wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning , we analyze this problem from a novel cognition perspective: learning a hierarchical cognitive structure of the highly-biased relationships and navigating that hierarchy to locate the classes , making the tail classes receive more attention in a coarse-to-fine mode.
Revised:  Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning , we analyze this problem from a novel cognition perspective: learning a hierarchical cognitive structure of the highly-biased relationships and navigating that hierarchy to locate the relationships , making the tail classes receive more attention in a coarse-to-fine mode.
Edit intent: fluency
Original:  Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning , we analyze this problem from a novel cognition perspective: learning a hierarchical cognitive structure of the highly-biased relationships and navigating that hierarchy to locate the classes , making the tail classes receive more attention in a coarse-to-fine mode.
Revised:  Inspired by the role of the prefrontal cortex (PFC) in hierarchical reasoning , we analyze this problem from a novel cognition perspective: learning a hierarchical cognitive structure of the highly-biased relationships and navigating that hierarchy to locate the classes , making the tail relationships receive more attention in a coarse-to-fine mode.
Edit intent: fluency
Original:  To this end, we propose a novel  Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree URLanize the relationships based on the prediction of a biased SGG model.
Revised:  To this end, we propose a novel debiasing Cognition Tree (CogTree) loss for unbiased SGG. We first build a cognitive structure CogTree URLanize the relationships based on the prediction of a biased SGG model.
Edit intent: meaning-changed
Original:  We extend perturbation results for the singular subspace decomposition to derive the first-order perturbation expansion of the truncated operator about a matrix with rank no less than r. Observing that the first-order expansion can be greatly simplified when the matrix has exact rank r, we further show that the singular value truncation admits a simple second-order perturbation expansion about a rank-r matrix.
Revised:  We extend perturbation results for the singular subspace decomposition to derive the first-order perturbation expansion of the truncated operator about a matrix with rank greater than or equal to r. Observing that the first-order expansion can be greatly simplified when the matrix has exact rank r, we further show that the singular value truncation admits a simple second-order perturbation expansion about a rank-r matrix.
Edit intent: meaning-changed
Original:  Intriguingly, while the singular subspaces are known to be extremely sensitive to additive noises, the proposed error bound holds universally for perturbations with arbitrary magnitude  .
Revised:  Intriguingly, while the singular subspaces are known to be extremely sensitive to additive noises, the proposed error bound holds universally for perturbations with arbitrary magnitude . Finally, we demonstrate an application of our results to the analysis of the mean squared error associated with the TSVD-based matrix denoising solution .
Edit intent: meaning-changed
Original: Regression discontinuity designs (RDDs) may not deliver reliable results if unitsmanipulate their running variables. It is commonly believed that imprecise manipulations are harmless and, diagnostic tests detect precise manipulations. However, we demonstrate that RDDs may fail to point-identify in the presence of imprecise manipulation, and that not all harmful manipulations are detectable. To formalize these claims, we propose a class of RDDs with harmless or detectable manipulations over locally randomized running variables as manipulation-robust RDDs. The conditions for the manipulation-robust RDDs may be intuitively verified using the institutional background. We demonstrate its verification process in case studies of applications that use the McCrary (2008) density test. The restrictions of manipulation-robust RDDs generate partial identification results that are robust to possible manipulation . We apply the partial identification result to a controversy regarding the incumbency margin study of the U.S. House of Representatives elections .
Revised: Regression discontinuity (RD) design in a practical context is often contaminated by units' behavior to manipulate their treatment assignment. However, we have no formal justification for point identification in such a contaminated RD design. Diagnostic tests have been proposed to detect manipulations, but they do not guarantee identification without some auxiliary assumptions, and the auxiliary assumptions have not been proposed. This study proposes a set of restrictions for possibly manipulated RD designs to validate point identification and diagnostic tests. The same restrictions simultaneously validate worst-case bounds when the diagnostic tests are validated. Therefore, our designs are manipulation robust in testing and identification. The worst-case bounds have two shorter bounds as special cases, and we apply special-case bounds to a controversy regarding the incumbency margin study of the U.S. House of Representatives elections .
Edit intent: coherence
Original:  Measure of consensus in attitudinal graph reflects how easy or difficult consensus can be reached that is acceptable by everyone. Frustration index is one such measure as it determines the distance of a network from a state of total structural balance.
Revised:  A signed graph is called balanced if each of its cycles includes an even number of negative edges. Balance is often characterized by frustration index or by finding a single convergent balanced state i.e. network consensus.
