wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Furthermore,it always holds that v_{\tau\rm rel \leq 2v_{\pi}, thus the worst-case complexity of DynaMITE is }%DIFDELCMD < \tilde{\cal O}%%% (\frac{TR{\varepsilon} +\frac{\tau_{\rm rel} v_{\pi}}{\varepsilon^{2}}), improving the dependence of classical methods on the loose bounds T and V}  .
Revised:  Furthermore,it always holds that v_{\tau\rm rel \leq 2v_{\pi}, thus the worst-case complexity of DynaMITE is }%DIFDELCMD < \tilde{\cal O}%%% (\frac{TR{\varepsilon} +\frac{\tau_{\rm rel} v_{\pi}}{\varepsilon^{2}}), improving the dependence of classical methods on the loose bounds T and V} (f)), thus DynaMITE outperforms prior methods in these cases .
Edit intent: meaning-changed
Original: As SARS-CoV-2 is becoming endemic, a sustainable strategy to manage the pandemic is needed, especially when facing a steep wave. We identified a metastable regime at low case numbers, where test-trace-and-isolate (TTI) together with moderate contact reduction is sufficient to control the spread.
Revised: The traditional long-term solutions for epidemic control involve eradication or herd immunity. Neither of them will be attained within a few months for the COVID-19 pandemic. Here, we analytically derive the existence of a third, viable solution: a stable equilibrium at low case numbers, where test-trace-and-isolate (TTI) together with moderate contact reduction is sufficient to control the spread.
Edit intent: meaning-changed
Original:  We identified a metastable regime at low case numbers, where test-trace-and-isolate (TTI) together with moderate contact reduction is sufficient to control the spread.
Revised:  We identified a metastable regime at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events, and only moderate contact restrictions remain necessary. Across wide parameter ranges of our complementary compartmental model, the equilibrium is reached at or below 10 daily new cases per million people. Such low levels had been maintained over months in most European countries.
Edit intent: meaning-changed
Original:  However, this control is lost once case numbers overwhelm the limited TTI capacity. Beyond that tipping point , increasingly more infectious individuals remain undetected, generating a self-accelerating spread .
Revised:  However, this equilibrium is endangered (i) if contact restrictions are relaxed, or (ii) if case numbers grow too high. The latter destabilisation marks a novel tipping point beyond which the spread self-accelerates because test-trace-and-isolate capacities are overwhelmed .
Edit intent: clarity
Original:  To reestablish control  , a lockdown (circuit breaker) has to strike a delicate balance between duration, stringency, and timeliness; otherwise, lockdowns are ineffective, or their effect is soon lost. However, once reestablishing control at low case numbers, no additional lockdowns are necessary.
Revised:  To reestablish control , a lockdown is required. We show that a lockdown is either effective within a few weeks, or tends to fail its aim. If effective, recurring lockdowns are not necessary -- contrary to the oscillating dynamics previously presented in the context of circuit breakers, and contrary to a regime with high case numbers -- if moderate contact reductions are maintained. Hence, at low case numbers, no additional lockdowns are necessary.
Edit intent: meaning-changed
Original:  However, once reestablishing control at low case numbers, no additional lockdowns are necessary. In the long-term, immunity and large-scale testing will further facilitate the control of COVID-19 .
Revised:  However, once reestablishing control at low case numbers, the control is easier, and more freedom can be granted. We demonstrate that this strategy reduces case numbers and fatalities by a factor of 5 compared to a strategy focused only on avoiding major congestion of hospitals. Furthermore, our solution minimises lockdown duration, and hence economic impact. In the long term, control will successively become easier due to immunity through vaccination or large scale testing programmes. International coordination would facilitate even more the implementation of this solution .
Edit intent: meaning-changed
Original:  Here, we analytically derive the existence of a third , viable solution: a stable equilibrium at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events, and only moderate contact restrictions remain necessary.
Revised:  Here, we analytically derive the existence of a third viable solution: a stable equilibrium at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events, and only moderate contact restrictions remain necessary.
Edit intent: fluency
Original:  Here, we analytically derive the existence of a third , viable solution: a stable equilibrium at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events, and only moderate contact restrictions remain necessary.
Revised:  Here, we analytically derive the existence of a third , viable solution: a stable equilibrium at low case numbers, where test-trace-and-isolate policies partially compensate for local spreading events, and only moderate restrictions remain necessary.
Edit intent: clarity
Original:  Across wide parameter ranges of our complementary compartmental model, the equilibriumis reached at or below 10 daily new cases per million people . Such low levels had been maintained over months in most European countries. However, this equilibrium is endangered (i) if contact restrictions are relaxed , or (ii) if case numbers grow too high.
