wanyu/IteraTeR_full_sent huggingface dataset:
Original:  And, by carrying out numerical simulation, we will show that the progressive tax-like norm function (i) does not increase disparity for the distribution of the actions, unlike the other norm functions, and (ii) has high resource productivity and low Gini coefficient , i. e., the progressive tax-like norm function has the highest sustainability and fairness.
Revised:  And, by carrying out numerical simulation, we will show that the progressive tax-like norm function (i) does not increase disparity for the distribution of the actions, unlike the other norm functions, and (ii) has high resource productivity and low Gini coefficient . Therefore the progressive tax-like norm function has the highest sustainability and fairness.
Edit intent: coherence
Original:  We also analytically characterize the asymptotic marginal propensities to consume (MPCs) out of wealth and derive necessary and sufficient conditions under which they are 0, 1, or are somewhere in between. When the return process with time-varying volatility is calibrated from data, the asymptotic MPCs can be zero with moderate risk aversion. Our results potentially explain why the saving rates among the rich are positive and increasing in wealth .
Revised:  We also analytically characterize the asymptotic marginal propensities to consume (MPCs) out of wealth . Our results are useful for obtaining good initial guesses when numerically computing consumption functions, and provide a theoretical justification for linearly extrapolating consumption functions outside the grid .
Edit intent: coherence
Original: Playing text-based games requires skill in processing natural language and in planning. Although a key goal for agents solving this task is to generalize across multiple games , most previous work has either focused on solving a single game or has tackled generalization with rule-based heuristics.
Revised: Playing text-based games requires skill in processing natural language and sequential decision making. Achieving human-level performance on text-based games remains an open challenge, and prior research has largely relied on hand-crafted structured representations and heuristics.
Edit intent: clarity
Original:  In this work, we investigate how structured information in the form of a knowledge graph (KG) can facilitate effective planning and generalization. We introduce a novel transformer-based sequence-to-sequence model that constructs a "belief " KG from raw text observations of the environment, dynamically updating this belief graph at every game step as it receives new observations. To train this model to build useful graph representations, we introduce and analyze a set of graph-related pre-training tasks. We demonstrate empirically that KG-based representations from our model help agents to converge faster to better policies for multiple text-based games, and further, enable stronger zero-shot performance on unseen games . Experiments on unseen games show that our best agent outperforms text-based baselines by 21.6 \%.
Revised:  In this work, we investigate how an agent can plan and generalize in text-based games using graph-structured representations learned end-to-end from raw text. We propose a novel graph-aided transformer agent (GATA) that infers and updates latent belief graphs during planning to enable effective action selection by capturing the underlying game dynamics. GATA is trained using a combination of reinforcement and self-supervised learning. Our work demonstrates that the learned graph-based representations help agents converge to better policies for multiple text-based games, and further, enable stronger zero-shot performance on unseen games . Experiments on unseen games show that our best agent outperforms text-based baselines by 21.6 \%.
Edit intent: clarity
Original:  We demonstrate empirically that KG-based representations from our model help agents to converge faster to better policies for multiple text-based games, and further, enable stronger zero-shot performance on unseen games . Experiments on unseen games show that our best agent outperforms text-based baselines by 21.6 \%.
Revised:  We demonstrate empirically that KG-based representations from our model help agents to converge faster to better policies than their text-only counterparts and facilitate effective generalization across game configurations . Experiments on unseen games show that our best agent outperforms text-based baselines by 21.6 \%.
Edit intent: clarity
Original:  We demonstrate empirically that KG-based representations from our model help agents to converge faster to better policies for multiple text-based games, and further, enable stronger zero-shot performance on unseen games . Experiments on unseen games show that our best agent outperforms text-based baselines by 21.6 \%.
Revised:  We demonstrate empirically that KG-based representations from our model help agents to converge faster to better policies for multiple text-based games, and further, enable stronger zero-shot performance on unseen games . Experiments on 500+ unique games from the TextWorld suite show that our best agent outperforms text-based baselines by 21.6 \%.
Edit intent: meaning-changed
Original:  We demonstrate empirically that KG-based representations from our model help agents to converge faster to better policies for multiple text-based games, and further, enable stronger zero-shot performance on unseen games . Experiments on unseen games show that our best agent outperforms text-based baselines by 21.6 \%.