Edit intent: meaning-changed
Original:  In this paper, we propose to measure the consensus in the graph by expanding the notion of frustration index to a frustration cloud, a collection of nearest balanced states for a given network. The frustration cloud resolves the consensus problem with minimal sentiment disruption, taking all possible consensus views over the entire network into consideration. A frustration cloud based approach removes the brittleness of traditional network graph analysis, as it allows one to examine the consensus on entire graph . A spanning-tree-based balancing algorithm captures the variations of balanced states and global consensus of the network, and enables us to measure vertex influence on consensus and strength of its expressed attitudes. The proposed algorithm provides a parsimonious account of the differences between strong and weak statuses and influences of a vertex in a large network, as demonstrated on sample attitudinal network graphs constructed from social and  survey data .
Revised:  In this paper, we propose to measure the consensus in the graph by expanding the notion of frustration index to a frustration cloud, a collection of nearest balanced states . We introduce the frustration cloud as a set of all nearest balanced states, and use a graph balancing algorithm to find all nearest balanced states in deterministic way. Computational concerns are addressed by measuring consensus probabilistically, and we introduce new vertex and survey data .
Edit intent: coherence
Original:  The proposed algorithm provides a parsimonious account of the differences between strong and weak statuses and influences of a vertex in a large network, as demonstrated on sample attitudinal network graphs constructed from social and  survey data .
Revised:  The proposed algorithm provides a parsimonious account of the differences between strong and weak statuses and influences of a vertex in a large network, as demonstrated on sample attitudinal network graphs constructed from social and edge metrics to quantify status, agreement, and influence. We introduce new global measure of controversy for a given signed graph, and show that vertex status is a zero-sum game in the signed network. We propose an efficient scalable algorithm for calculating frustration cloud based measures in social network and survey data .
Edit intent: meaning-changed
Original:  The proposed algorithm provides a parsimonious account of the differences between strong and weak statuses and influences of a vertex in a large network, as demonstrated on sample attitudinal network graphs constructed from social and  survey data . We show that the proposed method accurately models the alliance network, provides discriminant features for community discovery , successfully predicts administrator election outcome consistent with real election outcomes, and provides deeper analytic insights into ANG outcome analysis by pinpointing influential vertices and anomalous decisions  .
Revised:  The proposed algorithm provides a parsimonious account of the differences between strong and weak statuses and influences of a vertex in a large network, as demonstrated on sample attitudinal network graphs constructed from social and survey data of up to 80,000 vertices and half-a-million edges, and we demonstrate the power of the proposed approach to provide discriminant features for community discovery , successfully predicts administrator election outcome consistent with real election outcomes, and provides deeper analytic insights into ANG outcome analysis by pinpointing influential vertices and anomalous decisions .
Edit intent: clarity
Original:  We show that the proposed method accurately models the alliance network, provides discriminant features for community discovery , successfully predicts administrator election outcome consistent with real election outcomes, and provides deeper analytic insights into ANG outcome analysis by pinpointing influential vertices and anomalous decisions  .
Revised:  We show that the proposed method accurately models the alliance network, provides discriminant features for community discovery when compared to spectral clustering and to automatically identify dominant vertices and anomalous decisions .
Edit intent: meaning-changed
Original:  We show that the proposed method accurately models the alliance network, provides discriminant features for community discovery , successfully predicts administrator election outcome consistent with real election outcomes, and provides deeper analytic insights into ANG outcome analysis by pinpointing influential vertices and anomalous decisions  .
Revised:  We show that the proposed method accurately models the alliance network, provides discriminant features for community discovery , successfully predicts administrator election outcome consistent with real election outcomes, and provides deeper analytic insights into ANG outcome analysis by pinpointing influential vertices and anomalous decisions in the network .
Edit intent: clarity
Original:  In experiments, we show that the proposed generative approach outperforms the discriminative approach in the Room-2-Room (R2R) dataset , especially in the unseen environments.
Revised:  In experiments, we show that the proposed generative approach outperforms the discriminative approach in the Room-2-Room (R2R) and Room-4-Room (R4R) datasets , especially in the unseen environments.
Edit intent: meaning-changed
Original: We provide first evidence that some families of nonlinear quantum systems , rephrased in terms of coined quantum walks with effective nonlinear phase, display a strong computational advantage for search algorithms, over graphs having sets of vertices of constant size, e. g. the infinite square grid.
Revised: We provide numerical evidence that the nonlinear searching algorithm introduced by Wong and Meyer \mbox{%DIFAUXCMD meyer2013nonlinear , rephrased in terms of coined quantum walks with effective nonlinear phase, display a strong computational advantage for search algorithms, over graphs having sets of vertices of constant size, e. g. the infinite square grid.
Edit intent: meaning-changed
Original: We provide first evidence that some families of nonlinear quantum systems , rephrased in terms of coined quantum walks with effective nonlinear phase, display a strong computational advantage for search algorithms, over graphs having sets of vertices of constant size, e. g. the infinite square grid.
