wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Specifically, on an input video sequence, STSGR (i) creates a two-stream visual and semantic scene graph on every frame, (ii) conducts intra-graph reasoning using node and edge convolutions generating visual memories, and (iii) applies inter-graph aggregation to capture their temporal evolutions. These visual memories are then combined with other modalities and the question embeddings using a novel semantics-controlled multi-head shuffled transformer, which then produces the answer recursively.
Revised:  Specifically, on an input video sequence, STSGR (i) creates a two-stream visual and semantic scene graph on every frame, and an inter-frame aggregation module capturing temporal cues.
Edit intent: clarity
Original:  We  generate questions,  SPARQLs, and functional programs with recursive templates and then paraphrase the questions by crowdsourcing, giving rise to around 120K diverse instances.
Revised:  We define a compositional and highly-interpretable formal format, named Program, to represent the reasoning process of complex questions. We propose compositional strategies to generate questions, SPARQLs, and functional programs with recursive templates and then paraphrase the questions by crowdsourcing, giving rise to around 120K diverse instances.
Edit intent: meaning-changed
Original:  We  generate questions,  SPARQLs, and functional programs with recursive templates and then paraphrase the questions by crowdsourcing, giving rise to around 120K diverse instances.
Revised:  We generate questions, SPARQLs, and functional programs with recursive templates and then paraphrase the generated questions to natural language questions (NLQ) by crowdsourcing, giving rise to around 120K diverse instances.
Edit intent: meaning-changed
Original:  We contribute a unified codebase and conduct extensive evaluations for baselines and state-of-the-arts: a blind GRU obtains 31.58 \\%DIF < , the best model achieves only 35.15\\%, and humans top at 97.5\\%, which offers great research potential to fill the gap.abstract %DIF > answering accuracy, which is even close to the human expert performance...
Revised:  Besides the QA task, KQA Pro can also serves for the semantic parsing task. As far as we know, it is currently the largest corpus of NLQ-to-SPARQL and NLQ-to-Program. We conduct extensive experiments to evaluate whether machines can learn to answer our complex questions in different cases, that is, with only QA supervision or with intermediate SPARQL/Program supervision. We find that state-of-the-art KBQA methods learnt from only QA pairs perform very poor on our dataset, implying our questions are more challenging than previous datasets. However, pretrained models learnt from our NLQ-to-SPARQL and NLQ-to-Program annotations surprisingly achieve about 90 \\%DIF < , the best model achieves only 35.15\\%, and humans top at 97.5\\%, which offers great research potential to fill the gap.abstract %DIF > answering accuracy, which is even close to the human expert performance...
Edit intent: meaning-changed
Original:  A vertex v \in V is a twinless strong articulation point of G , if the deletion of v increases the number of TSCCs of G. Here, we present the first linear-time algorithm that finds all the twinless strong articulation points of a directed graph.
Revised:  A vertex v \in V is a twinless strong articulation point of G if the deletion of v increases the number of TSCCs of G. Here, we present the first linear-time algorithm that finds all the twinless strong articulation points of a directed graph.
Edit intent: fluency
Original:  We show that the computation of twinless strong articulation points reduces to the following problem in undirected graphs, which may be of independent interest: Given a 2-vertex-connected (biconnected) undirected graph H, find all vertices v that belong to a vertex-edge cut pair , i.e., for which there exists an edge e such that H \setminus \{v,e\} is not connected.
Revised:  We show that the computation of twinless strong articulation points reduces to the following problem in undirected graphs, which may be of independent interest: Given a 2-vertex-connected (biconnected) undirected graph H, find all vertices v that belong to a vertex-edge cut-pair , i.e., for which there exists an edge e such that H \setminus \{v,e\} is not connected.
