wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Hilbert complex and polynomial complexes are presented and several decomposition of polynomial vector and tensors spaces are revealed from the complexes . The constructed divdiv conforming elements are exploited to discretize the mixed formulation of the biharmonic equation. Optimal order and superconvergence error analysis is provided. Hybridization is given for the ease of implementation .
Revised:  Hilbert complex and polynomial complexes are presented and several decomposition of polynomial vector and tensors spaces are revealed from the complexes .
Edit intent: coherence
Original:   Besides the normal-normal component, another trace involving combination of first order derivatives of the tensor should be continuous across the face.
Revised:  The key tools of the construction are the decomposition of polynomial tensor spaces and the characterization of the trace operators. First, the divdiv Hilbert complex and its corresponding polynomial complexes are presented. Several decompositions of polynomial vector and tensors spaces are derived from the polynomial complexes. Then, traces for div-div operator are characterized through a Green's identity. Besides the normal-normal component, another trace involving combination of first order derivatives of the tensor should be continuous across the face.
Edit intent: meaning-changed
Original:  Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best macro F1-score with 0.498 on development data and 0.739 on test data.
Revised:  Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best weighted F1-score with 0.498 on development data and 0.739 on test data.
Edit intent: meaning-changed
Original:  Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best macro F1-score with 0.498 on development data and 0.739 on test data.
Revised:  Although two-step fine-tuning improves sentiment classification performance over the base model, the large multilingual XLM-RoBERTa model achieves best macro F1-score with 0.537 on development data and 0.739 on test data.
Edit intent: meaning-changed
Original:  We find that the percolation threshold vanishes in the limit of large networks for \gamma<3, while it is finite for \gamma>\le 3,  although its value for \gamma between 3 and 4 is exceedingly small and preasymptotic effects are huge.
Revised:  We find that the percolation threshold vanishes in the limit of large networks for \gamma\le 3, although its value for \gamma between 3 and 4 is exceedingly small and preasymptotic effects are huge.
Edit intent: clarity
Original:  We find that the percolation threshold vanishes in the limit of large networks for \gamma<3, while it is finite for \gamma>\le 3,  although its value for \gamma between 3 and 4 is exceedingly small and preasymptotic effects are huge.
Revised:  We find that the percolation threshold vanishes in the limit of large networks for \gamma<3, while it is finite for \gamma>\le 3, while it is finite for \gamma>3, although its value for \gamma between 3 and 4 is exceedingly small and preasymptotic effects are huge.
Edit intent: meaning-changed
Original:  We also derive the critical properties of the DOP transition, in particular how the exponents depend on the heterogeneity of the network, determining that DOP does not belong to the universality class of random percolation for \gamma < \le 3.
Revised:  We also derive the critical properties of the DOP transition, in particular how the exponents depend on the heterogeneity of the network, determining that DOP does not belong to the universality class of random percolation for \gamma \le 3.
Edit intent: clarity
Original:  As part of the Information Extraction task of the Cheminformatics Elseiver Melbourne University challenge, in this work we study the effectiveness of contextualized language models to extract reaction information in chemical patents.
Revised:  As part of the Information Extraction task of the Cheminformatics Elsevier Melbourne University challenge, in this work we study the effectiveness of contextualized language models to extract reaction information in chemical patents.
Edit intent: fluency
Original:  As reaching the optimal capacity via non-local learning rules like back-propagation requires slow and neurally implausible training procedures, our results indicate that one-shot URLanized Hebbian learning can be just as efficient.
Revised:  As reaching the optimal capacity via non-local learning rules like backpropagation requires slow and neurally implausible training procedures, our results indicate that one-shot URLanized Hebbian learning can be just as efficient.
Edit intent: fluency
Original:  we implement and share a toolkit that provides an extensible and unified API for evaluating summarization models across a broad range of automatic metrics, 5) we assemble and share the largest and most diverse, in terms of model types, collection of human judgments of model-generated summaries on the CNN/Daily Mail dataset annotated by both expert judges and crowd source workers.
