wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Given a set S of n slopes and an integer k\geq 1, is it possible to draw a complete graph on k vertices in the plane using only slopes from S? Equivalently, does there exist a set K of k points in general position such that the slope of every segment between two points of K is in S? We also present a polynomial algorithm for this question when n\leq 2k-c, conditionally on a conjecture of R.E. Jamison.
Revised:  Given a set S of n slopes and an integer k\geq 1, is it possible to draw a complete graph on k vertices in the plane using only slopes from S? Equivalently, does there exist a set K of k points in general position such that the slope of every segment between two points of K is in S? We then present a polynomial algorithm for this question when n\leq 2k-c, conditionally on a conjecture of R.E. Jamison.
Edit intent: clarity
Original:  Given a set S of n slopes and an integer k\geq 1, is it possible to draw a complete graph on k vertices in the plane using only slopes from S? Equivalently, does there exist a set K of k points in general position such that the slope of every segment between two points of K is in S? We also present a polynomial algorithm for this question when n\leq 2k-c, conditionally on a conjecture of R.E. Jamison.
Revised:  Given a set S of n slopes and an integer k\geq 1, is it possible to draw a complete graph on k vertices in the plane using only slopes from S? Equivalently, does there exist a set K of k points in general position such that the slope of every segment between two points of K is in S? We also present a polynomial algorithm for this question when n\leq 2k-c, conditional on a conjecture of R.E. Jamison.
Edit intent: fluency
Original:  A positive answer would have important implications for the science of success and managerial practices, yet recent works have supported diametrically opposite answers. To resolve this tension, we address this question in a unique, large-scale dataset that combines individuals'  purchasing history with their social and mobility traits across an entire nation.
Revised:  Although most previous studies focused on the predictive power of "hub" individuals with many social contacts, which sources of customer behavioral data are needed to address this question remains unclear, mostly due to the scarcity of available datasets that simultaneously capture individuals' purchasing patterns and social interactions. Here, we address this question in a unique, large-scale dataset that combines individuals' purchasing history with their social and mobility traits across an entire nation.
Edit intent: meaning-changed
Original:  Endogenous agglomeration emerges when inter-regional trade and/or social interactions incur high transportation costs, and the uniform dispersion occurs when these costs become negligibly small (i.e., when distance dies).
Revised:  As transportation costs decline, the economy undergoes a progressive dispersion process. Mono-centric agglomeration emerges when inter-regional trade and/or social interactions incur high transportation costs, and the uniform dispersion occurs when these costs become negligibly small (i.e., when distance dies).
Edit intent: meaning-changed
Original:  Endogenous agglomeration emerges when inter-regional trade and/or social interactions incur high transportation costs, and the uniform dispersion occurs when these costs become negligibly small (i.e., when distance dies).
Revised:  Endogenous agglomeration emerges when inter-regional trade and/or production externalities incur high transportation costs, and the uniform dispersion occurs when these costs become negligibly small (i.e., when distance dies).
Edit intent: clarity
Original:  Endogenous agglomeration emerges when inter-regional trade and/or social interactions incur high transportation costs, and the uniform dispersion occurs when these costs become negligibly small (i.e., when distance dies).
Revised:  Endogenous agglomeration emerges when inter-regional trade and/or social interactions incur high transportation costs, while uniform dispersion occurs when these costs become negligibly small (i.e., when distance dies).
Edit intent: coherence
Original:  In many-region geography, the network structure of social proximity emerges as the determinant of the geographical distribution of workers when trade becomes freer. If social proximity is governed by geographical distance (as in ground transportation) , a mono-centric concentration emerges .
Revised:  In many-region geography, the network structure of social proximity emerges as the determinant of the geographical distribution of workers as economic integration increases. If production externalities are governed solely by geographical distance (as in ground transportation) , a mono-centric concentration emerges .
