wanyu/IteraTeR_full_sent huggingface dataset:
Original:  ANNs have subsequently become a popular model class to infer computational principles underlying complex cognitive functions, and in turn  they have also emerged as a natural testbed for applying methods originally developed to probe information in neural populations.
Revised:  DNNs have subsequently become a popular model class to infer computational principles underlying complex cognitive functions, and in turn they have also emerged as a natural testbed for applying methods originally developed to probe information in neural populations.
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Original:  ANNs have subsequently become a popular model class to infer computational principles underlying complex cognitive functions, and in turn  they have also emerged as a natural testbed for applying methods originally developed to probe information in neural populations.
Revised:  ANNs have subsequently become a popular model class to infer computational principles underlying complex cognitive functions, and in turn , they have also emerged as a natural testbed for applying methods originally developed to probe information in neural populations.
Edit intent: fluency
Original:  In this work, we utilize mean-field theoretic manifold analysis, a recent technique from computational neuroscience  , to analyze the high dimensional geometry of language representations from large-scale contextual embedding models.
Revised:  In this work, we utilize mean-field theoretic manifold analysis, a recent technique from computational neuroscience that connects geometry of feature representations with linear separability of classes , to analyze the high dimensional geometry of language representations from large-scale contextual embedding models.
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Original:  We explore representations from different model families (BERT, RoBERTa, GPT-2 , etc.) and find evidence for emergence of linguistic manifold across layer depth (e.g., manifolds for part-of-speech and combinatory categorical grammar tags).
Revised:  We explore representations from different model families (BERT, RoBERTa, GPT , etc.) and find evidence for emergence of linguistic manifold across layer depth (e.g., manifolds for part-of-speech and combinatory categorical grammar tags).
Edit intent: meaning-changed
Original:  We explore representations from different model families (BERT, RoBERTa, GPT-2 , etc.) and find evidence for emergence of linguistic manifold across layer depth (e.g., manifolds for part-of-speech and combinatory categorical grammar tags).
Revised:  We explore representations from different model families (BERT, RoBERTa, GPT-2 , etc.) and find evidence for emergence of linguistic manifolds across layer depth (e.g., manifolds for part-of-speech and combinatory categorical grammar tags).
Edit intent: fluency
Original:  We explore representations from different model families (BERT, RoBERTa, GPT-2 , etc.) and find evidence for emergence of linguistic manifold across layer depth (e.g., manifolds for part-of-speech and combinatory categorical grammar tags). We further observe that different encoding schemes used to obtain the representations lead to differences in whether these linguistic manifolds emerge in earlier or later layers of the network .
Revised:  We explore representations from different model families (BERT, RoBERTa, GPT-2 , etc.) and find evidence for emergence of linguistic manifold across layer depth (e.g., manifolds for part-of-speech tags), especially in ambiguous data (i.e, words with multiple part-of-speech tags, or part-of-speech classes including many words) .
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Original: Social media provides an unfiltered stream of user-generated input, leading to creative languageuse and many interesting linguistic phenomena, which were previously not available so abundantly. However, this language is harder to process automatically. One particularly challenging phenomenon is the use of multiple languages within one utterance, also called Code-Switching (CS) . Whereas monolingual social media data already provides many problems for natural language processing, CS adds another challenging dimension.
Revised: Lexical normalization, the translation of non-canonical data to standard language, has shown to improve the performance of manynatural language processing tasks on social media. Yet, using multiple languages in one utterance, also called Code-Switching (CS) . Whereas monolingual social media data already provides many problems for natural language processing, CS adds another challenging dimension.
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Original:  One particularly challenging phenomenon is the use of multiple languages within one utterance, also called Code-Switching (CS) . Whereas monolingual social media data already provides many problems for natural language processing, CS adds another challenging dimension.
Revised:  One particularly challenging phenomenon is the use of multiple languages within one utterance, also called code-switching (CS) . Whereas monolingual social media data already provides many problems for natural language processing, CS adds another challenging dimension.