Revised:  we implement and share a toolkit that provides an extensible and unified API for evaluating summarization models across a broad range of automatic metrics, 5) we assemble and share the largest and most diverse, in terms of model types, collection of human judgments of model-generated summaries on the CNN/Daily Mail dataset annotated by both expert judges and crowd-source workers.
Edit intent: fluency
Original: Smart FinTechhas emerged as a new area that synthesizes and transforms AI and finance, and broadly data science , machine learning, economics, etc . Smart FinTech also transforms and drives new economic and financial businesses, services and systems , and plays an increasingly important role in economy, technology and society transformation.
Revised: Financial technology (FinTech) has been playing an increasingly critical role in driving modern economies, society, technology, and many other areas. Smart FinTech is the new-generation FinTech, largely inspired and empowered by data science and broadly data science , machine learning, economics, etc . Smart FinTech also transforms and drives new economic and financial businesses, services and systems , and plays an increasingly important role in economy, technology and society transformation.
Edit intent: meaning-changed
Original: Smart FinTechhas emerged as a new area that synthesizes and transforms AI and finance, and broadly data science , machine learning, economics, etc . Smart FinTech also transforms and drives new economic and financial businesses, services and systems , and plays an increasingly important role in economy, technology and society transformation.
Revised: Smart FinTechhas emerged as a new area that synthesizes and transforms AI and finance, and new-generation AI and (DSAI) techniques . Smart FinTech also transforms and drives new economic and financial businesses, services and systems , and plays an increasingly important role in economy, technology and society transformation.
Edit intent: meaning-changed
Original: Smart FinTechhas emerged as a new area that synthesizes and transforms AI and finance, and broadly data science , machine learning, economics, etc . Smart FinTech also transforms and drives new economic and financial businesses, services and systems , and plays an increasingly important role in economy, technology and society transformation.
Revised: Smart FinTechhas emerged as a new area that synthesizes and transforms AI and finance, and broadly data science , machine learning, economics, etc . Smart FinTech synthesizes broad DSAI and transforms finance and economies to drive intelligent, automated, whole-of-business and personalized economic and financial businesses, services and systems , and plays an increasingly important role in economy, technology and society transformation.
Edit intent: meaning-changed
Original: Smart FinTechhas emerged as a new area that synthesizes and transforms AI and finance, and broadly data science , machine learning, economics, etc . Smart FinTech also transforms and drives new economic and financial businesses, services and systems , and plays an increasingly important role in economy, technology and society transformation. This article presents a highly summarized research overview of smart FinTech, including FinTech businesses and challenges, various FinTech-associated data and repositories, FinTech-driven business decision and optimization, areas in smart FinTech, and research methods and techniques for smart FinTech .
Revised: Smart FinTechhas emerged as a new area that synthesizes and transforms AI and finance, and broadly data science , machine learning, economics, etc . Smart FinTech also transforms and drives new economic and financial businesses, services and systems . The research on data science and AI in FinTech involves many latest progress made in smart FinTech for BankingTech, TradeTech, LendTech, InsurTech, WealthTech, PayTech, RiskTech, cryptocurrencies, and blockchain, and the DSAI techniques including complex system methods, quantitative methods, intelligent interactions, recognition and responses, data analytics, deep learning, federated learning, privacy-preserving processing, augmentation, optimization, and system intelligence enhancement. Here, we present a highly dense research overview of smart FinTech, including FinTech businesses and challenges, various FinTech-associated data and repositories, FinTech-driven business decision and optimization, areas in smart FinTech, and research methods and techniques for smart FinTech .
Edit intent: meaning-changed
Original:  This article presents a highly summarized research overview of smart FinTech, including FinTech businesses and challenges, various FinTech-associated data and repositories, FinTech-driven business decision and optimization, areas in smart FinTech, and research methods and techniques for smart FinTech .