Edit intent: clarity
Original:  If social proximity is governed by geographical distance (as in ground transportation) , a mono-centric concentration emerges . If geographically distant pairs of regions are ``socially close'' (due to, e.g., passenger transportation modes with strong distance economy such as regional airlines), then geographically multi-centric spatial distribution can be sustainable.
Revised:  If social proximity is governed by geographical distance (as in ground transportation) , a mono-centric spatial distribution emerges in the form of suburbanization. However, if geographically distant pairs of regions are ``socially close'' (due to, e.g., passenger transportation modes with strong distance economy such as regional airlines), then geographically multi-centric spatial distribution can be sustainable.
Edit intent: meaning-changed
Original:  SEMs typically represent counts of individuals in discrete infection states using Markov jump processes ( MJP ), but are computationally challenging as imperfect surveillance, lack of subject-level information, and temporal coarseness of the data obscure the true epidemic.
Revised:  SEMs typically represent counts of individuals in discrete infection states using Markov jump processes ( MJPs ), but are computationally challenging as imperfect surveillance, lack of subject-level information, and temporal coarseness of the data obscure the true epidemic.
Edit intent: clarity
Original:  Analytic integration over the latent epidemic process is generally impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space.
Revised:  Analytic integration over the latent epidemic process is impossible, and integration via Markov chain Monte Carlo (MCMC) is cumbersome due to the dimensionality and discreteness of the latent state space.
Edit intent: coherence
Original:  A linear noise approximation (LNA) that replaces the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings . Existing LNA frameworks are inappropriate for incidence data and depend on simulation-based methods or an assumption that disease counts are normally distributed.
Revised:  A linear noise approximation (LNA) that approximates the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings . Existing LNA frameworks are inappropriate for incidence data and depend on simulation-based methods or an assumption that disease counts are normally distributed.
Edit intent: clarity
Original:  A linear noise approximation (LNA) that replaces the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings . Existing LNA frameworks are inappropriate for incidence data and depend on simulation-based methods or an assumption that disease counts are normally distributed.
Revised:  A linear noise approximation (LNA) that replaces the MJP transition density with a Gaussian density has been explored for analyzing prevalence data in large-population settings , but requires modification for analyzing incidence counts without assuming that the data are normally distributed.
Edit intent: clarity
Original:  We demonstrate how to reparameterize a SEM to properly analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation-based methods, computationally.
Revised:  We demonstrate how to reparameterize SEMs to appropriately analyze incidence data, and fold the LNA into a data augmentation MCMC framework that outperforms deterministic methods, statistically, and simulation-based methods, computationally.
Edit intent: clarity
Original:  We  apply our method to national-level surveillance counts from the 2013--2015 West Africa Ebola outbreak , modeling within-country transmission and importation of infections from neighboring countries .
Revised:  We evaluate our method in simulations that reflect Ebola, influenza, and SARS-CoV-2 dynamics, and apply our method to national-level surveillance counts from the 2013--2015 West Africa Ebola outbreak , modeling within-country transmission and importation of infections from neighboring countries .
Edit intent: meaning-changed
Original:  Our extensive experiments on 7  directions with varying data sizes demonstrate that our model achieves competitive, if not better, performance compared to the state of the art in non-autoregressive machine translation while significantly reducing decoding time on average. 
Revised:  Our extensive experiments on 7 translation directions with varying data sizes demonstrate that our model achieves competitive, if not better, performance compared to the state of the art in non-autoregressive machine translation while significantly reducing decoding time on average. 
Edit intent: clarity
Original:  Our extensive experiments on 7  directions with varying data sizes demonstrate that our model achieves competitive, if not better, performance compared to the state of the art in non-autoregressive machine translation while significantly reducing decoding time on average. 
