wanyu/IteraTeR_full_sent huggingface dataset:
Original:  About 60\% of multiple affiliations involve institutions from within the academic sector and international co-affiliations  often involve institutions from the United States,  Germany and the United Kingdom as well as institutions in neighboring countries .
Revised:  About 60\% of multiple affiliations are between institutions from within the academic sector and international co-affiliations often involve institutions from the United States, Germany and the United Kingdom as well as institutions in neighboring countries .
Edit intent: clarity
Original:  About 60\% of multiple affiliations involve institutions from within the academic sector and international co-affiliations  often involve institutions from the United States,  Germany and the United Kingdom as well as institutions in neighboring countries .
Revised:  About 60\% of multiple affiliations involve institutions from within the academic sector . International co-affiliations often involve institutions from the United States, Germany and the United Kingdom as well as institutions in neighboring countries .
Edit intent: coherence
Original:  About 60\% of multiple affiliations involve institutions from within the academic sector and international co-affiliations  often involve institutions from the United States,  Germany and the United Kingdom as well as institutions in neighboring countries .
Revised:  About 60\% of multiple affiliations involve institutions from within the academic sector and international co-affiliations , which account for about a quarter of multiple affiliations, most often involve institutions from the United States, Germany and the United Kingdom as well as institutions in neighboring countries .
Edit intent: meaning-changed
Original:  About 60\% of multiple affiliations involve institutions from within the academic sector and international co-affiliations  often involve institutions from the United States,  Germany and the United Kingdom as well as institutions in neighboring countries .
Revised:  About 60\% of multiple affiliations involve institutions from within the academic sector and international co-affiliations often involve institutions from the United States, China, Germany and the United Kingdom as well as institutions in neighboring countries .
Edit intent: meaning-changed
Original:  About 60\% of multiple affiliations involve institutions from within the academic sector and international co-affiliations  often involve institutions from the United States,  Germany and the United Kingdom as well as institutions in neighboring countries .
Revised:  About 60\% of multiple affiliations involve institutions from within the academic sector and international co-affiliations often involve institutions from the United States, Germany and the United Kingdom , suggesting a core-periphery network. Network analysis also reveals a number communities of countries that are more likely to share affiliations .
Edit intent: meaning-changed
Original: A structure of a protein has a direct impact on its properties and functions.
Revised: Motivation: Bridging the exponential gap between the number of unlabeled and labeled protein sequences, a couple of works have adopted semi-supervised learning for protein sequence modeling. They pre-train a model with a substantial amount of unlabeled data and transfer the learned representations to various downstream tasks. Nonetheless, the current pre-training methods mostly rely on a language modeling pre-training task and often show limited performances. Therefore, a protein has a direct impact on its properties and functions.
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Original:  As natural language representation models capture syntactic and semantic information of words from a large unlabeled text corpus, PLUS captures structural information of amino acids from a large weakly labeled protein database. Since the Transformer encoder, BERT's original model architecture, has a severe computational requirement to handle long sequences, we first propose to combine a bidirectional recurrent neural network with the BERT-wise pre-training scheme.
Revised:  PLUS consists of masked language modeling and a protein-specific pre-training task, namely same family prediction. PLUS can be used to pre-train various model architectures. In this work, we mainly use PLUS to pre-train a recurrent neural network with the BERT-wise pre-training scheme.
Edit intent: meaning-changed
Original:  Since the Transformer encoder, BERT's original model architecture, has a severe computational requirement to handle long sequences, we first propose to combine a bidirectional recurrent neural network with the BERT-wise pre-training scheme.
Revised:  Since the Transformer encoder, BERT's original model architecture, has a severe computational requirement to handle long sequences, we first propose to combine a bidirectional recurrent neural network (RNN) and refer to the resulting model as PLUS-RNN. It advances the state-of-the-art pre-training scheme.
Edit intent: meaning-changed
Original:  Since the Transformer encoder, BERT's original model architecture, has a severe computational requirement to handle long sequences, we first propose to combine a bidirectional recurrent neural network with the BERT-wise pre-training scheme. PLUS is designed to learn protein representations with two pre-training objectives , i.e., masked language modeling and same family prediction.
Revised:  Since the Transformer encoder, BERT's original model architecture, has a severe computational requirement to handle long sequences, we first propose to combine a bidirectional recurrent neural network with the BERT-wise pre-training methods on six out of seven tasks , i.e., masked language modeling and same family prediction.
Edit intent: meaning-changed
Original:  PLUS is designed to learn protein representations with two pre-training objectives , i.e., masked language modeling and same family prediction. Then, the pre-trained model can be fine-tuned for a wide range of tasks without training randomly initialized task-specific models from scratch. It obtains new state-of-the-art results on both (1)  protein-level and (2) amino-acid-level tasks, outperforming many task-specific algorithms.