Revised: We provide first evidence that some families of nonlinear quantum systems , rephrased in terms of quantum walks with effective nonlinear phase, display a strong computational advantage for search algorithms, over graphs having sets of vertices of constant size, e. g. the infinite square grid.
Edit intent: clarity
Original: We provide first evidence that some families of nonlinear quantum systems , rephrased in terms of coined quantum walks with effective nonlinear phase, display a strong computational advantage for search algorithms, over graphs having sets of vertices of constant size, e. g. the infinite square grid.
Revised: We provide first evidence that some families of nonlinear quantum systems , rephrased in terms of coined quantum walks with effective nonlinear phase, can be extended to the finite 2-dimensional grid, keeping the same computational advantage
Edit intent: meaning-changed
Original:   The numerical simulations show that the walker finds the marked vertex in O(N^{1/4} \log^{3/4} N) steps  with probability O(1/\log N), for an overall complexity of O(N^{1/4}\log^{7/4}N).
Revised:  respect to the classical algorithms. For this purpose, we have considered the free lattice Hamiltonian, with linear dispersion relation introduced by Childs and Ge \mbox{%DIFAUXCMD Childs_2014 The numerical simulations show that the walker finds the marked vertex in O(N^{1/4} \log^{3/4} N) steps with probability O(1/\log N), for an overall complexity of O(N^{1/4}\log^{7/4}N).
Edit intent: meaning-changed
Original:   The numerical simulations show that the walker finds the marked vertex in O(N^{1/4} \log^{3/4} N) steps  with probability O(1/\log N), for an overall complexity of O(N^{1/4}\log^{7/4}N).
Revised:  The numerical simulations showed that the walker finds the marked vertex in O(N^{1/4} \log^{3/4} N) steps with probability O(1/\log N), for an overall complexity of O(N^{1/4}\log^{7/4}N).
Edit intent: fluency
Original:   The numerical simulations show that the walker finds the marked vertex in O(N^{1/4} \log^{3/4} N) steps  with probability O(1/\log N), for an overall complexity of O(N^{1/4}\log^{7/4}N).
Revised:  The numerical simulations show that the walker finds the marked vertex in O(N^{1/4} \log^{3/4} N) steps , with probability O(1/\log N), for an overall complexity of O(N^{1/4}\log^{7/4}N).
Edit intent: fluency
Original:  We also confirm this result analytically. Moreover the algorithm is implemented without the need for a specific oracle step, but by topological hole defect in the grid. From a quantum computing perspective, however, this hints at novel applications of quantum walk search: instead of using them to look for "good" solutions within the configuration space of a problem, we could use them to look for topological properties of the entire configuration space .
Revised:  We also proved that there exists an optimal choice of the walker parameters to avoid that the time measurement precision affects the complexity searching time of the algorithm .
Edit intent: coherence
Original:  Finally, we differentiate CoDEx from a popular link prediction benchmark by showing that CoDEx covers more diverse and interpretable content, and contains fewer relation patterns that can be covered by trivial frequency-based rules . Data, code, and pretrained models are available at URL
Revised:  Finally, we differentiate CoDEx from the popular FB15K-237 knowledge graph completion dataset by showing that CoDEx covers more diverse and interpretable content, and contains fewer relation patterns that can be covered by trivial frequency-based rules . Data, code, and pretrained models are available at URL
Edit intent: meaning-changed
Original:  Finally, we differentiate CoDEx from a popular link prediction benchmark by showing that CoDEx covers more diverse and interpretable content, and contains fewer relation patterns that can be covered by trivial frequency-based rules . Data, code, and pretrained models are available at URL
Revised:  Finally, we differentiate CoDEx from a popular link prediction benchmark by showing that CoDEx covers more diverse and interpretable content, and is a more difficult link prediction benchmark . Data, code, and pretrained models are available at URL
Edit intent: meaning-changed
Original:  To alleviate these problems, we frame text generation as a reinforcement learning (RL) problem with expert demonstrations (i.e., the training data ), where the goal is to maximize quality given model-generated histories.
Revised:  To alleviate these problems, we frame text generation as an offline reinforcement learning (RL) problem with expert demonstrations (i.e., the training data ), where the goal is to maximize quality given model-generated histories.
Edit intent: meaning-changed
Original:  To alleviate these problems, we frame text generation as a reinforcement learning (RL) problem with expert demonstrations (i.e., the training data ), where the goal is to maximize quality given model-generated histories.
Revised:  To alleviate these problems, we frame text generation as a reinforcement learning (RL) problem with expert demonstrations (i.e., the reference ), where the goal is to maximize quality given model-generated histories.
Edit intent: clarity
Original:  We propose GOLD (generation by off-policy learning from demonstrations): an  algorithm that learns from the off-policy demonstrations by importance weighting and does not suffer from degenerative solutions. We find that GOLD outperforms the baselines according to automatic and human evaluation  on summarization, question generation, and machine translation , including attaining state-of-the-art results for CNN/DailyMail summarization .