Revised:  We demonstrate empirically that KG-based representations from our model help agents to converge faster to better policies for multiple text-based games, and further, enable stronger zero-shot performance on unseen games . Experiments on unseen games show that our best agent outperforms text-based baselines by an average of 24.2 \%.
Edit intent: meaning-changed
Original:  In this work, we examine two fundamental variants of submodular maximization in the adaptive complexity model: cardinality-constrained monotone maximization, and unconstrained non-monotone maximization.
Revised:  In this work, we examine two fundamental variants of submodular maximization in the adaptive complexity model: cardinality-constrained monotone maximization, and unconstrained non-mono-tone maximization.
Edit intent: fluency
Original:  Our main result is that an r-round algorithm for cardinality-constrained monotone maximization cannot achieve a factor better than 1 - 1/e - \Omega(\min \{ 1{r}, \log^2 n{r^3} \}), for any r < n^c (where c>0 is some constant).
Revised:  Our main result is that an r-round algorithm for cardinality-constrained monotone maximization cannot achieve an approximation factor better than 1 - 1/e - \Omega(\min \{ 1{r}, \log^2 n{r^3} \}), for any r < n^c (where c>0 is some constant).
Edit intent: clarity
Original:  In particular (  in contrast to the cardinality-constrained case), there cannot be an instance where (i) it is impossible to achieve a factor better than 1/2 regardless of the number of rounds, and (ii) it takes r rounds to achieve a factor of 1/2-O(1/r).
Revised:  In particular ( and in contrast to the cardinality-constrained case), there cannot be an instance where (i) it is impossible to achieve a factor better than 1/2 regardless of the number of rounds, and (ii) it takes r rounds to achieve a factor of 1/2-O(1/r).
Edit intent: fluency
Original:  In particular (  in contrast to the cardinality-constrained case), there cannot be an instance where (i) it is impossible to achieve a factor better than 1/2 regardless of the number of rounds, and (ii) it takes r rounds to achieve a factor of 1/2-O(1/r).
Revised:  In particular ( in contrast to the cardinality-constrained case), there cannot be an instance where (i) it is impossible to achieve an approximation factor better than 1/2 regardless of the number of rounds, and (ii) it takes r rounds to achieve a factor of 1/2-O(1/r).
Edit intent: meaning-changed
Original:  In this paper, we propose a new evaluation scheme for link prediction algorithms, i.e., link prediction with random sampling . We use this method to evaluate the performance of twelve link predictors on ten real-world networks of different contexts and scales .
Revised:  In this paper, we propose a new evaluation paradigm that is sampling unobserved links to address this problem. First, we demonstrate that the proposed paradigm is feasible in theory. Then, we perform extensive evaluation experiments in real-world networks of different contexts and scales .
Edit intent: coherence
Original:  The results show that the performance ranking of these algorithms is not affected by randomly sampling a very small part from unobserved links for experiments, whether using AUC or the precision metric. Moreover, this sampling method can reduce the computational complexity for the evaluation of link prediction algorithms from O(n^2) to O(n) in large networks  .
Revised:  The results show that the performance of similarity-based link prediction algorithms is highly stable even at a low sampling ratio in large networks .
Edit intent: clarity
Original:  Hereby, we examine the data in China (01/20/2020--02/ 19 /2020), and indeed find that the growth closely follows a power-law kinetics over a significantly wide time period.
Revised:  Hereby, we examine the data in China (01/20/2020--02/ 24 /2020), and indeed find that the growth closely follows a power-law kinetics over a significantly wide time period.
Edit intent: meaning-changed
Original:  The exponents are 2.42 (20), 2.21( 8) and 4.14(20 ) for the number of confirmed infections, deaths and cured cases, respectively, indicating an underlying small-world network structure in the pandemic.
Revised:  The exponents are 2.48 (20), 2.21( 8) and 4.14(20 ) for the number of confirmed infections, deaths and cured cases, respectively, indicating an underlying small-world network structure in the pandemic.
Edit intent: meaning-changed
Original:  The exponents are 2.42 (20), 2.21( 8) and 4.14(20 ) for the number of confirmed infections, deaths and cured cases, respectively, indicating an underlying small-world network structure in the pandemic.
Revised:  The exponents are 2.42 (20), 2.21( 6) and 4.26(12 ) for the number of confirmed infections, deaths and cured cases, respectively, indicating an underlying small-world network structure in the pandemic.