Revised:  This article presents a highly summarized research overview of smart financial businesses and their challenges, the smart FinTech ecosystem, the DSAI techniques to enable smart FinTech, and research methods and techniques for smart FinTech .
Edit intent: meaning-changed
Original:  This article presents a highly summarized research overview of smart FinTech, including FinTech businesses and challenges, various FinTech-associated data and repositories, FinTech-driven business decision and optimization, areas in smart FinTech, and research methods and techniques for smart FinTech .
Revised:  This article presents a highly summarized research overview of smart FinTech, including FinTech businesses and challenges, various FinTech-associated data and repositories, FinTech-driven business decision and optimization, areas in smart FinTech, and some research directions of smart FinTech futures to the DSAI communities .
Edit intent: meaning-changed
Original:  Here we propose a new method for detecting dynamic states in temporal networks using connection series (i.e., time series of connection status) between nodes. Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, and community detection in the similarity network of those time windows . Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states effectively .
Revised:  Here we propose a new method for detecting dynamic states in temporal networks using information regarding the timeline of contacts between each pair of nodes. We apply a similarity measure informed by the techniques of processing time series and community detection in the similarity network of those time windows . Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states effectively .
Edit intent: clarity
Original:  Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, and community detection in the similarity network of those time windows . Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states effectively .
Revised:  Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, and community detection to sequentially discompose a given temporal network into multiple dynamic states (including repeated ones) . Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states effectively .
Edit intent: meaning-changed
Original:  Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, and community detection in the similarity network of those time windows . Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states effectively .
Revised:  Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, and community detection in the similarity network of those time windows . Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states .
Edit intent: clarity
Original: In real-time, Twitter strongly imprints world events, popular culture, and the day-to-day ; Twitter records an ever growing compendium of language use and change; and Twitter has been shown to enable certain kinds of prediction . Vitally, and absent from many standard corpora such as books and news archives, Twitter also encodes popularityand spreading through retweets .
Revised: In real-time, Twitter strongly imprints world events, popular culture, and the day-to-day conversations by millions of ordinary people at a scale that is scarcely conventionalized and recorded . Vitally, and absent from many standard corpora such as books and news archives, Twitter also encodes popularityand spreading through retweets .
Edit intent: clarity
Original:  and Twitter has been shown to enable certain kinds of prediction . Vitally, and absent from many standard corpora such as books and news archives, Twitter also encodes popularityand spreading through retweets .
Revised:  and Twitter has been shown to enable certain kinds of prediction . Vitally, and absent from many standard corpora such as books and news archives, sharing and commenting mechanisms are native to social media platforms, enabling us to quantify social amplification (i.e., popularity) of trending storylines and contemporary cultural phenomena .
Edit intent: meaning-changed
Original:  Here, we describe Storywrangler,  an ongoing, day-scale curation of over 100 billion tweets containing around 1 trillion 1-grams from 2008 to 2020.
Revised:  Here, we describe Storywrangler, a natural language processing instrument designed to carry out an ongoing, day-scale curation of over 100 billion tweets containing around 1 trillion 1-grams from 2008 to 2020.
Edit intent: meaning-changed
Original:  Here, we describe Storywrangler,  an ongoing, day-scale curation of over 100 billion tweets containing around 1 trillion 1-grams from 2008 to 2020.
Revised:  Here, we describe Storywrangler, an ongoing, day-scale curation of over 100 billion tweets containing roughly 1 trillion 1-grams from 2008 to 2020.
Edit intent: clarity
Original:  Here, we describe Storywrangler,  an ongoing, day-scale curation of over 100 billion tweets containing around 1 trillion 1-grams from 2008 to 2020.
Revised:  Here, we describe Storywrangler, an ongoing, day-scale curation of over 100 billion tweets containing around 1 trillion 1-grams from 2008 to 2021.