Edit intent: fluency
Original:  One particularly challenging phenomenon is the use of multiple languages within one utterance, also called Code-Switching (CS) . Whereas monolingual social media data already provides many problems for natural language processing, CS adds another challenging dimension. One solution that is commonly used to improve processing of social mediadata is to translate input texts to standard language first. This normalization has shown to improve performance of many natural language processing tasks.
Revised:  One particularly challenging phenomenon is the use of multiple languages within one utterance, also called Code-Switching (CS) , is frequently overlooked by these normalization systems, despite its common use in social media.
Edit intent: coherence
Original:  In this paper, we focus on normalization in the context of code-switching. We introduce a variety of models to perform normalization on CS data , and analyse the impact of word-level language identification on normalization . We show that the performance of the proposed normalization models is generally high, but language labels are only slightly informative. We also carry out POS taggingas extrinsic evaluation and show that automatic normalization of the input leads to 3.2\% absolute performance increase , whereas gold normalization leads to an increase of 6.8\% .
Revised:  In this paper, we propose three normalization models specifically designed to handle code-switched data which we evaluate for two language pairs: Indonesian-English (Id-En) and Turkish-German (Tr-De). For the latter, we introduce novel normalization layers and their corresponding language ID and POS tags for the dataset, and evaluate the downstream effect of normalization on POS tagging. Results show that our CS-tailored normalization models outperform Id-En state of the art and Tr-De monolingual models, and lead to 5.4\% relative performance increase for POS tagging as compared to unnormalized input .
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Original: We suggest an approach to creation of general multi-commodity equilibrium models , where the current market state is supposed to be completely defined by the vector of the full transaction quantities of economic agents . These quantities must also satisfy the balance equation. The agents determine feasible transaction and price sets dependent of the current market state and can strike bargains changing market states .
Revised: We suggest an approach to creation of general market equilibrium models involving economic agents with local and partial knowledge about the system and under different restrictions .
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Original:  The market equilibrium problem is  formulated as a quasi-variational inequality . This approach is utilized for derivation of existence results and for suggesting dynamic market processesconverging to equilibrium points .
Revised:  The market equilibrium problem is formulated as a quasi-variational inequality that enables us to establish existence results for the model in different settings. We also describe dynamic processes, which fall into information exchange schemes of the proposed market model. In particular, we propose an iterative solution method for quasi-variational inequalities, which is based on evaluations of the proper market information only in a neighborhood of the current market state without knowledge of the whole feasible set and prove its convergence .
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Original:  For the second subtask, which is to select the reason why a statement does not make sense, we stand within the first six teams (93.7\%) among 27 participants with very competitive results  .
Revised:  For the second subtask, which is to select the reason why a statement does not make sense, we stand within the first six teams (93.7\%) among 27 participants with very competitive results . Our result for last subtask of generating reason against the nonsense statement shows many potentials for future researches as we applied the most powerful generative model of language (GPT-2) with 6.1732 BLEU score among first four teams .
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Original:  Currently, there is no effective vaccine or therapy approved for the treatment COVID-19, though many such therapeutics are being evaluated in clinical trials. Using the unique profiles of altered gene expression found in NHBE, A549_ACE2 and Calu3 human lung epithelial cells after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, we conducted a computational analysis to identify dysfunctional signaling pathways within infected cells.
Revised:  Currently, there is no effective vaccine or therapy for treatment thus far. After affected by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, we conducted a computational analysis to identify dysfunctional signaling pathways within infected cells.
Edit intent: clarity
Original:  Using the unique profiles of altered gene expression found in NHBE, A549_ACE2 and Calu3 human lung epithelial cells after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, we conducted a computational analysis to identify dysfunctional signaling pathways within infected cells.
Revised:  Using the unique profiles of altered gene expression found in NHBE, A549_ACE2 and Calu3 human lung epithelial cells after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) , molecular signaling of host cells plays critical roles during the life cycle of SARS-CoV-2. Thus, it is significant to identify the involved molecular signaling pathways within infected cells.
