wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In order to get a numerical stable and positive definite modification of the sample covariance matrix in the high-dimensional data setting, we in this paper  consider the condition number constrained covariance matrix approximation problem and present its explicit solution with respect to  Frobenius norm.
Revised:  In order to get a numerically stable and positive definite modification of the sample covariance matrix in the high-dimensional data setting, we in this paper consider the condition number constrained covariance matrix approximation problem and present its explicit solution with respect to Frobenius norm.
Edit intent: clarity
Original:  In order to get a numerical stable and positive definite modification of the sample covariance matrix in the high-dimensional data setting, we in this paper  consider the condition number constrained covariance matrix approximation problem and present its explicit solution with respect to  Frobenius norm.
Revised:  In order to get a numerical stable and positive definite modification of the sample covariance matrix in the high-dimensional data setting, in this paper consider the condition number constrained covariance matrix approximation problem and present its explicit solution with respect to Frobenius norm.
Edit intent: coherence
Original:  In order to get a numerical stable and positive definite modification of the sample covariance matrix in the high-dimensional data setting, we in this paper  consider the condition number constrained covariance matrix approximation problem and present its explicit solution with respect to  Frobenius norm.
Revised:  In order to get a numerical stable and positive definite modification of the sample covariance matrix in the high-dimensional data setting, we in this paper we consider the condition number constrained covariance matrix approximation problem and present its explicit solution with respect to Frobenius norm.
Edit intent: meaning-changed
Original:  In order to get a numerical stable and positive definite modification of the sample covariance matrix in the high-dimensional data setting, we in this paper  consider the condition number constrained covariance matrix approximation problem and present its explicit solution with respect to  Frobenius norm.
Revised:  In order to get a numerical stable and positive definite modification of the sample covariance matrix in the high-dimensional data setting, we in this paper consider the condition number constrained covariance matrix approximation problem and present its explicit solution with respect to the Frobenius norm.
Edit intent: fluency
Original:  In this paper, we present a novel approach for hard label black-box attacks against Natural Language Processing (NLP) classifiers, where no model information is disclosed, and an attacker can only query the model to get  final decision of the classifier, without confidence scores of the classes involved.
Revised:  In this paper, we present a novel approach for hard-label black-box attacks against Natural Language Processing (NLP) classifiers, where no model information is disclosed, and an attacker can only query the model to get final decision of the classifier, without confidence scores of the classes involved.
Edit intent: fluency
Original:  In this paper, we present a novel approach for hard label black-box attacks against Natural Language Processing (NLP) classifiers, where no model information is disclosed, and an attacker can only query the model to get  final decision of the classifier, without confidence scores of the classes involved.
Revised:  In this paper, we present a novel approach for hard label black-box attacks against Natural Language Processing (NLP) classifiers, where no model information is disclosed, and an attacker can only query the model to get a final decision of the classifier, without confidence scores of the classes involved.
Edit intent: fluency
Original: The 1st wave of COVID-19 changed social distancing around the globe: severe lockdowns to stop pandemics at the cost of state economies preceded a series of lockdown lifts. To understand social distancing dynamics it is important to combine basic epidemiology models for viral unfold (like SIR ) with game theory tools, such as a utility function that quantifies individual or government forecast for epidemic damage and economy cost as the functions of social distancing.
Revised: To describe the dynamics of social distancing during pandemics, we follow previous efforts to combine basic epidemiology models for viral unfold (like SIR ) with game theory tools, such as a utility function that quantifies individual or government forecast for epidemic damage and economy cost as the functions of social distancing.
Edit intent: clarity
Original:  To understand social distancing dynamics it is important to combine basic epidemiology models for viral unfold (like SIR ) with game theory tools, such as a utility function that quantifies individual or government forecast for epidemic damage and economy cost as the functions of social distancing.