Edit intent: fluency
Original:  This also implies that for each twinless strong articulation point v , that is not a strong articulation point in a strongly connected digraph G, we can compute the number of TSCCs in G \setminus v. We note that the problem of computing all vertices that belong to a vertex-edge cut pair can be solved in linear-time by exploiting the structure of 3-vertex connected (triconnected)
Revised:  This also implies that for each twinless strong articulation point v which is not a strong articulation point in a strongly connected digraph G, we can compute the number of TSCCs in G \setminus v. We note that the problem of computing all vertices that belong to a vertex-edge cut pair can be solved in linear-time by exploiting the structure of 3-vertex connected (triconnected)
Edit intent: clarity
Original:  This also implies that for each twinless strong articulation point v , that is not a strong articulation point in a strongly connected digraph G, we can compute the number of TSCCs in G \setminus v. We note that the problem of computing all vertices that belong to a vertex-edge cut pair can be solved in linear-time by exploiting the structure of 3-vertex connected (triconnected)
Revised:  This also implies that for each twinless strong articulation point v , that is not a strong articulation point in a strongly connected digraph G, we can compute the number of TSCCs in G \setminus v. We note that the problem of computing all vertices that belong to a vertex-edge cut-pair can be solved in linear-time by exploiting the structure of 3-vertex connected (triconnected)
Edit intent: fluency
Original:  This also implies that for each twinless strong articulation point v , that is not a strong articulation point in a strongly connected digraph G, we can compute the number of TSCCs in G \setminus v. We note that the problem of computing all vertices that belong to a vertex-edge cut pair can be solved in linear-time by exploiting the structure of 3-vertex connected (triconnected)
Revised:  This also implies that for each twinless strong articulation point v , that is not a strong articulation point in a strongly connected digraph G, we can compute the number of TSCCs in G \setminus v. We note that the problem of computing all vertices that belong to a vertex-edge cut pair can be solved in linear-time by exploiting the structure of 3-vertex-connected (triconnected)
Edit intent: fluency
Original:  We propose a theoretically-motivated class of default priors on a non-parametric nuisance parameter. These priors imply computationally tractable Bayes decision rules in the limit problem, while leaving the prior on the structural parameter free to be selected by the researcher .
Revised:  We propose a theoretically-motivated class of priors which give rise to quasi-Bayes decision rules as a limiting case. Together with results in the previous literature, this establishes desirable properties for the quasi-Bayes approach regardless of model identification status .
Edit intent: clarity
Original:  We further obtain quasi-Bayes decision rules as the limit of sequences in this class, and derive weighted average power-optimal identification-robust frequentist tests . Finally, we prove a Bernstein-von Mises-type result for the quasi-Bayes posterior under weak and partial identification.
Revised:  We further obtain quasi-Bayes decision rules as the limit of sequences in this class, and derive weighted average power-optimal identification-robust frequentist tests and confidence sets, and prove a Bernstein-von Mises-type result for the quasi-Bayes posterior under weak and partial identification.
Edit intent: meaning-changed
Original:  In the second part of the paper we study the equilibrium of the social distancing game and we show that voluntary social distancing will always be socially sub-optimal .
Revised:  In the second part of the paper we study the equilibrium of the social distancing game , in which individuals choose their social distancing level according to an anticipated global infection rate, which then must equal the actual infection rate following their choices. We give conditions for the existence and uniqueness of equilibrium. For the case of random regular graphs, we show that voluntary social distancing will always be socially sub-optimal .
Edit intent: meaning-changed
Original:  In the second part of the paper we study the equilibrium of the social distancing game and we show that voluntary social distancing will always be socially sub-optimal . Our numerical study using Covid-19 data serves to quantify the absolute and relative utility gaps across age cohorts .
Revised:  In the second part of the paper we study the equilibrium of the social distancing game and we show that voluntary social distancing will always be socially sub-optimal .
Edit intent: coherence
Original:  We qualitatively discuss some conditions, beyond the scope of simple models, under which a lockdown might increase or decrease the epidemic's final toll in the real world. We conclude that SEIR models and their generalizations provide no reliable quantitative evidence that the Indian lockdown has averted any deaths from the COVID-19 pandemic.
Revised:  We qualitatively discuss some conditions, beyond the scope of simple models, under which a lockdown might increase or decrease the epidemic's final toll . Given the significance of these effects in India, and the limitations of currently available data, we conclude that simple epidemiological models cannot be used to reliably quantify the impact of the Indian lockdown on fatalities caused by the COVID-19 pandemic.
Edit intent: meaning-changed
Original: In order to interact with objects in our environment, we rely on an understanding of the actions that can be performed on them, and the extent to which they rely or have an effect on the propertiesof the object.
Revised: In order to interact with objects in our environment, humans rely on an understanding of the actions that can be performed on them, and the extent to which they rely or have an effect on the propertiesof the object.
Edit intent: clarity
Original: In order to interact with objects in our environment, we rely on an understanding of the actions that can be performed on them, and the extent to which they rely or have an effect on the propertiesof the object. This knowledge is called the object "affordance".
Revised: In order to interact with objects in our environment, we rely on an understanding of the actions that can be performed on them, as well as their properties. When considering concrete motor actions, this knowledge has been called the object "affordance".
Edit intent: meaning-changed
Original:  This knowledge is called the object "affordance". We propose an approach for creating an embedding of objects in an affordance space , in which each dimension corresponds to an aspect of meaning shared by many actions, using text corpora.
Revised:  This knowledge is called the object affordance. Can this notion be generalized to any type of interaction that one can have with an object? In this paper we introduce a method to represent objects in a space where each dimension corresponds to an aspect of meaning shared by many actions, using text corpora.
Edit intent: meaning-changed
Original:  This  embedding makes it possible to predict which verbs will be applicable to a given object, as captured in human judgments of affordance. We show that the dimensions learned are interpretable, and that they correspond to patterns of interaction with objects . Finally , we show that they can be used to predict other dimensions of object representation that have been shown to underpin human judgments of object similarity  .
