wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Model simulations using the Covid-19 data obtained from John Hopkins University for some selected states in the US suggest that even late intervention of CT may reasonably reduce the transmission of Covid-19 and reduce peak hospitalizations and deaths . In particular, our findings suggest that effective monitoring policy of tracked cases and tracking of traced contacts while incubating are more crucial than tracing more contacts .
Revised:  Model simulations using the Covid-19 data obtained from John Hopkins University for some selected states in the US suggest that even late intervention of CT may reasonably reduce the transmission of Covid-19 and reduce peak hospitalizations and deaths .
Edit intent: coherence
Original:  Finally, to guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies in terms of their work and locality .
Revised:  Finally, to guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies . Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering .
Edit intent: meaning-changed
Original: The success of pre-trained contextualized language models such as BERT motivates a line of work that investigates linguistic knowledge inside such models in order to explain the huge improvement in downstream tasks .
Revised: BERT has been used for solving commonsense tasks such as CommonsenseQA. While prior research has found that BERT does contain commonsense information to some extent, there has been work showing that pre-trained contextualized language models such as BERT motivates a line of work that investigates linguistic knowledge inside such models in order to explain the huge improvement in downstream tasks .
Edit intent: meaning-changed
Original: The success of pre-trained contextualized language models such as BERT motivates a line of work that investigates linguistic knowledge inside such models in order to explain the huge improvement in downstream tasks . While previous work shows syntactic, semantic and word sense knowledge in BERT , little work has been done on investigating how BERT solves CommonsenseQA tasks.
Revised: The success of pre-trained models can rely on spurious associations (e.g., data bias) rather than key cues in solving sentiment classification and other problems. We quantitatively investigate the presence of structural commonsense cues in BERT when solving commonsense tasks , little work has been done on investigating how BERT solves CommonsenseQA tasks.
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Original:  While previous work shows syntactic, semantic and word sense knowledge in BERT , little work has been done on investigating how BERT solves CommonsenseQA tasks.In particular, it is an interesting research question whether BERT relies on shallow syntactic patterns or deeper commonsense knowledge for disambiguation. We propose two attention-based methods to analyze commonsense knowledge inside BERT , and the contribution of such knowledge for the model prediction.
Revised:  While previous work shows syntactic, semantic and word sense knowledge in BERT , and the contribution of such knowledge for the model prediction.
Edit intent: coherence
Original:  We find that attention heads successfully capture the structured commonsense knowledge encoded in ConceptNet, which helps BERT solve commonsense tasks directly. Fine-tuning further makes BERT learn to use the commonsense knowledge on higher layers .
Revised:  Using two different measures, we find that BERT does use relevant knowledge for solving the task, and the presence of commonsense knowledge is positively correlated to the model accuracy .
Edit intent: coherence
Original:  We significantly improve upon the RMSE of previous work on fisheye by 25\% reduction in RMSE. As there is limited work on fisheye cameras, we evaluated the proposed method on KITTI using a pinhole model.
Revised:  We significantly improve upon the RMSE of previous work on fisheye by 25\% reduction in RMSE. As there is little work on fisheye cameras, we evaluated the proposed method on KITTI using a pinhole model.
Edit intent: clarity
Original:  The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions  and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction.
Revised:  The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions , and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction.
Edit intent: fluency
Original:  The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can make a significant difference in the performance of ML models, and it can boost ML performance by up to 29 \%.
Revised:  The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20 \%.
Edit intent: meaning-changed
Original:  Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models , and we found that soil and weather-related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables.
Revised:  Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found that soil and weather-related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables.
Edit intent: fluency
Original:  Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models , and we found that soil and weather-related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables.
Revised:  Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models , and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables.
Edit intent: meaning-changed
Original:  Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient , and ML models need more hydrological inputs to make improved yield predictions.
Revised:  Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.
Edit intent: fluency
Original:  In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then compare the learned relationships of words in the model to the known ground truth of the chess board, move legality, and historical patterns of play.
Revised:  In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then "launch" clusters of synthetic agents into the model, using text strings to create context and orientation. We compare the trajectories contained in the text generated by the agents/model and compare that to the known ground truth of the chess board, move legality, and historical patterns of play.
Edit intent: meaning-changed
Original:  Central to pyroptosis induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the formation of the NLRP3 inflammasome .
Revised:  The formation of the NLRP3 inflammasome is central to pyroptosis, which may be induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the formation of the NLRP3 inflammasome .
Edit intent: coherence
Original:  In this study, we present a single-cell mathematical model that captures the formation of the NLRP3 inflammasome, pyroptotic cell death and drug-responses to tranilast .
Revised:  In this study, we present a single-cell mathematical model that captures the formation of the NLRP3 inflammasome, pyroptotic cell death and responses to anti-inflammatory intervention that hinder the formation of the NLRP3 inflammasome .
Edit intent: meaning-changed
Original:  The model demonstrates that tranilast delays the formation of the NLRP3 inflammasome, and thus may alter the mode of cell death from inflammatory (pyroptosis) to non-inflammatory ( e.g., apoptosis)  .