Revised:  To understand social distancing dynamics it is important to combine basic epidemiology models (e.g. SIR - Susceptible, Infected, and Recovered ) with game theory tools, such as a utility function that quantifies individual or government forecast for epidemic damage and economy cost as the functions of social distancing.
Edit intent: meaning-changed
Original:  To understand social distancing dynamics it is important to combine basic epidemiology models for viral unfold (like SIR ) with game theory tools, such as a utility function that quantifies individual or government forecast for epidemic damage and economy cost as the functions of social distancing. Here we present a model that predicts a series of discontinuous transitions in social distancing after pandemics climax .
Revised:  To understand social distancing dynamics it is important to combine basic epidemiology models for viral unfold (like SIR ) with game and economy theory tools. We present an extension of the SIR model that predicts a series of discontinuous transitions in social distancing after pandemics climax .
Edit intent: clarity
Original:  When certain dynamical variables (hidden neurons) are integrated out from our microscopic theory, one can recover many of the models that were previously discussed in the literature, e.g. the model presented in '' Hopfield Networks is All You Need '' paper.
Revised:  When certain dynamical variables (hidden neurons) are integrated out from our microscopic theory, one can recover many of the models that were previously discussed in the literature, e.g. the model presented in " Hopfield Networks is All You Need '' paper.
Edit intent: fluency
Original:  When certain dynamical variables (hidden neurons) are integrated out from our microscopic theory, one can recover many of the models that were previously discussed in the literature, e.g. the model presented in '' Hopfield Networks is All You Need '' paper.
Revised:  When certain dynamical variables (hidden neurons) are integrated out from our microscopic theory, one can recover many of the models that were previously discussed in the literature, e.g. the model presented in '' Hopfield Networks is All You Need " paper.
Edit intent: fluency
Original:  Other generic video object segmentation tasks have dealt with instance-wise motion segmentation but on much simpler setting and have not dealt with the multi-task learning problem for both semantic and class agnostic instance segmentation. In this paper, we propose a motion-based instance segmentation task and provide a new annotated dataset based on KITTIMoSeg , which will be released publicly. Our datasetprovide extra class annotations which is crucial for studying class agnostic  segmentation.
Revised:  As the main issue is the lack of large public datasets, we create a new InstanceMotSeg dataset comprising of 12.9K samples improving upon our KITTIMoSeg dataset. In addition to providing instance level annotations, we have added 4 additional classes which is crucial for studying class agnostic segmentation.
Edit intent: meaning-changed
Original:  We further propose an efficient multi-task learning approach that learns an extra class agnostic instance segmentation head through sharing the prototype generation network with the semantic head .
Revised:  We adapt YOLACT and implement a motion-based class agnostic instance segmentation model which would act as a baseline for the dataset. We also extend it to an efficient multi-task learning approach that learns an extra class agnostic instance segmentation head through sharing the prototype generation network with the semantic head .
Edit intent: meaning-changed
Original:  By rigorous error analysis, we first show that the ELRI scheme provides the first order accuracy in H^\gamma for initial data in H^{\gamma+1} for \gamma> %DIFDELCMD < \frac32%%% \frac12 . Moreover, by adding two more correction terms to the first order scheme, we show a second order ELRI that provides the second order accuracy in H^\gamma for initial data in H^{\gamma+3} \ge0 .
Revised:  By rigorous error analysis, we first show that the ELRI scheme provides the first order accuracy in H^\gamma for initial data in H^{\gamma+1} for \gamma> %DIFDELCMD < \frac32%%% \frac12 . Moreover, by adding two more correction terms to the first order scheme, we show a second order ELRI that provides the second order accuracy in H^\gamma for initial data in H^{\gamma+3} for \gamma\ge0 .
Edit intent: clarity
Original: It is notoriously hard to build a bad Random Forest (RF). Concurrently, RF is perhaps the only standard ML algorithm that blatantly overfits in-sample without any consequence out-of-sample.