Edit intent: meaning-changed
Original:  Using the unique profiles of altered gene expression found in NHBE, A549_ACE2 and Calu3 human lung epithelial cells after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, we conducted a computational analysis to identify dysfunctional signaling pathways within infected cells. Using these data, potential therapeutic targets were identified and matched with existing medications to develop a theoretical approach to pharmacologic repurposing and inform clinical trial decisions. Specifically, signaling pathways upregulated by SARS-CoV-2 infection and their associated gene ontologies (GOs) were extracted and clustered to generate 10 super-GOs.
Revised:  Using the unique profiles of altered gene expression found in NHBE, A549_ACE2 and Calu3 human lung epithelial cells after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, we conducted a computational analysis to identify dysfunctional signaling pathways within the host cells, and drugs targeting these molecular signaling pathways could be potentially effective for COVID-19 treatment. In this study, we aimed to identify these potential molecular signaling pathways, and repurpose existing drugs as a potentially effective treatment of COVID-19 to facilitate the therapeutic discovery, based on the transcriptional response of host cells. We first identified dysfunctional signaling pathways associated with the infection caused SARS-CoV-2 infection and their associated gene ontologies (GOs) were extracted and clustered to generate 10 super-GOs.
Edit intent: coherence
Original:  Specifically, signaling pathways upregulated by SARS-CoV-2 infection and their associated gene ontologies (GOs) were extracted and clustered to generate 10 super-GOs.
Revised:  Specifically, signaling pathways upregulated by SARS-CoV-2 in human lung epithelial cells through analysis of the altered gene expression profiles. In addition to the signaling pathway analysis, the activated gene ontologies (GOs) were extracted and clustered to generate 10 super-GOs.
Edit intent: meaning-changed
Original:  Specifically, signaling pathways upregulated by SARS-CoV-2 infection and their associated gene ontologies (GOs) were extracted and clustered to generate 10 super-GOs. The genes associated with the identified signaling pathways and the super-GOs were used as gene signatures to identify drugs that can potentially inhibit these signaling cascades. In these results, interesting signaling pathways and GOs such as  JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were identified.
Revised:  Specifically, signaling pathways upregulated by SARS-CoV-2 infection and their associated gene ontologies (GOs) and super gene ontologies were identified. Signaling pathways and GOs such as JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were identified.
Edit intent: clarity
Original:  In these results, interesting signaling pathways and GOs such as  JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were identified.
Revised:  In these results, interesting signaling pathways and GOs such as MAPK, JNK, STAT, ERK, JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were identified.
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Original:  In these results, interesting signaling pathways and GOs such as  JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were identified. Many drugs were identified with downstream inhibition of these signaling pathways and GOs, and many of which are currently undergoing clinical trial evaluation.
Revised:  In these results, interesting signaling pathways and GOs such as JAK-STAT, IRF7-NFkB signaling, and MYD88/CXCR6 immune signaling were particularly identified. Based on the identified signaling pathways and GOs, and many of which are currently undergoing clinical trial evaluation.
Edit intent: meaning-changed
Original:  Many drugs were identified with downstream inhibition of these signaling pathways and GOs, and many of which are currently undergoing clinical trial evaluation. The analysis results can be potentially helpful to facilitate the future clinical trial design.
Revised:  Many drugs were identified with downstream inhibition of these signaling pathways and GOs, a set of potentially effective drugs were repurposed by integrating the drug-target and reverse gene expression data resources. The dexamethasone was top-ranked in the prediction, which was the first reported drug to be able to significantly reduce the death rate of COVID-19 patients receiving respiratory support. The results can be potentially helpful to facilitate the future clinical trial design.
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Original:  The analysis results can be potentially helpful to facilitate the future clinical trial design. Rigorous clinical trial and experimental validations are needed to validate these results, considering the limitation of pure computational data analysis based on the noisy and limited data. some of these drugs might have harmful effects and cautions .
