
Revised:  The BIRL framework has a few key features , such as : easily extendable, performance evaluation, parallel experimenting , simple visualisations, experiment's time-out limit, resuming unfinished experiments. From the research practice, we identified and focused on these two main use-cases are (a) compare your (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experiment SOTA methods on your custom dataset (which should contain landmark annotation).
Edit intent: meaning-changed
Original:  The main use-cases are (a) compare your (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experiment SOTA methods on your custom dataset (which should contain landmark annotation).
Revised:  The main use-cases : (a) compare your (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experiment SOTA methods on your custom dataset (which should contain landmark annotation).
Edit intent: coherence
Original:  The main use-cases are (a) compare your (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experiment SOTA methods on your custom dataset (which should contain landmark annotation).
Revised:  The main use-cases are (a) comparison of user's (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experiment SOTA methods on your custom dataset (which should contain landmark annotation).
Edit intent: clarity
Original:  The main use-cases are (a) compare your (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experiment SOTA methods on your custom dataset (which should contain landmark annotation).
Revised:  The main use-cases are (a) compare your (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experimenting SOTA methods on your custom dataset (which should contain landmark annotation).
Edit intent: fluency
Original:  The main use-cases are (a) compare your (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experiment SOTA methods on your custom dataset (which should contain landmark annotation).
Revised:  The main use-cases are (a) compare your (newly developed) method with some State-of-the-Art (SOTA) methods on a common dataset and (b) experiment SOTA methods on user's custom dataset (which should contain landmark annotation).
Edit intent: clarity
Original:  In this paper , we present mixed-methods aiming at bio-medical imaging and experimental result on CIMA dataset. However, any other methods for other domain can be added or costume dataset to be used. URL
Revised:  In this paper , we present an integration of several standard image registration methods aiming at biomedical imaging into the BIRL framework. This report also contains experimental results of these SOTA methods on the CIMA dataset, which is a dataset of Whole Slice Imaging (WSI) from histology/pathology containing several multi-stain tissue samples from three tissue kinds. Source and results: URL
Edit intent: meaning-changed
Original: The source code of a program not only serves as a formal description of an executable task, but it also serves to communicate developer intent in a human-readable form. To facilitate this, developers use meaningful identifier names and natural-language documentation. This makes it possible to successfully apply sequence-modeling approaches, shown to be effective in natural-language processing, to source code . A major advancement in natural-language understanding has been the use of pre-trained token embeddings;
Revised: Recent research has achieved impressive results on understanding and improving source code by building up on machine-learning techniques developed for natural languages. A significant advancement in natural-language understanding has been the use of pre-trained token embeddings;
Edit intent: meaning-changed
Original:  A major advancement in natural-language understanding has been the use of pre-trained token embeddings; BERT and other works have further shown that pre-trained contextual embeddingscan be extremely powerful and can be fine-tuned effectively for a variety of downstream supervised tasks .
Revised:  A major advancement in natural-language understanding has been the use of pre-trained contextual embeddings, such as BERT, which can be fine-tuned effectively for a variety of downstream supervised tasks .
Edit intent: clarity
Original:  BERT and other works have further shown that pre-trained contextual embeddingscan be extremely powerful and can be fine-tuned effectively for a variety of downstream supervised tasks . Inspired by these developments, we present the first attempt to replicate this success on source code. We curate a massive corpus of Python programs from GitHub to pre-train a BERT model, which we call Code Understanding BERT (CuBERT).
Revised:  BERT and other works have further shown that pre-trained contextual embeddingscan be extremely powerful and can be fine-tuned for downstream tasks with less labeled data and training budget, while achieving better accuracies. However, there is no attempt yet to obtain a high-quality contextual embedding of source code, and to evaluate it on multiple program-understanding tasks simultaneously; that is the gap that this paper aims to mitigate. Specifically, first, we curate a massive corpus of Python programs from GitHub to pre-train a BERT model, which we call Code Understanding BERT (CuBERT).
Edit intent: meaning-changed
Original:  We curate a massive corpus of Python programs from GitHub to pre-train a BERT model, which we call Code Understanding BERT (CuBERT). We also pre-train Word2Vec embeddings on the same dataset. We create a benchmark of five classification tasks and compare fine-tuned CuBERT against sequence models trained with and without the Word2Vec embeddings.
Revised:  We curate a massive corpus of Python programs from GitHub to pre-train CuBERT, an open-sourced code-understanding BERT model; and, second, we create an open-sourced benchmark that comprises five classification tasks and compare fine-tuned CuBERT against sequence models trained with and without the Word2Vec embeddings.
Edit intent: clarity
Original:  We create a benchmark of five classification tasks and compare fine-tuned CuBERT against sequence models trained with and without the Word2Vec embeddings.
Revised:  We create a benchmark of five classification tasks and one program-repair task, akin to code-understanding tasks proposed in the literature before. We fine-tune CuBERT on our benchmark tasks, and compare the resulting models to different variants of Word2Vec embeddings.
Edit intent: meaning-changed
Original:  We create a benchmark of five classification tasks and compare fine-tuned CuBERT against sequence models trained with and without the Word2Vec embeddings. Our results show that CuBERT outperforms the baseline methods by a margin of 2.9-22\%.