Revised:  PLUS is designed to learn protein representations with two pre-training objectives , i.e., (1) protein-level and (2) amino-acid-level tasks, outperforming many task-specific algorithms.
Edit intent: coherence
Original:  It obtains new state-of-the-art results on both (1)  protein-level and (2) amino-acid-level tasks, outperforming many task-specific algorithms.
Revised:  It obtains new state-of-the-art results on both (1) three protein(-pair)-level classification, (2) two protein-level and (2) amino-acid-level tasks, outperforming many task-specific algorithms.
Edit intent: meaning-changed
Original:  It obtains new state-of-the-art results on both (1)  protein-level and (2) amino-acid-level tasks, outperforming many task-specific algorithms.
Revised:  It obtains new state-of-the-art results on both (1) protein-level regression, and (3) two amino-acid-level tasks, outperforming many task-specific algorithms.
Edit intent: meaning-changed
Original:  It obtains new state-of-the-art results on both (1)  protein-level and (2) amino-acid-level tasks, outperforming many task-specific algorithms.
Revised:  It obtains new state-of-the-art results on both (1) protein-level and (2) amino-acid-level classification tasks. Furthermore, we present results from our ablation studies and qualitative interpretation analyses to better understand the strengths of PLUS-RNN. Availability: The codes and pre-trained models are available at URL
Edit intent: meaning-changed
Original:  In this work, we mainly use PLUS to pre-train a recurrent neural network (RNN) and refer to the resulting model as PLUS-RNN. It advances the state-of-the-art pre-training methods on six out of seven tasks, i.e., (1) three protein(-pair)-level classification, (2) two protein-level regression, and (3) two amino-acid-level classification tasks.
Revised:  In this work, we mainly use PLUS to pre-train a recurrent neural network (RNN) and refer to the resulting model as PLUS-RNN. It advances state-of-the-art pre-training methods on six out of seven tasks, i.e., (1) three protein(-pair)-level classification, (2) two protein-level regression, and (3) two amino-acid-level classification tasks.
Edit intent: fluency
Original: We consider the problem of optimizing a linear function over a lattice polytope P contained in [0,k]^n and defined via m linear inequalities.
Revised: The goal of this paper is to design a simplex algorithm for linear programs on lattice polytopes that traces `short' simplex paths from any given vertex to an optimal one. We consider a lattice polytope P contained in [0,k]^n and defined via m linear inequalities.
Edit intent: meaning-changed
Original:  The length of this path is independent on m and is the best possible up to a polynomial function , since it is only polynomially far from the worst case diameter. The\pm  number of arithmetic operations needed to compute the next vertex in the path is polynomial in n, m and log k.
Revised:  The length of this path is independent on m and is the best possible up to a polynomial function , since it is only polynomially far from the worst-case diameter, which roughly grows as a linear function in n and k. Motivated by the fact that most known lattice polytopes are defined via 0,\pm number of arithmetic operations needed to compute the next vertex in the path is polynomial in n, m and log k.
Edit intent: meaning-changed
Original:  The\pm  number of arithmetic operations needed to compute the next vertex in the path is polynomial in n, m and log k.
Revised:  The\pm 1 constraint matrices, our second contribution is an iterative algorithm which exploits the largest absolute value \alpha of the entries in the constraint matrix. We show that the length of the simplex path generated by the iterative algorithm is in O(n^2k \log(nk\alpha)). In particular, if \alpha is bounded by a polynomial in n, k, then the length of the simplex path is in O(n^2k \log(nk)). For both algorithms, the number of arithmetic operations needed to compute the next vertex in the path is polynomial in n, m and log k.
Edit intent: meaning-changed
Original: Language is central to human intelligence. We review recent breakthroughs in machine language processing and consider what remains to be achieved. Recent approaches rely on domain general principles of learning and representationcaptured in artificial neural networks.
Revised: Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. The human ability to understand and communicate about situations emerges gradually from experience and depends on domain-general principles of biological neural networks: connection-based learning, distributed representation, and context-sensitive, mutual constraint satisfaction-based processing. Current artificial language processing systems rely on the same domain general principles, embodied in artificial neural networks.
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Original:  In humans, language is part of a larger system for acquiring, representing, and communicating about objects and situationsin the physical and social world, and future machine language models should emulate such a system .
Revised:  In humans, language is part of a larger system for acquiring, representing, and communicating about objects and situationsin the physical and social world, and future machine language models should emulate such a systemquery-based attention .