Revised:  Across wide parameter ranges of our complementary compartmental model, the equilibriumis reached at or below 10 daily new cases per million people or less. However, stability is endangered if restrictions are relaxed , or (ii) if case numbers grow too high.
Edit intent: clarity
Original:  We show that a lockdown is either effective within a few weeks, or tends to fail its aim. If effective, recurring lockdowns are not necessary -- contrary to the oscillating dynamics previously presented in the context of circuit breakers, and contrary to a regime with high case numbers -- if moderate contact reductions are maintained.
Revised:  We show that a lockdown can reestablish control and that recurring lockdowns are not necessary -- contrary to the oscillating dynamics previously presented in the context of circuit breakers, and contrary to a regime with high case numbers -- if moderate contact reductions are maintained.
Edit intent: clarity
Original:  If effective, recurring lockdowns are not necessary -- contrary to the oscillating dynamics previously presented in the context of circuit breakers, and contrary to a regime with high case numbers -- if moderate contact reductions are maintained. Hence, at low case numbers, the control is easier, and more freedom can be granted. We demonstrate that this strategy reduces case numbers and fatalities by a factor of 5 compared to a strategy focused only on avoiding major congestion of hospitals. Furthermore, our solution minimises lockdown duration, and hence economic impact.
Revised:  If effective, recurring lockdowns are not necessary given sustained, moderate contact reduction. We illustrate how this strategy profits from vaccination and helps mitigate variants of concern. This strategy reduces cumulative cases (and fatalities) 4x more than strategies that only avoid hospital collapse.
Edit intent: clarity
Original: Many online comments on social media platforms are hateful , humorous, or sarcastic. The sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments , which leads to misinterpretations by the existing sentiment analysis models.
Revised: Many online comments on social media platforms are hateful , humorous, or hateful. This sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments , which leads to misinterpretations by the existing sentiment analysis models.
Edit intent: clarity
Original:  The sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments , which leads to misinterpretations by the existing sentiment analysis models. A lot of research has already been done to detect sarcasm in the text using user-based, topical, and conversational information but not much work has been done to use inter-sentence contextual information for detecting the same.
Revised:  The sarcastic nature of these short texts change the actual sentiments of the review as predicted by a machine learning model that attempts to detect sentiment alone. Thus, having a model that is explicitly aware of these features should help it perform better on reviews that are characterized by them. Several research has already been done to detect sarcasm in the text using user-based, topical, and conversational information but not much work has been done to use inter-sentence contextual information for detecting the same.
Edit intent: meaning-changed
Original:  A lot of research has already been done to detect sarcasm in the text using user-based, topical, and conversational information but not much work has been done to use inter-sentence contextual information for detecting the same. This paper proposes a new state-of-the-art deep learning architecture that uses a novel Bidirectional Inter-Sentence Contextual Attention mechanism(Bi-ISCA) to capture inter-sentence dependencies for detecting sarcasm in the user-generated short text using only the conversational context .
Revised:  A lot of research has already been done in this field. This paper deals with sarcasm detection on reddit comments. Several machine learning and deep learning algorithms have been applied for the same but each of these models only take into account the initial text instead of the conversation which serves as a better measure to determine sarcasm. The other shortcoming these papers have is they rely on word embedding for representing comments and thus do not take into account the problem of polysemy(A word can have multiple meanings based on the context in which it appears). These existing modules were able to solve the problem of capturing inter sentence contextual information but not the intra sentence contextual information. So we propose a novel architecture which solves the problem of sarcasm detection by capturing intra sentence contextual information using a novel contextual attention mechanism .
Edit intent: meaning-changed
Original:  The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words phrases responsible for invoking sarcasm . Bi-ISCA generates state-of-the-art results on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter).
Revised:  The proposed phrases responsible for invoking sarcasm . Bi-ISCA generates state-of-the-art results on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter).
Edit intent: clarity
Original:  The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words phrases responsible for invoking sarcasm . Bi-ISCA generates state-of-the-art results on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter). To the best of our knowledge, none of the existing state-of-the-art models use an inter-sentence contextual attention mechanism to detect sarcasm in the user-generated short text using only conversational context .
Revised:  The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words model solves the problem of polysemy also by using context enriched language modules like ELMO and BERT in its first component. This model comprises a total of three major components which takes into account inter sentence, intra sentence contextual information and at last use a convolutional neural network for capturing global contextual information for sarcasm detection. The proposed model was able to generate decent results and cleared showed potential to perform state of the art if trained on a larger dataset .