Revised:  The analysis results can be helpful to understand the associated molecular signaling pathways within host cells, and facilitate the discovery of effective drugs for COVID-19 treatment .
Edit intent: clarity
Original: This paper describes and develops a fast and accurate algorithm that computes the field of values boundary curve for every conceivable complex square matrix A, i.e., for matrices with or without repeated eigenvalues and with or without Jordan block structures  .
Revised: This paper describes and develops a fast and accurate algorithm that computes the field of values boundary curve for every conceivable complex square matrix A, i.e., for all matrices, those with or without repeated eigenvalues and with or without Jordan block structures .
Edit intent: clarity
Original: This paper describes and develops a fast and accurate algorithm that computes the field of values boundary curve for every conceivable complex square matrix A, i.e., for matrices with or without repeated eigenvalues and with or without Jordan block structures  .
Revised: This paper describes and develops a fast and accurate algorithm that computes the field of values boundary curve for every conceivable complex square matrix A, i.e., for matrices with or without repeated eigenvalues and with or without Jordan block structures , with ill-conditioned eigenvalues or ill-conditioned ones and so forth .
Edit intent: meaning-changed
Original:  It relies on a matrix flow decomposition method that finds the coarsest block diagonal flow representation for the hermitean matrix flow H\!K  (t) = \cos(t) H + \sin(t) K  . Here H\!K  (t) is a 1-parameter varying linear combination of the real and skew part matrices H = (A+A^*)/2 and K = (A-A^*)/(2i) of A.
Revised:  It relies on a matrix flow decomposition method that finds a block-diagonal flow representation for the hermitean matrix flow H\!K (t) = \cos(t) H + \sin(t) K . Here H\!K (t) is a 1-parameter varying linear combination of the real and skew part matrices H = (A+A^*)/2 and K = (A-A^*)/(2i) of A.
Edit intent: clarity
Original:  It relies on a matrix flow decomposition method that finds the coarsest block diagonal flow representation for the hermitean matrix flow H\!K  (t) = \cos(t) H + \sin(t) K  . Here H\!K  (t) is a 1-parameter varying linear combination of the real and skew part matrices H = (A+A^*)/2 and K = (A-A^*)/(2i) of A.
Revised:  It relies on a matrix flow decomposition method that finds the coarsest block diagonal flow representation for the hermitean matrix flow H\!K _A (t) = \cos(t) H + \sin(t) K . Here H\!K (t) is a 1-parameter varying linear combination of the real and skew part matrices H = (A+A^*)/2 and K = (A-A^*)/(2i) of A.
Edit intent: meaning-changed
Original:  It relies on a matrix flow decomposition method that finds the coarsest block diagonal flow representation for the hermitean matrix flow H\!K  (t) = \cos(t) H + \sin(t) K  . Here H\!K  (t) is a 1-parameter varying linear combination of the real and skew part matrices H = (A+A^*)/2 and K = (A-A^*)/(2i) of A.
Revised:  It relies on a matrix flow decomposition method that finds the coarsest block diagonal flow representation for the hermitean matrix flow H\!K (t) = \cos(t) H + \sin(t) K if this is possible . Here H\!K (t) is a 1-parameter varying linear combination of the real and skew part matrices H = (A+A^*)/2 and K = (A-A^*)/(2i) of A.
Edit intent: meaning-changed
Original:  It relies on a matrix flow decomposition method that finds the coarsest block diagonal flow representation for the hermitean matrix flow H\!K  (t) = \cos(t) H + \sin(t) K  . Here H\!K  (t) is a 1-parameter varying linear combination of the real and skew part matrices H = (A+A^*)/2 and K = (A-A^*)/(2i) of A.
Revised:  It relies on a matrix flow decomposition method that finds the coarsest block diagonal flow representation for the hermitean matrix flow H\!K (t) = \cos(t) H + \sin(t) K . Here H\!K _A (t) is a 1-parameter varying linear combination of the real and skew part matrices H = (A+A^*)/2 and K = (A-A^*)/(2i) of A.