Edit intent: meaning-changed
Original:  In humans, language is part of a larger system for acquiring, representing, and communicating about objects and situationsin the physical and social world, and future machine language models should emulate such a system .
Revised:  In humans, language is part of a larger system for acquiring, representing, and communicating about objects and situationsin the physical and social world, and future machine language models should emulate such a system, which extends the ability of these systems to exploit context and has contributed to remarkable breakthroughs. Nevertheless, most current models focus exclusively on language-internal tasks, limiting their ability to perform tasks that depend on understanding situations. These systems also lack memory for the contents of prior situations outside of a fixed contextual span .
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Original:  We describe existing machine models linking language to concrete situations, and point toward extensions to address more abstract cases. Human language processing exploits complementary learning systems, including a deep neural network-like learning system that learns gradually as machine systems do, as well as a fast-learning system that supports learning new information quickly.
Revised:  We describe URLanization of the brain's distributed understanding system, which includes a fast learning system that learns gradually as machine systems do, as well as a fast-learning system that supports learning new information quickly.
Edit intent: clarity
Original:  Human language processing exploits complementary learning systems, including a deep neural network-like learning system that learns gradually as machine systems do, as well as a fast-learning system that supports learning new information quickly. Adding such a system to machine language models will be an important further step toward truly human-like language understanding .
Revised:  Human language processing exploits complementary learning systems, including a deep neural network-like learning system that addresses the memory problem. We sketch a framework for future models of understanding drawing equally on cognitive neuroscience and artificial intelligence and exploiting query-based attention. We highlight relevant current directions and consider further developments needed to fully capture human-level language understanding in a computational system .
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Original: We present a novel system for singing synthesis , based on attention. Starting from a musical score with notes and lyrics, we build a phoneme-level multi stream note embedding. The embedding contains the information encoded in the score regarding pitch, duration and the phonemes to be pronounced on each note. This note representation is used to condition an attention-based sequence-to-sequence architecture, in order to generate mel-spectrograms.
Revised: We present UTACO, a singing synthesis model based on an attention-based sequence-to-sequence architecture, in order to generate mel-spectrograms.
Edit intent: clarity
Original:  This note representation is used to condition an attention-based sequence-to-sequence architecture, in order to generate mel-spectrograms. Our model demonstrates attention can be successfully applied to the singing synthesis field  .
Revised:  This note representation is used to condition an attention-based sequence-to-sequence mechanism and a vocoder based on dilated causal convolutions. These two classes of models have significantly affected the field of text-to-speech, but have never been thoroughly applied to the task of singing synthesis. UTACO demonstrates that attention can be successfully applied to the singing synthesis field .
Edit intent: meaning-changed
Original:   However, we observe that  completely dispensing with any duration modelling introduces occasional instabilities in the generated spectrograms.
Revised:  Despite this, it shows a strong improvement in naturalness with respect to previous neural singing synthesis models. The model does not require any durations or pitch patterns as inputs, and learns to insert vibrato autonomously according to the musical context. However, we observe that completely dispensing with any duration modelling introduces occasional instabilities in the generated spectrograms.
Edit intent: meaning-changed
Original:   However, we observe that  completely dispensing with any duration modelling introduces occasional instabilities in the generated spectrograms. We train an autoregressive WaveNet to be used as a neural vocoder to synthesise the mel-spectrograms produced by the sequence-to-sequence architecture, using a combination of speech and singing data .
Revised:  However, we observe that completely dispensing with any explicit duration modelling it becomes harder to obtain the fine control of timing needed to exactly match the tempo of a song .
Edit intent: clarity
Original:  However, the lexical semantics captured by these embeddings are difficult to interpret due to the dense vector representations. In order to improve the interpretability of word vectors, we explore definition modeling task and propose a novel framework (Semantics-Generator) to generate more reasonable and understandable context-dependent definitions.
Revised:  However, the lexical semantics captured by these embeddings are difficult to be interpreted. Definition modeling provides a more intuitive way to evaluate embeddings by utilizing them to generate natural language definitions of corresponding words. This task is of great significance for practical application and in-depth understanding of word representations. We propose a novel framework (Semantics-Generator) to generate more reasonable and understandable context-dependent definitions.
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Original:  Two multi-task learning methods are used to combine usage modeling and definition modeling . To verify our approach, we construct Oxford-2019 dataset, where each entry contains word, context, example sentence and corresponding definition.
Revised:  Two multi-task learning methods are used to combine usage modeling and definition modeling together. Experimental results on existing Oxford dataset and a new collected Oxford-2019 dataset, where each entry contains word, context, example sentence and corresponding definition.