Revised:  This embedding makes it possible to predict which verbs will be applicable to a given object, as captured in human judgments of verb applicability to objects better than a variety of alternative approaches. Furthermore , we show that they can be used to predict other dimensions of object representation that have been shown to underpin human judgments of object similarity .
Edit intent: meaning-changed
Original:  We show that under parallel trends conditions, these parameters can be unbiasedly estimated by a weighted average of differences-in-differences, provided that at least one group is always untreated, and another group is always treated.
Revised:  We show that under common trends conditions, these parameters can be unbiasedly estimated by a weighted average of differences-in-differences, provided that at least one group is always untreated, and another group is always treated.
Edit intent: meaning-changed
Original:  We show that under parallel trends conditions, these parameters can be unbiasedly estimated by a weighted average of differences-in-differences, provided that at least one group is always untreated, and another group is always treated.
Revised:  We show that under parallel trends conditions, these parameters can be unbiasedly estimated by weighted sums of differences-in-differences, provided that at least one group is always untreated, and another group is always treated.
Edit intent: meaning-changed
Original:  We focus on parameters aggregating instantaneous and dynamic treatment effects, as a way to evaluate the welfare effects of the policies that occurred over the duration of the  panel.
Revised:  We consider two sets of parameters of interest. The first are the average effects of having changed treatment for the first time \ell periods ago. Those parameters generalize the average effect of having started receiving the treatment \ell periods ago that has often been estimated in applications with a binary treatment and staggered adoption. We also consider cost-benefit ratios a planner may use to compare the treatments actually assigned to a counterfactual panel.
Edit intent: meaning-changed
Original:  We show that under common trends conditions, these parameters can be unbiasedly estimated by weighted sums of differences-in-differences , provided that at least one group is always untreated, and another group is always treated .
Revised:  We show that under common trends conditions, all these parameters are unbiasedly estimated by weighted sums of differences-in-differences , provided that at least one group is always untreated, and another group is always treated .
Edit intent: clarity
Original:  We also propose estimators of a dynamic linear model, with group-specific but time-invariant effects of the current and lagged treatments, which may be used to evaluate ex-ante the effect of future policies  .
Revised:  We also propose estimators of a dynamic linear model, with group-specific but time-invariant effects of the current and lagged treatments, which may be used to evaluate ex-ante the effect of future policies . In an application, we find that our estimators differ substantially from those obtained with dynamic two-way fixed effects regressions .
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Original: Visualization techniques have proven to be useful tools to support decision making and to cope with increasing amount of data. In order to help analyzing the progressionof number of COVID-19 cases, we propose a new visual analytic tool .
Revised: Visualization techniques have proven to be useful tools to support decision making and to cope with increasing data, which is particularly important when monitoring natural or artificial phenomena. When monitoring disease progression, visual analytics approaches help decision-makers choose to understand or even prevent dissemination paths. In this paper, we propose a new visual analytic tool .
Edit intent: meaning-changed
Original:  We use k-nearest neighbors of cities to mimic regions and allow analysis of COVID-19 dissemination based on the comparison of a city under consideration and its neighborhood , moreover, such analysis is performed based on periods of time , which facilitates  assessment of isolation policies.
Revised:  We use k-nearest neighbors of cities to mimic neighboring cities and analyze COVID-19 dissemination based on the comparison of a city under consideration and its neighborhood , moreover, such analysis is performed based on periods of time , which facilitates assessment of isolation policies.
Edit intent: clarity
Original:  We use k-nearest neighbors of cities to mimic regions and allow analysis of COVID-19 dissemination based on the comparison of a city under consideration and its neighborhood , moreover, such analysis is performed based on periods of time , which facilitates  assessment of isolation policies.
Revised:  We use k-nearest neighbors of cities to mimic regions and allow analysis of COVID-19 dissemination based on the comparison of a city under consideration and its neighborhood . Moreover, such analysis is performed based on periods of time , which facilitates assessment of isolation policies.
Edit intent: fluency
Original:  We use k-nearest neighbors of cities to mimic regions and allow analysis of COVID-19 dissemination based on the comparison of a city under consideration and its neighborhood , moreover, such analysis is performed based on periods of time , which facilitates  assessment of isolation policies.
Revised:  We use k-nearest neighbors of cities to mimic regions and allow analysis of COVID-19 dissemination based on the comparison of a city under consideration and its neighborhood , moreover, such analysis is performed based on periods , which facilitates assessment of isolation policies.
Edit intent: clarity
Original:  We use k-nearest neighbors of cities to mimic regions and allow analysis of COVID-19 dissemination based on the comparison of a city under consideration and its neighborhood , moreover, such analysis is performed based on periods of time , which facilitates  assessment of isolation policies.