Revised:  We propose GOLD (generation by off-policy learning from demonstrations): an algorithm that learns from the off-policy demonstrations by importance weighting . Intuitively, GOLD upweights confident tokens and downweights unconfident ones in the reference during training, avoiding optimization issues faced by prior RL approaches that rely on online data collection. According to both automatic and human evaluation on summarization, question generation, and machine translation , including attaining state-of-the-art results for CNN/DailyMail summarization .
Edit intent: meaning-changed
Original:  We find that GOLD outperforms the baselines according to automatic and human evaluation  on summarization, question generation, and machine translation , including attaining state-of-the-art results for CNN/DailyMail summarization .
Revised:  We find that GOLD outperforms the baselines according to automatic and human evaluation , models trained by GOLD outperform those trained by MLE and policy gradient on summarization, question generation, and machine translation , including attaining state-of-the-art results for CNN/DailyMail summarization .
Edit intent: meaning-changed
Original:  We  find that the vast majority of sites that implement opt-out mechanisms do so with a Do Not Sell link rather than with a privacy banner, and that many of the linked opt-out controls exhibit features such as nudging and indirect mechanisms (e.g., fillable forms).
Revised:  We also perform an automated analysis of the Top 5000 U.S. websites. We find that the vast majority of sites that implement opt-out mechanisms do so with a Do Not Sell link rather than with a privacy banner, and that many of the linked opt-out controls exhibit features such as nudging and indirect mechanisms (e.g., fillable forms).
Edit intent: meaning-changed
Original:  We then perform a pair of user studies with 4357 unique users (recruited from Google Ads and Amazon Mechanical Turk) in which we observe how users interact with different opt-out mechanisms and evaluate how the implementation choices we observed---exclusive use of links, prevalent nudging, and indirect mechanisms---affect the rate at which users exercise their right to opt-out of sale.
Revised:  We then perform a pair of user studies with 4357 unique users (recruited from Google Ads and Amazon Mechanical Turk) in which we observe how users interact with different opt-out mechanisms and evaluate how the implementation choices we observed -- exclusive use of links, prevalent nudging, and indirect mechanisms---affect the rate at which users exercise their right to opt-out of sale.
Edit intent: fluency
Original:  We then perform a pair of user studies with 4357 unique users (recruited from Google Ads and Amazon Mechanical Turk) in which we observe how users interact with different opt-out mechanisms and evaluate how the implementation choices we observed---exclusive use of links, prevalent nudging, and indirect mechanisms---affect the rate at which users exercise their right to opt-out of sale.
Revised:  We then perform a pair of user studies with 4357 unique users (recruited from Google Ads and Amazon Mechanical Turk) in which we observe how users interact with different opt-out mechanisms and evaluate how the implementation choices we observed---exclusive use of links, prevalent nudging, and indirect mechanisms -- affect the rate at which users exercise their right to opt-out of sale.
Edit intent: fluency
Original:  We find that these design elements significantly deter interactions with opt-out mechanisms ( including reducing the opt-out rate for users who are uncomfortable with the sale of their information ) and that they reduce users' awareness of their ability to opt-out.
Revised:  We find that these design elements significantly deter interactions with opt-out mechanisms -- including reducing the opt-out rate for users who are uncomfortable with the sale of their information ) and that they reduce users' awareness of their ability to opt-out.
Edit intent: fluency
Original:  We find that these design elements significantly deter interactions with opt-out mechanisms ( including reducing the opt-out rate for users who are uncomfortable with the sale of their information ) and that they reduce users' awareness of their ability to opt-out.
Revised:  We find that these design elements significantly deter interactions with opt-out mechanisms ( including reducing the opt-out rate for users who are uncomfortable with the sale of their information -- and that they reduce users' awareness of their ability to opt-out.
Edit intent: fluency
Original:  On average, the Gaussian method requires ~5ms ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ~ 7ms ms on average in the presence of 4 agents.
Revised:  On average, the Gaussian method requires ~5ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ~ 7ms ms on average in the presence of 4 agents.
Edit intent: fluency
Original:  On average, the Gaussian method requires ~5ms ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ~ 7ms ms on average in the presence of 4 agents.
Revised:  On average, the Gaussian method requires ~5ms ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ~ 9ms on average in the presence of 4 agents.
Edit intent: meaning-changed
Original:  On average, the Gaussian method requires ~5ms ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ~ 7ms ms on average in the presence of 4 agents.
Revised:  On average, the Gaussian method requires ~5ms ms to compute a local collision-free trajectory, while our non-Gaussian method is computationally more expensive and requires ~ 7ms ms on average in scenarios with 4 agents.
Edit intent: clarity