Edit intent: meaning-changed
Original:  For decomposing flows H\!K  (t), the algorithm decomposes the given dense matrix A conformally into diagonal blocks as H\!K in U^*AU = \text { diag} (A_j) for a unitary matrix U . It then computes the field of values boundaries separately for each diagonal block A_j using the fast ZNN parameter varying flow eigenvalue method.
Revised:  For decomposing flows H\!K _A (t), the algorithm decomposes the given dense matrix A conformally into diagonal blocks as H\!K in U^*AU = \text { diag} (A_j) for a unitary matrix U . It then computes the field of values boundaries separately for each diagonal block A_j using the fast ZNN parameter varying flow eigenvalue method.
Edit intent: meaning-changed
Original:  For decomposing flows H\!K  (t), the algorithm decomposes the given dense matrix A conformally into diagonal blocks as H\!K in U^*AU = \text { diag} (A_j) for a unitary matrix U . It then computes the field of values boundaries separately for each diagonal block A_j using the fast ZNN parameter varying flow eigenvalue method.
Revised:  For decomposing flows H\!K (t), the algorithm decomposes the given dense general matrix A unitarily into U^*AU = \text { diag} (A_j) for a unitary matrix U . It then computes the field of values boundaries separately for each diagonal block A_j using the fast ZNN parameter varying flow eigenvalue method.
Edit intent: clarity
Original:  For decomposing flows H\!K  (t), the algorithm decomposes the given dense matrix A conformally into diagonal blocks as H\!K in U^*AU = \text { diag} (A_j) for a unitary matrix U . It then computes the field of values boundaries separately for each diagonal block A_j using the fast ZNN parameter varying flow eigenvalue method.
Revised:  For decomposing flows H\!K (t), the algorithm decomposes the given dense matrix A conformally into diagonal blocks as H\!K in U^*AU = \text { diag} (A_j) with diagonal blocks A_j that conform in size with those of H\!K_A . It then computes the field of values boundaries separately for each diagonal block A_j using the fast ZNN parameter varying flow eigenvalue method.
Edit intent: meaning-changed
Original:  For decomposing flows H\!K  (t), the algorithm decomposes the given dense matrix A conformally into diagonal blocks as H\!K in U^*AU = \text { diag} (A_j) for a unitary matrix U . It then computes the field of values boundaries separately for each diagonal block A_j using the fast ZNN parameter varying flow eigenvalue method.
Revised:  For decomposing flows H\!K (t), the algorithm decomposes the given dense matrix A conformally into diagonal blocks as H\!K in U^*AU = \text { diag} (A_j) for a unitary matrix U . It then computes the field of values boundaries separately for each diagonal block A_j using the path following ZNN eigenvalue method.
Edit intent: clarity
Original:  The algorithm removes standard restrictions for path following FoV methods that generally cannot deal with decomposing matrices A due to possible eigencurve crossings for such A . Tests and numerical comparisons are included   .
Revised:  The algorithm removes standard restrictions for path following FoV methods that generally cannot deal with decomposing matrices A due to possible eigencurve crossings of H\!K_A(t) . Tests and numerical comparisons are included .
Edit intent: meaning-changed
Original:  The algorithm removes standard restrictions for path following FoV methods that generally cannot deal with decomposing matrices A due to possible eigencurve crossings for such A . Tests and numerical comparisons are included   .
Revised:  The algorithm removes standard restrictions for path following FoV methods that generally cannot deal with decomposing matrices A due to possible eigencurve crossings for such A . Tests and numerical comparisons are included . Our ZNN based method is coded for sequential and parallel computations and both versions run very accurately and very fast when compared with Johnson's global QR eigenvalue and Bedixon rectangle based method that computes complete eigenanalyses of H\!K_A(t_k) for every chosen t_k \in .
Edit intent: meaning-changed