Revised:  We use k-nearest neighbors of cities to mimic regions and allow analysis of COVID-19 dissemination based on the comparison of a city under consideration and its neighborhood , moreover, such analysis is performed based on periods of time , which facilitates the assessment of isolation policies.
Edit intent: fluency
Original:  The coordinate transformation is performed on the semi-discretized level which makes them applicable to a wide range of kinetic schemes and replaces the nonlinear optimization problems by inversion of the positive-definite Hessian matrix.
Revised:  The transformation is performed on the semi-discretized level which makes them applicable to a wide range of kinetic schemes and replaces the nonlinear optimization problems by inversion of the positive-definite Hessian matrix.
Edit intent: clarity
Original:  Word embedding models overcome this problem by constructing a standardized meaning space where words are assigned a location based on relations of similarity to , and difference from, other words based on how they are used in natural language samples.
Revised:  Word embedding models overcome this problem by constructing a standardized and continuous "meaning-space" where words are assigned a location based on relations of similarity to , and difference from, other words based on how they are used in natural language samples.
Edit intent: clarity
Original:  Word embedding models overcome this problem by constructing a standardized and continuous " meaning-space " where words are assigned a location based on relations of similarity to other words based on how they are used in natural language samples.
Revised:  Word embedding models overcome this problem by constructing a standardized and continuous " meaning space " where words are assigned a location based on relations of similarity to other words based on how they are used in natural language samples.
Edit intent: fluency
Original: Ideal point estimation and dimensionality reduction have long been utilized to simplify and cluster complex, high-dimensional political data(e. g., roll-call votes and surveys) for use in analysis and visualization. These methods often work by finding the directions or principal components (PCs) on which either the data varies the most or respondents make the fewest decision errors. However, these PCs, which usually reflect the left-right political spectrum, are sometimes uninformative in explaining significant differences in the distribution of the data(e. g., how to categorize a set of highly-moderate voters).
Revised: Ideal point estimation and dimensionality reduction have long been utilized to simplify and cluster complex, high-dimensional data. However, the latent spaces derived from these methods are sometimes uninformative in explaining significant differences in the distribution of the data(e. g., how to categorize a set of highly-moderate voters).
Edit intent: clarity
Original:  However, these PCs, which usually reflect the left-right political spectrum, are sometimes uninformative in explaining significant differences in the distribution of the data(e. g., how to categorize a set of highly-moderate voters).
Revised:  However, these PCs, which usually reflect the left-right political spectrum, are sometimes uninformative or unable to identify significant differences in the distribution of the data(e. g., how to categorize a set of highly-moderate voters).
Edit intent: clarity
Original:  Contrastive learning-e.g., contrastive principal component analysis (cPCA)-works by first splitting the data by predefined groups, and then deriving PCs on which the target group varies the most but the background group varies the least. As a result, cPCA can often find `hidden' patterns, such as subgroups within the target group, which PCA cannot reveal when some variables are the dominant source of variations across the groups.
Edit intent: coherence
Original:  We contribute to the field of contrastive learning by extending it to multiple correspondence analysis (MCA)  to enable an analysis of data often encountered by social scientists---namely binary, ordinal, and nominal variables.
Revised:  We contribute to the field of contrastive learning by extending it to multiple correspondence analysis (MCA) in order to enable an analysis of data often encountered by social scientists---namely binary, ordinal, and nominal variables.
Edit intent: coherence
Original:  We contribute to the field of contrastive learning by extending it to multiple correspondence analysis (MCA)  to enable an analysis of data often encountered by social scientists---namely binary, ordinal, and nominal variables.
Revised:  We contribute to the field of contrastive learning by extending it to multiple correspondence analysis (MCA) to enable an analysis of data often encountered by social scientists -- namely binary, ordinal, and nominal variables.
Edit intent: fluency
Original:  second, for the caseswhen MCA does not show clear differences , cMCA can successfully identify meaningful directions and subgroups, which traditional methods overlook .
Revised:  second, for the caseswhen MCA does not show clear differences , cMCA can still derive latent traits that generalize across and apply to multiple groups in the dataset; finally, when data is high-dimensional and unstructured, cMCA provides objective heuristics, above and beyond the standard results, enabling more complex subgroup analysis .
Edit intent: meaning-changed
Original: This paper proposes a multiscale method for solving the numerical solution of mean field games  . Starting from an approximate solution at the coarsest level, the method constructs approximations on successively finer grids via alternating sweeping  .
Revised: This paper proposes a multiscale method for solving the numerical solution of mean field games which accelerates the convergence and addresses the problem of determining the initial guess . Starting from an approximate solution at the coarsest level, the method constructs approximations on successively finer grids via alternating sweeping .
Edit intent: meaning-changed