Revised:  We create a benchmark of five classification tasks and compare fine-tuned CuBERT against sequence models trained with and without the Word2Vec token embeddings, BiLSTM and Transformer models, as well as published state-of-the-art models, showing that CuBERT outperforms the baseline methods by a margin of 2.9-22\%.
Edit intent: meaning-changed
Original:  Our results show that CuBERT outperforms the baseline methods by a margin of 2.9-22\%. We also show its superiority when fine-tuned with smaller datasets, and over fewer epochs. We further evaluate CuBERT's effectiveness on a joint classification, localization and repair task involving prediction of two pointers .
Revised:  Our results show that CuBERT outperforms them all, even with shorter training, and with fewer labeled examples. Future work on source-code embedding can benefit from reusing our benchmark, and comparing against CuBERT models as a strong baseline .
Edit intent: clarity
Original:  Specifically, first, we curate a massive, deduplicated corpus of 6M Python files from GitHub, which we use to pre-train CuBERT, an open-sourced code-understanding BERT model;
Revised:  Specifically, first, we curate a massive, deduplicated corpus of 7.4M Python files from GitHub, which we use to pre-train CuBERT, an open-sourced code-understanding BERT model;
Edit intent: meaning-changed
Original: Partitioning problems where the goal is to partition a graph into connected components are known to be NP-hard for some graph classes but polynomial-time solvable for others. We consider different variants of the (l,u)-partition problem: the p-(l,u)-partition problem and the minimum resp. maximum (l,u)-partition problem. That is partitioning a graph into exactly p or a minimal resp. maximal number of clusters such that every cluster fulfills the following weight condition: the weight of each cluster should to be at least l and at most u.
Revised: Partitioning problems where the goal is to partition a graph into connected components such that each component fulfills upper and lower weight constraints. Partitioning into a minimum, maximum or a fixed number of clusters such that every cluster fulfills the following weight condition: the weight of each cluster should to be at least l and at most u.
Edit intent: clarity
Original:  That is partitioning a graph into exactly p or a minimal resp. maximal number of clusters such that every cluster fulfills the following weight condition: the weight of each cluster should to be at least l and at most u. These kind of partitioning problems are NP-hard for series-parallel graphs but can be solved in polynomial time on trees.
Revised:  That is partitioning a graph into exactly p or a minimal resp. maximal number of clusters is NP-hard for series-parallel graphs but can be solved in polynomial time on trees.
Edit intent: clarity
Original:  In this paper we present partitioning algorithms for cactus graphs to show that these partition problems are polynomial-time solvable for this graph class as well .
Revised:  In this paper we present partitioning algorithms for cactus graphs . For other optimization goals or additional constraints, the partition problem becomes NP-hard even on trees and for a lower weight bound equal to zero. We show that our method can be used as an algorithmic framework to solve other partition problems for cactus graphs in pseudo-polynomial time .
Edit intent: meaning-changed
Original: Knowing the assembly-crucial interaction of virus capsid help us understand its assembly process. Validating assembly-crucial interaction via mutagenesis experiments in wet lab is usually very slow. We predict the assembly crucial interactions by measuring the entropy change of the capsid assembly system before and after the removal of each interaction.
Revised: We predict crucial inter-atomic interactions between monomers for the autonomous assembly of three icosahedral viral capsids, Adeno Associated Virus type 2 (AAV2), Murine Parvo Virus (MVM), and Bromo Mosaic Virus (BMV), by approximating changes in the partition function of the capsid assembly system before and after the removal of each interaction.
Edit intent: clarity
Original:  We predict the assembly crucial interactions by measuring the entropy change of the capsid assembly system before and after the removal of each interaction. The entropy-change is measured by analysis of the viral capsid configuration space on two levels. On the interface level the entropy of the small interface assembly system is approximated by the sampling of the configurational space using the recently reported geometric method called  EASAL (efficient atlasing and sampling of assembly landscapes) .
Revised:  We predict the assembly crucial interactions by measuring the entropy change of the capsid assembly landscape when an interaction is removed. At the interface-scale, the partition function computation uses EASAL (efficient atlasing and sampling of assembly landscapes) .
Edit intent: clarity
Original:  On the interface level the entropy of the small interface assembly system is approximated by the sampling of the configurational space using the recently reported geometric method called  EASAL (efficient atlasing and sampling of assembly landscapes) .
Revised:  On the interface level the entropy of the small interface assembly system is approximated by the sampling of the configurational space using the recently reported geometric method calledatlases EASAL (efficient atlasing and sampling of assembly landscapes) .
Edit intent: meaning-changed
Original:  On the interface level the entropy of the small interface assembly system is approximated by the sampling of the configurational space using the recently reported geometric method called  EASAL (efficient atlasing and sampling of assembly landscapes) .
Revised:  On the interface level the entropy of the small interface assembly system is approximated by the sampling of the configurational space using the recently reported geometric method called of selected subassembly landscapes for each interface type and their abbreviated EASAL (efficient atlasing and sampling of assembly landscapes) .
Edit intent: meaning-changed