Revised: It is notoriously difficult to build a bad Random Forest (RF). Concurrently, RF is perhaps the only standard ML algorithm that blatantly overfits in-sample without any consequence out-of-sample.
Edit intent: fluency
Original: It is notoriously difficult to build a bad Random Forest (RF). Concurrently, RF is perhaps the only standard ML algorithm that blatantly overfits in-sample without any  consequence out-of-sample.
Revised: It is notoriously difficult to build a bad Random Forest (RF). Concurrently, RF is perhaps the only standard ML algorithm that blatantly overfits in-sample without any apparent consequence out-of-sample.
Edit intent: clarity
Original:  Working with a data set comprising around 20 billion 1-grams, we first compare each day's 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016 onwards.
Revised:  Working with a data set comprising around 20 billion 1-grams, we first compare each day's 1-gram and 2-gram usage frequencies to those of a year before, to create day- and week-scale timelines for Trump stories for 2016 through 2020.
Edit intent: clarity
Original:  We show that 2017 was the most turbulent  year for Trump , and that story generation slowed dramatically during the  COVID-19 pandemic in 2020.
Revised:  We show that 2017 was the most turbulent year for Trump , and that story generation slowed dramatically during the COVID-19 pandemic in 2020, with rapid turnover returning first with the Black Lives Matter protests following URLe Floyd's murder and then later by events leading up to and following the presidential election.
Edit intent: meaning-changed
Original:  Story generation slowed dramatically during the  major waves of the COVID-19 pandemic in 2020 , with rapid turnover returning first with the Black Lives Matter protests following URLe Floyd's murder and then later by events leading up to and following the presidential election.
Revised:  In 2020, story generation slowed dramatically during the major waves of the COVID-19 pandemic in 2020 , with rapid turnover returning first with the Black Lives Matter protests following URLe Floyd's murder and then later by events leading up to and following the presidential election.
Edit intent: meaning-changed
Original:  Story generation slowed dramatically during the  major waves of the COVID-19 pandemic in 2020 , with rapid turnover returning first with the Black Lives Matter protests following URLe Floyd's murder and then later by events leading up to and following the presidential election.
Revised:  Story generation slowed dramatically during the first two major waves of the COVID-19 pandemic in 2020 , with rapid turnover returning first with the Black Lives Matter protests following URLe Floyd's murder and then later by events leading up to and following the presidential election.
Edit intent: meaning-changed
Original:  Story generation slowed dramatically during the  major waves of the COVID-19 pandemic in 2020 , with rapid turnover returning first with the Black Lives Matter protests following URLe Floyd's murder and then later by events leading up to and following the presidential election.
Revised:  Story generation slowed dramatically during the major waves of the COVID-19 pandemic , with rapid turnover returning first with the Black Lives Matter protests following URLe Floyd's murder and then later by events leading up to and following the presidential election.
Edit intent: coherence
Original:  Story generation slowed dramatically during the  major waves of the COVID-19 pandemic in 2020 , with rapid turnover returning first with the Black Lives Matter protests following URLe Floyd's murder and then later by events leading up to and following the presidential election.
Revised:  Story generation slowed dramatically during the major waves of the COVID-19 pandemic in 2020 , with rapid turnover returning first with the Black Lives Matter protests following URLe Floyd's murder and then later by events leading up to and following the 2020 US presidential election, including the storming of the US Capitol six days into 2021.
Edit intent: meaning-changed
Original:  We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors , to assess the ability of this track-and-trace strategy to mitigate the epidemic.
Revised:  We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the ability of this track-and-trace strategy to mitigate the epidemic.
Edit intent: fluency
Original:  Results suggest that the app can contribute substantially to the reduction of infections in the population  , although complete suppression of the virus is unlikely to be achieved.
Revised:  Results suggest that the app can contribute substantially to the reduction of infections in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing , although complete suppression of the virus is unlikely to be achieved.