Edit intent: meaning-changed
Original:  To verify our approach, we construct Oxford-2019 dataset, where each entry contains word, context, example sentence and corresponding definition. Experimental results show that Semantics-Generator achieves the state-of-the-art result in definition modeling and the multi-task learning methods are helpful for two tasks to improve the performance.
Revised:  To verify our approach, we construct Oxford-2019 dataset show that our single-task model achieves the state-of-the-art result in definition modeling and the multi-task learning methods are helpful for two tasks to improve the performance.
Edit intent: meaning-changed
Original:  This study focused on predicting and optimizing inhibitors of NaV1.7 using machine learning methods , and using patch-clamp methods to validate them at the cellular level. Materials and Methods We used Chembl, BindingDB, and data from the literature to establish classification models for inhibitors . The imbalanced data set test2 was used to screen the best-performing model to screen commercial compound libraries, and whole-cell voltage-clamp was used to validate inhibitors. We propose a molecular group optimization method using a combination of Grammer Variational Autoencoder, classification model, and simulated annealing algorithm. Results and Conclusion We get the model RF-CDK that performs best in the imbalanced data set.
Revised:  This study focused on predicting and optimizing inhibitors of NaV1.7 using machine learning methods to predict NaV1.7 inhibitors and found the model RF-CDK that performs best in the imbalanced data set.
Edit intent: clarity
Original:  Results and Conclusion We get the model RF-CDK that performs best in the imbalanced data set. Of the three compounds that may have inhibitory effects, Nortriptyline has been experimentally verified. In the molecular optimization method , the best result of the optimization results of CHEMBL2325245 is MS = 1.052, PROB = 0.527, SA = 2.587, QED = 0.462. 40 molecules located in the applicability domain of RF-CDK were used for optimization, among which 34 molecules gave larger MS values .
Revised:  Results and Conclusion We get the model RF-CDK that performed best on the imbalanced dataset. Using the RF-CDK were used for optimization, among which 34 molecules gave larger MS values .
Edit intent: clarity
Original:  In the molecular optimization method , the best result of the optimization results of CHEMBL2325245 is MS = 1.052, PROB = 0.527, SA = 2.587, QED = 0.462. 40 molecules located in the applicability domain of RF-CDK were used for optimization, among which 34 molecules gave larger MS values .
Revised:  In the molecular optimization method , the best result of the optimization results of CHEMBL2325245 is MS = 1.052, PROB = 0.527, SA = 2.587, QED = 0.462. 40 molecules located in the applicability domain of RF-CDK model for screening drugs, we got effective compounds K1. We use the cell patch clamp method to verify K1. However, because the model evaluation method in this article is not comprehensive enough, there is still a lot of research work to be performed, such as comparison with other existing methods. The target protein has multiple active sites and requires our further research. We need more detailed models to consider this biological process and compare it with the current results, which is an error in this article. So we want to withdraw this article .
Edit intent: meaning-changed
Original:  The prediction of workers' safety behaviour will enable the prior insights into each worker's behavioural tendency and will be useful in the design of management practices prior to the occurrence of accidents and contribute to the reduction of injury rates.
Revised:  The prediction of workers' safety behaviour can help identify vulnerable workers who intend to undertake unsafe behaviours and be useful in the design of management practices prior to the occurrence of accidents and contribute to the reduction of injury rates.
Edit intent: clarity
Original:  The prediction of workers' safety behaviour will enable the prior insights into each worker's behavioural tendency and will be useful in the design of management practices prior to the occurrence of accidents and contribute to the reduction of injury rates.
Revised:  The prediction of workers' safety behaviour will enable the prior insights into each worker's behavioural tendency and will be useful in the design of management practices to minimise the occurrence of accidents and contribute to the reduction of injury rates.
Edit intent: clarity
Original:  The prediction of workers' safety behaviour will enable the prior insights into each worker's behavioural tendency and will be useful in the design of management practices prior to the occurrence of accidents and contribute to the reduction of injury rates. In recent years, researchers have found that people do have stable predispositions to engage in certain safety behavioural patterns which vary among individuals as a function of personality features .
Revised:  The prediction of workers' safety behaviour will enable the prior insights into each worker's behavioural tendency and will be useful in the design of management practices prior to the occurrence of accidents . The latest literature has evidenced that there is within-population diversity that leads people's intended safety behaviours in the workplace, which are found to vary among individuals as a function of personality features .
Edit intent: coherence
Original:  The data-driven nature of machine learning technique enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship between construction workers' personality traits and safety behaviour.
Revised:  The data-driven nature of neural network enabled a reliable estimate of the personality-safety behaviour relationship, which allowed this study to provide novel insight that nonlinearity may exist in the relationship between construction workers' personality traits and safety behaviour.
Edit intent: clarity