Revised:  Our extensive experiments on 7 directions with varying data sizes demonstrate that our model achieves competitive, if not better, performance compared to the state of the art in non-autoregressive machine translation while significantly reducing decoding time on average. Our code is available at URL
Edit intent: meaning-changed
Original:  Using medical imaging as a point of departure, we conducted three iterations of design activities to formulate CheXplain---a system that enables physicians to explore and understand AI-enabled chest X-ray analysis: %DIFDELCMD < \one %%%  a paired survey between referring physicians and radiologists reveals whether, when, and what kinds of explanations are needed;
Revised:  Using medical imaging as a point of departure, we conducted three iterations of design activities to formulate CheXplain---a system that enables physicians to explore and understand AI-enabled chest X-ray analysis: %DIFDELCMD < \one %%% (1) a paired survey between referring physicians and radiologists reveals whether, when, and what kinds of explanations are needed;
Edit intent: meaning-changed
Original:  and %DIFDELCMD < \three %%%  a high-fidelity prototype evaluated by another six physicians provides detailed summative insights on how each feature enables the exploration and understanding of AI.
Revised:  and %DIFDELCMD < \three %%% (3) a high-fidelity prototype evaluated by another six physicians provides detailed summative insights on how each feature enables the exploration and understanding of AI.
Edit intent: meaning-changed
Original:  We measure the tradeoff between safety and liveness (the ability of active honest voters to maintain/change the status quo , respectively) in a variety of domains, and show that  Reality Enforcing voting is optimal .
Revised:  We measure the tradeoff between safety and liveness (the ability of active honest voters to maintain/change the status-quo , respectively) in a variety of domains, and show that Reality Enforcing voting is optimal .
Edit intent: fluency
Original:  We measure the tradeoff between safety and liveness (the ability of active honest voters to maintain/change the status quo , respectively) in a variety of domains, and show that  Reality Enforcing voting is optimal .
Revised:  We measure the tradeoff between safety and liveness (the ability of active honest voters to maintain/change the status quo , respectively) in a variety of domains, and show that the Reality Enforcing voting is optimal .
Edit intent: fluency
Original:  We measure the tradeoff between safety and liveness (the ability of active honest voters to maintain/change the status quo , respectively) in a variety of domains, and show that  Reality Enforcing voting is optimal .
Revised:  We measure the tradeoff between safety and liveness (the ability of active honest voters to maintain/change the status quo , respectively) in a variety of domains, and show that Reality Enforcing voting rule is optimal in this respect .
Edit intent: clarity
Original:  (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs . (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation  . Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
Revised:  (1) a novel CNN struc-ture called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs . (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation . Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
Edit intent: fluency
Original:  (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs . (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation  . Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
Revised:  (1) a novel CNN structure called CGS-CNN is proposed to capture both glyph information and interactive information between glyphs from neighboring characters . (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation . Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
Edit intent: clarity
Original:  (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs . (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation  . Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
Revised:  (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs . (2) we provide a method with sliding window and Slice-Attention to fuse the BERT representation and glyph representation . Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
Edit intent: clarity
Original:  (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs . (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation  . Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
Revised:  (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs . (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation for a character, which may capture potential interactive knowledge be-tween context and glyph . Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
Edit intent: meaning-changed
Original:  (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs . (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation  . Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
Revised:  (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information from both character graphs and their neighboring graphs . (2) we provide a method with sliding window and Slice-Attention to extract interactive information between BERT representation and glyph representation . Experiments are con-ducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-arts performance for Chinese NER.
Edit intent: fluency
Original:  The major in-novations of FGN include: (1) a novel CNN struc-ture called CGS-CNN is proposed to capture both glyph information and interactive information between glyphs from neighboring characters.
Revised:  The major innovations of FGN include: (1) a novel CNN struc-ture called CGS-CNN is proposed to capture both glyph information and interactive information between glyphs from neighboring characters.
Edit intent: fluency
Original:  The major in-novations of FGN include: (1) a novel CNN struc-ture called CGS-CNN is proposed to capture both glyph information and interactive information between glyphs from neighboring characters.
Revised:  The major in-novations of FGN include: (1) a novel CNN structure called CGS-CNN is proposed to capture both glyph information and interactive information between glyphs from neighboring characters.