Edit intent: clarity
Original:  Many online reviews are sarcastic , humorous, or hateful. This sarcastic nature of these short texts change the actual sentiments of the review as predicted by a machine learning model that attempts to detect sentiment alone.
Revised:  Many online reviews are sarcastic , humorous, or sarcastic. The sarcastic nature of these short texts change the actual sentiments of the review as predicted by a machine learning model that attempts to detect sentiment alone.
Edit intent: coherence
Original:  This sarcastic nature of these short texts change the actual sentiments of the review as predicted by a machine learning model that attempts to detect sentiment alone. Thus, having a model that is explicitly aware of these features should help it perform better on reviews that are characterized by them. Several research has already been done in this field.
Revised:  This sarcastic nature of these comments (especially the short ones) alters their actual implied sentiments, which leads to misinterpretations by the existing sentiment analysis models. A lot of research has already been done in this field.
Edit intent: clarity
Original:  Several research has already been done in this field. This paper deals with sarcasm detection on reddit comments. Several machine learning and deep learning algorithms have been applied for the same but each of these models only take into account the initial text instead of the conversation which serves as a better measure to determine sarcasm . The other shortcoming these papers have is they rely on word embedding for representing comments and thus do not take into account the problem of polysemy(A word can have multiple meanings based on the context in which it appears). These existing modules were able to solve the problem of capturing inter sentence contextual information but not the intra sentence contextual information .
Revised:  Several research has already been done to detect sarcasm in the text using user-based, topical, and conversational information but not the intra sentence contextual information .
Edit intent: clarity
Original:  These existing modules were able to solve the problem of capturing inter sentence contextual information but not the intra sentence contextual information .
Revised:  These existing modules were able to solve the problem of capturing inter sentence contextual information but not much work has been done to use inter-sentence contextual information for detecting the same. This paper proposes a new state-of-the-art deep learning architecture that uses a novel Bidirectional Inter-Sentence Contextual Attention mechanism (Bi-ISCA) to capture inter-sentence dependencies for detecting sarcasm in the user-generated short text using only the conversational context .
Edit intent: meaning-changed
Original:  So we propose a novel architecture which solves the problem of sarcasm detection by capturing intra sentence contextual information using a novel contextual attention mechanism . The proposed model solves the problem of polysemy also by using context enriched language modules like ELMO and BERT in its first component. This model comprises a total of three major components which takes into account inter sentence, intra sentence contextual information and at last use a convolutional neural network for capturing global contextual information for sarcasm detection. The proposed model was able to generate decent results and cleared showed potential to perform state of the art if trained on a larger dataset  .
Revised:  The proposed deep learning model demonstrates the capability to capture explicit, implicit, and contextual incongruous words .
Edit intent: clarity
Original:  The proposed model was able to generate decent results and cleared showed potential to perform state of the art if trained on a larger dataset  .
Revised:  The proposed model was able to generate decent results and cleared showed potential to perform state of the art if trained on a larger dataset phrases responsible for invoking sarcasm. Bi-ISCA generates state-of-the-art results on two widely used benchmark datasets for the sarcasm detection task (Reddit and Twitter). To the best of our knowledge, none of the existing state-of-the-art models use an inter-sentence contextual attention mechanism to detect sarcasm in the user-generated short text using only conversational context .
Edit intent: meaning-changed
Original:  Our algorithm is unique in that it builds the reachable sets by expanding a front of polyhedral cells in the input space, rather than iterating layer-by-layer through the network as in other methods.
Revised:  Our algorithm is unique in that it builds the reachable sets by incrementally enumerating polyhedral cells in the input space, rather than iterating layer-by-layer through the network as in other methods.
Edit intent: clarity
Original:  In particular, in the vertex-arrival streams, we get a randomized O( \frac{\sqrt{n}{\eps^2}} }{\epsilon^2}} \log n) space algorithm for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%%  ) factor in a planar graph on n vertices.
Revised:  In particular, in the vertex-arrival streams, we get a randomized O( }{\eps^2}} }{\epsilon^2}} \log n) space algorithm for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%% ) factor in a planar graph on n vertices.
Edit intent: meaning-changed
Original:  In particular, in the vertex-arrival streams, we get a randomized O( \frac{\sqrt{n}{\eps^2}} }{\epsilon^2}} \log n) space algorithm for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%%  ) factor in a planar graph on n vertices.
Revised:  In particular, in the vertex-arrival streams, we get a randomized O( \frac{\sqrt{n}{\eps^2}} \frac{\sqrt{n}{\epsilon^2}} \log n) space algorithm for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%% ) factor in a planar graph on n vertices.