Edit intent: meaning-changed
Original:  Results suggest that the app can contribute substantially to the reduction of infections in the population  , although complete suppression of the virus is unlikely to be achieved. The model also shows that, while adopting the app is beneficial for epidemic control in most cases , a high adoption rate is likely to generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive.
Revised:  Results suggest that the app can contribute substantially to the reduction of infections in the population , a high adoption rate is likely to generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive.
Edit intent: coherence
Original:  The model also shows that, while adopting the app is beneficial for epidemic control in most cases , a high adoption rate is likely to generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive.
Revised:  The model also shows that, while adopting the app is beneficial for epidemic control in most cases , a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive.
Edit intent: clarity
Original: We propose an algorithmic version of the hypergraph Tur\'{a}n problem (AHTP) : given a t-uniform hypergraph H=(V,E), the goal is to find the smallest collection of (t-1) -element subsets of V such that every hyperedge e \in E contains one of these subsets.
Revised: A famous conjecture of Tuza states that the minimum number of edges needed to cover all the triangles in a graph is at most twice the maximum number of edge-disjoint triangles. This conjecture was couched in a broader setting by Aharoni and Zerbib who proposed a hypergraph version of this conjecture, and also studied its implied fractional versions. We establish the fractional version of the Aharoni-Zerbib conjecture up to lower order terms. Specifically, we give a factor t/2+ O(\sqrt{t \log t an algorithmic version of the hypergraph Tur\'{a}n problem (AHTP) : given a t-uniform hypergraph H=(V,E), the goal is to find the smallest collection of (t-1) -element subsets of V such that every hyperedge e \in E contains one of these subsets.
Edit intent: meaning-changed
Original: We propose an algorithmic version of the hypergraph Tur\'{a}n problem (AHTP) : given a t-uniform hypergraph H=(V,E), the goal is to find the smallest collection of (t-1) -element subsets of V such that every hyperedge e \in E contains one of these subsets.
Revised: We propose an algorithmic version of the hypergraph Tur\'{a}n problem (AHTP) . The objective in AHTP is to pick the smallest collection of (t-1) -element subsets of V such that every hyperedge e \in E contains one of these subsets.
Edit intent: clarity
Original: We propose an algorithmic version of the hypergraph Tur\'{a}n problem (AHTP) : given a t-uniform hypergraph H=(V,E), the goal is to find the smallest collection of (t-1) -element subsets of V such that every hyperedge e \in E contains one of these subsets.
Revised: We propose an algorithmic version of the hypergraph Tur\'{a}n problem (AHTP) : given a t-uniform hypergraph H=(V,E), the goal is to find the smallest collection of (t-1) -sized subsets of vertices of an input t-uniform hypergraph such that every hyperedge e \in E contains one of these subsets.
Edit intent: meaning-changed
Original: We propose an algorithmic version of the hypergraph Tur\'{a}n problem (AHTP) : given a t-uniform hypergraph H=(V,E), the goal is to find the smallest collection of (t-1) -element subsets of V such that every hyperedge e \in E contains one of these subsets.
Revised: We propose an algorithmic version of the hypergraph Tur\'{a}n problem (AHTP) : given a t-uniform hypergraph H=(V,E), the goal is to find the smallest collection of (t-1) -element subsets of V such that every hyperedge contains one of these subsets.
Edit intent: meaning-changed
Original:  In addition to its inherent combinatorial interest---for instance, the t=3 case is connected to Tuza's famous conjecture on covering triangles of a graph with edges---variants of AHTP arise in recently proposed reductions to fundamental Euclidean clustering problems.
Revised:  Aharoni and Zerbib also posed whether Tuza's famous conjecture on covering triangles of a graph with edges---variants of AHTP arise in recently proposed reductions to fundamental Euclidean clustering problems.