Edit intent: fluency
Original:  (2) we provide a method with sliding window and Slice-Attention to fuse the BERT representation and glyph representation for a character, which may capture potential interactive knowledge be-tween context and glyph.
Revised:  (2) we provide a method with sliding window and Slice-Attention to fuse the BERT representation and glyph representation for a character, which may capture potential interactive knowledge between context and glyph.
Edit intent: fluency
Original: Widespread adoption of electronic health records (EHRs) has fueled development of clinical outcome models using machine learning . However, patient EHR data are complex, and how to optimally represent them is an open question. This complexity, along with often small training set sizes available to train these clinical outcome models, are two core challenges for training high quality models. In this paper, we demonstrate that learning generic representations from the data of all the patients in the EHR enables better performing prediction models for clinical outcomes, allowing for these challenges to be overcome. We adapt common representation learning techniques used in other domains and find that representations inspired by language models enable a 3.5\% mean improvement in AUROC on five clinical outcomes compared to standard baselines, with the average improvement rising to 19\% when only a small number of patients are available for training a prediction modelfor a given clinical outcome .
Revised: Widespread adoption of electronic health records (EHRs) has fueled development of clinical outcome models using machine learning to build prediction models for various clinical outcomes. This process is often constrained by having a relatively small number of patient records for training the model. We demonstrate that using patient representation schemes inspired from techniques in natural language processing can increase the accuracy of clinical prediction models by transferring information learned from the entire patient population to the task of training a specific model, where only a subset of the population is relevant. Such patient representation schemes enable a 3.5\% mean improvement in AUROC on five clinical outcomes compared to standard baselines, with the average improvement rising to 19\% when only a small number of patients are available for training a prediction modelfor a given clinical outcome .
Edit intent: clarity
Original:  We adapt common representation learning techniques used in other domains and find that representations inspired by language models enable a 3.5\% mean improvement in AUROC on five clinical outcomes compared to standard baselines, with the average improvement rising to 19\% when only a small number of patients are available for training a prediction modelfor a given clinical outcome .
Revised:  We adapt common representation learning techniques used in other domains and find that representations inspired by language models enable a 3.5\% mean improvement in AUROC on five prediction tasks compared to standard baselines, with the average improvement rising to 19\% when only a small number of patients are available for training a prediction modelfor a given clinical outcome .
Edit intent: clarity
Original:  We adapt common representation learning techniques used in other domains and find that representations inspired by language models enable a 3.5\% mean improvement in AUROC on five clinical outcomes compared to standard baselines, with the average improvement rising to 19\% when only a small number of patients are available for training a prediction modelfor a given clinical outcome .
Revised:  We adapt common representation learning techniques used in other domains and find that representations inspired by language models enable a 3.5\% mean improvement in AUROC on five clinical outcomes compared to standard baselines, with the average improvement rising to 19\% when only a small number of patient records are available for training a prediction modelfor a given clinical outcome .
Edit intent: clarity
Original:  We adapt common representation learning techniques used in other domains and find that representations inspired by language models enable a 3.5\% mean improvement in AUROC on five clinical outcomes compared to standard baselines, with the average improvement rising to 19\% when only a small number of patients are available for training a prediction modelfor a given clinical outcome .
Revised:  We adapt common representation learning techniques used in other domains and find that representations inspired by language models enable a 3.5\% mean improvement in AUROC on five clinical outcomes compared to standard baselines, with the average improvement rising to 19\% when only a small number of patients are available for training the clinical prediction model .
Edit intent: clarity
Original:  Although many success determinants have been reported in the literature, it remains unclear whether the cover photo and the text in the title and description could be combined in a fusion classifier to better predict the crowdfunding campaign's success.
Revised:  Although many successful determinants have been reported in the literature, it remains unclear whether the cover photo and the text in the title and description could be combined in a fusion classifier to better predict the crowdfunding campaign's success.
Edit intent: fluency