Edit intent: meaning-changed
Original:  Further, by analyzing the kinetics of the mortality in log-log scale, we obtains a rough estimate that near March 3, the death rate of COVID-19 would be about 4.0 \\%DIF < \thicksim 4.5\\% for Hubei province and 0.8\\%\thicksim1.
Revised:  Further, by analyzing the kinetics of the mortality in log-log scale, we obtains a rough estimate that near March 3, the death rate of COVID-19 would be about 4.7 \\%DIF < \thicksim 4.5\\% for Hubei province and 0.8\\%\thicksim1.
Edit intent: meaning-changed
Original:  Our tests are built on conditional moment embeddings (CMME)---a novel representation of conditional moment restrictions in a reproducing kernel Hilbert space (RKHS)  . After transforming the conditional moment restrictions into a continuum of unconditional counterparts, the test statistic is defined as the maximum moment restriction  within the unit ball of the RKHS.
Revised:  Our tests are built on a novel representation of conditional moment restrictions in a reproducing kernel Hilbert space (RKHS) . After transforming the conditional moment restrictions into a continuum of unconditional counterparts, the test statistic is defined as the maximum moment restriction within the unit ball of the RKHS.
Edit intent: clarity
Original:  Our tests are built on conditional moment embeddings (CMME)---a novel representation of conditional moment restrictions in a reproducing kernel Hilbert space (RKHS)  . After transforming the conditional moment restrictions into a continuum of unconditional counterparts, the test statistic is defined as the maximum moment restriction  within the unit ball of the RKHS.
Revised:  Our tests are built on conditional moment embeddings (CMME)---a novel representation of conditional moment restrictions in a reproducing kernel Hilbert space (RKHS) called conditional moment embedding (CMME) . After transforming the conditional moment restrictions into a continuum of unconditional counterparts, the test statistic is defined as the maximum moment restriction within the unit ball of the RKHS.
Edit intent: meaning-changed
Original:  Our tests are built on conditional moment embeddings (CMME)---a novel representation of conditional moment restrictions in a reproducing kernel Hilbert space (RKHS)  . After transforming the conditional moment restrictions into a continuum of unconditional counterparts, the test statistic is defined as the maximum moment restriction  within the unit ball of the RKHS.
Revised:  Our tests are built on conditional moment embeddings (CMME)---a novel representation of conditional moment restrictions in a reproducing kernel Hilbert space (RKHS) . After transforming the conditional moment restrictions into a continuum of unconditional counterparts, the test statistic is defined as the maximum moment restriction (MMR) within the unit ball of the RKHS.
Edit intent: meaning-changed
Original:  We show that the CMME fully characterizes the original conditional moment restrictions, leading to consistency in both hypothesis testing and parameter estimation . The proposed test also has an analytic expression that is easy to compute as well as closed-form asymptotic distributions.
Revised:  We show that the CMME fully characterizes the original conditional moment restrictions, leading to consistency in both hypothesis testing and parameter estimation , but also has an analytic expression that is easy to compute as well as closed-form asymptotic distributions.
Edit intent: coherence
Original:  Just like humans, our agent uses language compositionality to generate new goals by composing known ones  . Leveraging modular model architectures based on Deep Sets and gated-attention mechanisms, IMAGINE autonomously builds a repertoire of behaviors and shows good zero-shot generalization properties for various types of generalization.
Revised:  Just like humans, our agent uses language compositionality to generate new goals by composing known ones , using an algorithm grounded in construction grammar models of child language acquisition . Leveraging modular model architectures based on Deep Sets and gated-attention mechanisms, IMAGINE autonomously builds a repertoire of behaviors and shows good zero-shot generalization properties for various types of generalization.
Edit intent: meaning-changed
Original:  Just like humans, our agent uses language compositionality to generate new goals by composing known ones  . Leveraging modular model architectures based on Deep Sets and gated-attention mechanisms, IMAGINE autonomously builds a repertoire of behaviors and shows good zero-shot generalization properties for various types of generalization.
Revised:  Just like humans, our agent uses language compositionality to generate new goals by composing known ones . Leveraging modular model architectures based on deepsets and gated attention mechanisms, IMAGINE autonomously builds a repertoire of behaviors and shows good zero-shot generalization properties for various types of generalization.