Edit intent: meaning-changed
Original:  In addition to its inherent combinatorial interest---for instance, the t=3 case is connected to Tuza's famous conjecture on covering triangles of a graph with edges---variants of AHTP arise in recently proposed reductions to fundamental Euclidean clustering problems. AHTP admits a trivial factor t approximation algorithm as it can be cast as an instance of vertex cover on a structured t-uniform hypergraph that is a ``blown-up'' version of H. Our main result is an approximation algorithm with ratio \frac{t simple %DIFAUXCMD hypergraph with hyperedges intersecting in at most one element .
Revised:  In addition to its inherent combinatorial interest---for instance, the t=3 case is connected to Tuza's simple %DIFAUXCMD hypergraph with hyperedges intersecting in at most one element .
Edit intent: meaning-changed
Original:  AHTP admits a trivial factor t approximation algorithm as it can be cast as an instance of vertex cover on a structured t-uniform hypergraph that is a ``blown-up'' version of H. Our main result is an approximation algorithm with ratio \frac{t simple %DIFAUXCMD hypergraph with hyperedges intersecting in at most one element .
Revised:  AHTP admits a trivial factor t approximation algorithm as it can be cast as an instance of vertex cover on a structured t-uniform hypergraph that is a ``blown-up'' version of H. Our main result is an approximation algorithm with ratio \frac{t %DIFAUXCMD hypergraph with hyperedges intersecting in at most one element .
Edit intent: meaning-changed
Original:  AHTP admits a trivial factor t approximation algorithm as it can be cast as an instance of vertex cover on a structured t-uniform hypergraph that is a ``blown-up'' version of H. Our main result is an approximation algorithm with ratio \frac{t simple %DIFAUXCMD hypergraph with hyperedges intersecting in at most one element .
Revised:  AHTP admits a trivial factor t approximation algorithm as it can be cast as an instance of vertex cover on a structured t-uniform hypergraph that is a ``blown-up'' version of H. Our main result is an approximation algorithm with ratio \frac{t simple %DIFAUXCMD conjecture and its hypergraph versions could follow from non-trivial duality gaps between vertex covers and matchings on hypergraphs that exclude certain sub-hypergraphs, for instance, a "tent" structure that cannot occur in the incidence of triangles and edges. We give a strong negative answer to this question, by exhibiting tent-free hypergraphs, and indeed \mathcal{F at most one element .
Edit intent: meaning-changed
Original:  AHTP admits a trivial factor t approximation algorithm as it can be cast as an instance of vertex cover on a structured t-uniform hypergraph that is a ``blown-up'' version of H. Our main result is an approximation algorithm with ratio \frac{t simple %DIFAUXCMD hypergraph with hyperedges intersecting in at most one element .
Revised:  AHTP admits a trivial factor t approximation algorithm as it can be cast as an instance of vertex cover on a structured t-uniform hypergraph that is a ``blown-up'' version of H. Our main result is an approximation algorithm with ratio \frac{t simple %DIFAUXCMD hypergraph with hyperedges intersecting in at most one vertex .
Edit intent: clarity
Original:  The blown-up hypergraph further has many forbidden structures, including a ``tent'' structure for the case t=3. Whether a generalization of Tuza's conjecture could also hold for tent-free 3-uniform hypergraphswas posed in a recent work. We answer this question in the negative by giving a construction based on combinatorial lines that is tent-free, and yet needs to include most of the vertices in a vertex cover .
Revised:  However, for set cover on simple n-vertex hypergraphs, the greedy algorithm achieves a factor (\ln n)/2, better than the optimal \ln n factor for general hypergraphs .
Edit intent: meaning-changed
Original:  Currently, near 15000 SARS-CoV-2 single mutations have been recorded, having a great ramification to the development of diagnostics, vaccines, antibody therapies, and drugs.
Revised:  Currently, near 15,000 SARS-CoV-2 single mutations have been recorded, having a great ramification to the development of diagnostics, vaccines, antibody therapies, and drugs.
Edit intent: fluency