Revised:  Our results demonstrate that an anti-inflammatory drug can delay the formation of the NLRP3 inflammasome, and thus may alter the mode of cell death from inflammatory (pyroptosis) to non-inflammatory ( e.g., apoptosis) .
Edit intent: clarity
Original:  The model demonstrates that tranilast delays the formation of the NLRP3 inflammasome, and thus may alter the mode of cell death from inflammatory (pyroptosis) to non-inflammatory ( e.g., apoptosis)  .
Revised:  The model demonstrates that tranilast delays the formation of the NLRP3 inflammasome, and thus may alter the mode of cell death from inflammatory (pyroptosis) to non-inflammatory e.g., apoptosis) .
Edit intent: fluency
Original:  The model demonstrates that tranilast delays the formation of the NLRP3 inflammasome, and thus may alter the mode of cell death from inflammatory (pyroptosis) to non-inflammatory ( e.g., apoptosis)  .
Revised:  The model demonstrates that tranilast delays the formation of the NLRP3 inflammasome, and thus may alter the mode of cell death from inflammatory (pyroptosis) to non-inflammatory ( e.g., apoptosis) . The single-cell model is being implemented in a SARS-CoV-2 Tissue Simulator, in collaboration with a multidisciplinary coalition investigating within host-dynamics of COVID-19. In this paper, we provide an overview of the SARS-CoV-2 Tissue Simulator and highlight the effects of pyroptosis on a cellular level .
Edit intent: meaning-changed
Original:  First, we consider some simple memory binding tasks in which the output is set to be a copy of the given input and a selective sequence of previous inputs online. Next, we design a more complex binding taskbased on a cue that encodes binding relations .
Revised:  Three cases and their results are discussed similar to the n-back task called, first-order memory binding task, generalized first-order memory task, and second-order memory binding task .
Edit intent: meaning-changed
Original: We study two  random non-forced choice models that explain behavioural patterns suggesting that the no-choice outside option is often selected when people find it hard to decide between the market alternatives available to them, even when these are few and desirable.
Revised: We study two tractable random non-forced choice models that explain behavioural patterns suggesting that the no-choice outside option is often selected when people find it hard to decide between the market alternatives available to them, even when these are few and desirable.
Edit intent: meaning-changed
Original: We study two  random non-forced choice models that explain behavioural patterns suggesting that the no-choice outside option is often selected when people find it hard to decide between the market alternatives available to them, even when these are few and desirable.
Revised: We study two random non-forced choice models that explain behavioural patterns suggesting that the choice-deferral outside option is often selected when people find it hard to decide between the market alternatives available to them, even when these are few and desirable.
Edit intent: meaning-changed
Original:  This value captures the degree of complexity/decision difficulty at the relevant menu and allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on how many as well as on how attractive options are added to it.
Revised:  This value captures the degree of complexity/decision difficulty at the relevant menu and allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on how many as well as how attractive options are added to it.
Edit intent: fluency
Original:  This value captures the degree of complexity/decision difficulty at the relevant menu and allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on how many as well as how attractive options are added to it.
Revised:  This value captures the degree of complexity/decision difficulty at the relevant menu and allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on *how many* as well as how attractive options are added to it.
Edit intent: coherence
Original:  This value captures the degree of complexity/decision difficulty at the relevant menu and allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on how many as well as how attractive options are added to it.
Revised:  This value captures the degree of complexity/decision difficulty at the relevant menu and allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on how many as well as *how attractive* options are added to it.
Edit intent: coherence
Original: We study two tractable random non-forced choice models that explain behavioural patterns suggesting that the choice-deferral outside option is often selected when people find it hard to decide between the market alternatives available to them, even when these are few and desirable. The *decision-conflict logit* extends the logit model with an outside option by assigning a menu-dependent value to that option.
Revised: We propose the class of decision-conflict logit models. These are disciplined extensions of the logit with an outside option by assigning a menu-dependent value to that option.
Edit intent: clarity
Original:  This value captures the degree of complexity /  decision difficulty at the relevant menuand allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on *how many* as well as *how attractive* options are added to it.
Revised:  The relative value of this utility determines the probability of avoiding / decision difficulty at the relevant menuand allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on *how many* as well as *how attractive* options are added to it.
Edit intent: clarity
Original:  This value captures the degree of complexity /  decision difficulty at the relevant menuand allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on *how many* as well as *how attractive* options are added to it.
Revised:  This value captures the degree of complexity / deferring choice and can be interpreted as proxying decision difficulty at the relevant menuand allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on *how many* as well as *how attractive* options are added to it.
Edit intent: meaning-changed
Original:  This value captures the degree of complexity /  decision difficulty at the relevant menuand allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on *how many* as well as *how attractive* options are added to it. The *multicriteria logit* is a special case of this model and introduces multiple utility functions that jointly predict behaviour in a multiplicative-logit way. Every multicriteria logit admits a simple discrete-choice formulation  .