Edit intent: coherence
Original:  In particular, in the vertex-arrival streams, we get a randomized O( \frac{\sqrt{n}{\eps^2}} }{\epsilon^2}} \log n) space algorithm for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%%  ) factor in a planar graph on n vertices.
Revised:  In particular, in the vertex-arrival streams, we get a randomized O( \frac{\sqrt{n}{\eps^2}} }{\epsilon^2}} \log n) space algorithm for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%% \epsilon ) factor in a planar graph on n vertices.
Edit intent: meaning-changed
Original:  Similarly, we get a simultaneous protocol in the vertex-partition model for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%%  ) factor using O( \frac{n^{2/3}{\eps^2}} }{\epsilon^2}} \log n) communication from each player.
Revised:  Similarly, we get a simultaneous protocol in the vertex-partition model for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%% \epsilon ) factor using O( \frac{n^{2/3}{\eps^2}} }{\epsilon^2}} \log n) communication from each player.
Edit intent: meaning-changed
Original:  Similarly, we get a simultaneous protocol in the vertex-partition model for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%%  ) factor using O( \frac{n^{2/3}{\eps^2}} }{\epsilon^2}} \log n) communication from each player.
Revised:  Similarly, we get a simultaneous protocol in the vertex-partition model for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%% ) factor using O( }{\eps^2}} }{\epsilon^2}} \log n) communication from each player.
Edit intent: coherence
Original:  Similarly, we get a simultaneous protocol in the vertex-partition model for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%%  ) factor using O( \frac{n^{2/3}{\eps^2}} }{\epsilon^2}} \log n) communication from each player.
Revised:  Similarly, we get a simultaneous protocol in the vertex-partition model for approximating the matching size within (3.5+ %DIFDELCMD < \eps%%% ) factor using O( \frac{n^{2/3}{\eps^2}} \frac{n^{2/3}{\epsilon^2}} \log n) communication from each player.
Edit intent: coherence
Original:  Instance robustness formally ensures a prediction is robust to modest changes in the problem input . Further, the robustness model ensures two different predictions can be objectively compared, addressing a shortcoming in prior models. This paper establishes the existence of predictions which satisfy these properties. The paper considers online algorithms with predictions for a network flow allocation problem and the restricted assignment makespan minimization problem .
Revised:  Instance robustness formally ensures a prediction is robust to modest changes in the problem input , where the measure of the change may be problem specific. Instance robustness insists on a smooth degradation in performance as a function of the change. Ideally, the performance is never worse than worst-case bounds. This also allows predictions to be objectively compared. We design online algorithms with predictions for a network flow allocation problem and the restricted assignment makespan minimization problem .
Edit intent: meaning-changed
Original:  For both problems, three key properties are established: existence of useful predictions that give near optimal solutions, robustness of these predictions to errors that smoothly degrade as the underlying problem instance changes , and we prove high quality predictions can be learned from a small sample of prior instances .
Revised:  For both problems, two key properties are established: existence of useful predictions that give near optimal solutions, robustness of these predictions to errors that smoothly degrade as the underlying problem instance changes , and we prove high quality predictions can be learned from a small sample of prior instances .
Edit intent: meaning-changed
Original:  For both problems, three key properties are established: existence of useful predictions that give near optimal solutions, robustness of these predictions to errors that smoothly degrade as the underlying problem instance changes , and we prove high quality predictions can be learned from a small sample of prior instances .
Revised:  For both problems, three key properties are established: high quality predictions can be learned from a small sample of prior instances and these predictions are robust to errors that smoothly degrade as the underlying problem instance changes , and we prove high quality predictions can be learned from a small sample of prior instances .
Edit intent: clarity
Original:  For both problems, three key properties are established: existence of useful predictions that give near optimal solutions, robustness of these predictions to errors that smoothly degrade as the underlying problem instance changes , and we prove high quality predictions can be learned from a small sample of prior instances .
Revised:  For both problems, three key properties are established: existence of useful predictions that give near optimal solutions, robustness of these predictions to errors that smoothly degrade as the underlying problem instance changes .
Edit intent: coherence
Original: Accelerating the learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low or unknown. In this work , we propose a REPresentation-And-INstance Transfer algorithm (REPAINT) for deep actor-critic reinforcement learningparadigm.
Revised: Accelerating the learning processes for complex tasks by leveraging previously learned tasks has been one of the most challenging problems in reinforcement learning, especially when the similarity between source and target tasks is low . This work proposes REPresentation And INstance Transfer (REPAINT) for deep actor-critic reinforcement learningparadigm.
Edit intent: clarity