Edit intent: meaning-changed
Original:  For each day, we break tweets into 1-, 2-, and 3-grams across 150+ languages, record usage frequencies, and generate Zipf distributions  .
Revised:  For each day, we break tweets into 1-, 2-, and 3-grams across 150+ languages, record usage frequencies, and generate Zipf distributions , for words, hashtags, handles, numerals, symbols, and emojis .
Edit intent: meaning-changed
Original:   We showcase a few examples of the many possible avenues of study we aim to enable including how social amplification can be visualized through 'contagiograms'  .
Revised:  Although Storywrangler leverages Twitter data, our method of extracting and tracking dynamic changes of n-grams can be extended to any similar social media platform. We showcase a few examples of the many possible avenues of study we aim to enable including how social amplification can be visualized through 'contagiograms' .
Edit intent: meaning-changed
Original:   We showcase a few examples of the many possible avenues of study we aim to enable including how social amplification can be visualized through 'contagiograms'  .
Revised:  We showcase a few examples of the many possible avenues of study we aim to enable including how social amplification can be visualized through 'contagiograms' . We also present some example case studies that bridge n-gram time series with disparate data sources to explore sociotechnical dynamics of famous individuals, box office success, and social unrest .
Edit intent: meaning-changed
Original:  This work is the first study to empirically test Strong's model at scale . It does so by studying the use of language on 39M social media posts in US about the COVID-19 pandemic , which is the first pandemic to spread this quickly not only on a global scale but also online.
Revised:  This work is the first study to empirically test Strong's model at scale by studying the use of language on 39M social media posts in US about the COVID-19 pandemic , which is the first pandemic to spread this quickly not only on a global scale but also online.
Edit intent: clarity
Original:  It does so by studying the use of language on 39M social media posts in US about the COVID-19 pandemic , which is the first pandemic to spread this quickly not only on a global scale but also online.
Revised:  It does so by studying the use of language of 122M tweets related to the COVID-19 pandemic , which is the first pandemic to spread this quickly not only on a global scale but also online.
Edit intent: meaning-changed
Original:  It does so by studying the use of language on 39M social media posts in US about the COVID-19 pandemic , which is the first pandemic to spread this quickly not only on a global scale but also online.We identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries ;
Revised:  It does so by studying the use of language on 39M social media posts in US about the COVID-19 pandemic posted in the U.S. during the whole year of 2020. On Twitter, we identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries ;
Edit intent: meaning-changed
Original: We identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries ;
Revised: We identified three distinct phases . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries ;
Edit intent: clarity
Original: We identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries ;
Revised: We identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three psycho-social epidemics. In the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries ;
Edit intent: clarity
Original: We identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries ;
Revised: We identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, users refused to accept reality despite the increasing numbers of deaths in other countries ;
Edit intent: clarity
Original: We identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries ;
Revised: We identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing number of deaths in other countries ;
Edit intent: fluency
Original: We identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries ; in the suspended reality phase (started after the announcement of the first death in the country), people' s fear translated into anger about the looming feeling that things were about to change ;
Revised: We identified three distinct phases , which parallel Kuebler-Ross's stages of grief . Each of them is characterized by different regimes of the three social epidemics: in the refusal phase, people refused to accept reality despite the increasing numbers of deaths in other countries . In the anger phase (started after the announcement of the first death in the country), people' s fear translated into anger about the looming feeling that things were about to change ;
Edit intent: clarity
Original:  in the suspended reality phase (started after the announcement of the first death in the country), people' s fear translated into anger about the looming feeling that things were about to change ; finally , in the acceptance phase (started after the authorities imposed physical-distancing measures ), people found a "new normal" for their daily activities.
Revised:  in the suspended reality phase (started after the announcement of the first death in the country), people' s fear translated into anger about the looming feeling that things were about to change . Finally , in the acceptance phase (started after the authorities imposed physical-distancing measures ), people found a "new normal" for their daily activities.
Edit intent: fluency