Revised:  This value captures the degree of complexity / decision difficulty at each menu. We focus on two subclasses where such difficulty is endogenous and either:(i) caused by resampling, as captured by the quadratic and dual logit models; (ii) decreasing in the utility differences between alternatives. In the latter approach we focus on reciprocal utility differences. This special case is inspired by the influential drift diffusion neuroeconomic model and predicts several observed choice-deferral phenomena. On the applications side, we show how the quadratic logit admits a simple discrete-choice formulation .
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Original:  While our proposed Directional Laplacian Centrality may be applied to any graph , we study its effectiveness in identifying important Internet Protocol addresses in network flow data.
Revised:  To build intuition about our measure we show how it identifies the most central vertices in standard network data sets and compare to other graph centrality measures. Finally, we focus our attention on studying its effectiveness in identifying important Internet Protocol addresses in network flow data.
Edit intent: meaning-changed
Original:  Using both real and synthetic network flow data, we conduct several experiments to test our measure's sensitivity to two types of injected attack profiles  .
Revised:  Using both real and synthetic network flow data, we conduct several experiments to test our measure's sensitivity to two types of injected attack profiles , and show that vertices participating in injected attack profiles exhibit noticeable changes in our centrality measures, even when the injected anomalies are relatively small, and in the presence of simulated network dynamics .
Edit intent: meaning-changed
Original: Autonomous agentsusing novelty based goal exploration are often efficient in environments that require exploration. However, they get attracted to various forms of distracting unlearnable regions.
Revised: Designing agents, capable of learning autonomously a wide range of skills is critical in order to increase the scope of reinforcement learning. It will both increase the diversity of learned skills and reduce the burden of manually designing reward functions for each skill. Self-supervised agents, setting their own goals, and trying to maximize the diversity of those goals have shown great promise towards this end. However, a currently known limitation of agents trying to maximize the diversity of sampled goals is that they tend to get attracted to various forms of distracting unlearnable regions.
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Original:  To solve this problem, absolute learning progress (ALP)has been used in reinforcement learning agents with predefined goal features and access to expert knowledge. This work extends those concepts to unsupervised image-based goal exploration.
Revised:  To solve this problem, absolute learning progress (ALP)has been used in reinforcement learning agents with predefined goal features or expert knowledge, absolute Learning Progress (ALP) provides a way to distinguish between regions that can be controlled and those that cannot. However, those methods often fall short when the agents are only provided with raw sensory inputs such as images. In this work we extend those concepts to unsupervised image-based goal exploration.
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Original:  We present the GRIMGEP framework: it provides a learned robust goal sampling prior that can be used on top of current state-of-the-art novelty seeking goal exploration approaches, enabling them to ignore noisy distracting regions while searching for novelty in the learnable regions .
Revised:  We propose a framework that allows agents to autonomously identify and ignore noisy distracting regions while searching for novelty in the learnable regions .
Edit intent: clarity
Original:  We present the GRIMGEP framework: it provides a learned robust goal sampling prior that can be used on top of current state-of-the-art novelty seeking goal exploration approaches, enabling them to ignore noisy distracting regions while searching for novelty in the learnable regions . It clusters the goal space and estimates ALP for each cluster. These ALP estimates can then be used to detect the distracting regions, and build a prior that enables further goal sampling mechanisms to ignore them .
Revised:  We present the GRIMGEP framework: it provides a learned robust goal sampling prior that can be used on top of current state-of-the-art novelty seeking goal exploration approaches, enabling them to ignore noisy distracting regions while searching for novelty in the learnable regions to both improve overall performance and avoid catastrophic URLetting. Our framework can be combined with any state-of-the-art novelty seeking goal exploration approaches .
Edit intent: clarity
Original:  We construct an image based environment with distractors , on which we show that wrapping current state-of-the-art goal exploration algorithms with our framework allows them to concentrate on interesting regions of the environment and drastically improve performances.
Revised:  We construct a rich 3D image based environment with distractors , on which we show that wrapping current state-of-the-art goal exploration algorithms with our framework allows them to concentrate on interesting regions of the environment and drastically improve performances.
Edit intent: meaning-changed
Original:  We construct an image based environment with distractors , on which we show that wrapping current state-of-the-art goal exploration algorithms with our framework allows them to concentrate on interesting regions of the environment and drastically improve performances.
Revised:  We construct an image based environment with distractors , on which we show that wrapping current state-of-the-art goal exploration algorithms with our framework allows them to concentrate on interesting regions of the environment , resulting in drastically improved performances.
Edit intent: clarity
Original:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) }  , uses O(  d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which  uses O(log^2 n) bits of space  .
Revised:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + d/\epsilon )) } , uses O( d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which uses O(log^2 n) bits of space .
Edit intent: fluency
Original:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon )) }  , uses O(  d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which  uses O(log^2 n) bits of space  .
Revised:  As one of our main results, we devise an algorithm for d-Hitting Set that runs in time n^{O(d^2 + ( d/\epsilon } , uses O( d^2 + ( d/\epsilon) log n) bits of space, and achieves an approximation ratio of O((d/ \epsilon ) n^{\epsilon}) for any positive \epsilon \leq 1 and any constant d\in N . In particular, this yields a factor-O( d log n) approximation algorithm which uses O(log^2 n) bits of space .
Edit intent: fluency