Edit intent: fluency
Original:  As children do the same, they benefit from exposure to language, using it to formulate goals and imagine new ones as they learn their meaning. In our proposed learning architecture (IMAGINE), the agent freely explores its environment and turns natural language descriptions of interesting interactions from a social partner into potential goals. IMAGINE learns to represent goalsby jointly learning a language encoder and a goal-conditioned reward function . Just like humans, our agent uses language compositionality to generate new goals by composing known ones, using an algorithm grounded in construction grammar models of child language acquisition. Leveraging modular model architectures based on deepsets and gated attention mechanisms, IMAGINE autonomously builds a repertoire of behaviors and shows good zero-shot generalization properties for various types of generalization. When imagining its own goals, the agent leverages zero-shot generalization of the reward function to further train on imagined goals and refine its behavior. We present experiments in a simulated domain where the agent interacts with procedurally generated scenes containing objects of various types and colors, discovers goals, imagines others and learns to achieve them .
Revised:  Recent approaches have considered goal spaces that were either fixed and hand-defined or learned using generative models of states. This limited agents to sample goals within the distribution of known effects. We argue that the ability to imagine out-of-distribution goals is key to enable creative discoveries and open-ended learning. Children do so by leveraging the compositionality of language as a tool to imagine descriptions of outcomes they never experienced before, targeting them as goals during play. We introduce Imagine, an intrinsically motivated deep reinforcement learning architecture that models this ability. Such imaginative agents, like children, benefit from the guidance of a social peer who provides language descriptions. To take advantage of goal imagination, agents must be able to leverage these descriptions to interpret their imagined out-of-distribution goals. This generalization is made possible by modularity: a decomposition between learned goal-achievement reward function and policy relying on deep sets, gated attention and object-centered representations. We introduce the Playground environment and study how this form of goal imagination improves generalization and exploration over agents lacking this capacity. In addition, we identify the properties of goal imagination that enable these results and study the impacts of modularity and social interactions .
Edit intent: meaning-changed
Original: To gain insights into the regional inequality problem , we proposed new regional asset exchange models based on existing kinetic income-exchange models in economic physics by setting spatial exchange range and adding bias to asset fraction probability in equivalent exchanges.
Revised: To gain insights into the regional inequality problem , we proposed new regional asset exchange models based on existing kinetic income-exchange models in economic physics . We did this by setting the spatial exchange range and adding bias to asset fraction probability in equivalent exchanges.
Edit intent: coherence
Original:  Simulations of asset distribution and Gini coefficients showed that suppressing regional inequality requires, first, increasing the intra-regional economic circulation rate, and , second, narrowing down the exchange range (inter-regional economic zone).
Revised:  Simulations of asset distribution and Gini coefficients showed that suppressing regional inequality requires, firstly an increase in the intra-regional economic circulation rate, and , second, narrowing down the exchange range (inter-regional economic zone).
Edit intent: clarity
Original:  Simulations of asset distribution and Gini coefficients showed that suppressing regional inequality requires, first, increasing the intra-regional economic circulation rate, and , second, narrowing down the exchange range (inter-regional economic zone).
Revised:  Simulations of asset distribution and Gini coefficients showed that suppressing regional inequality requires, first, increasing the intra-regional economic circulation rate, and secondly the narrowing down of the exchange range (inter-regional economic zone).
Edit intent: clarity
Original:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution , eventually approaching the normal distribution and further reducing the Gini coefficient  . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Revised:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from over-concentration to exponential distribution , eventually approaching the normal distribution and further reducing the Gini coefficient . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Edit intent: fluency
Original:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution , eventually approaching the normal distribution and further reducing the Gini coefficient  . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Revised:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution and eventually approaching the normal distribution and further reducing the Gini coefficient . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Edit intent: fluency
Original:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution , eventually approaching the normal distribution and further reducing the Gini coefficient  . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Revised:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution , eventually approaching the normal distribution , reducing the Gini coefficient . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Edit intent: coherence
Original:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution , eventually approaching the normal distribution and further reducing the Gini coefficient  . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Revised:  A comprehensive solution incorporating these three measures enabled shifting the asset distribution from overconcentration to exponential distribution , eventually approaching the normal distribution and further reducing the Gini coefficient further . Going forward, we will further expand the models by setting production capacity based on assets, path dependency on two-dimensional space, and bias according to disparity, and verify measures to reduce regional inequality in actual communities.
Edit intent: clarity