wanyu/IteraTeR_full_sent huggingface dataset:
Original:  With an open-source implementation  we demonstrate that the algorithm can scale to large temporal graphs with tens of millions of edges, despite the problem being NP-hard.
Revised:  In particular, parameterized by the length of a path k sought, we show the problems can be solved in O(2^k k m \Delta) time and O(n \tau) space, where n is the number of vertices, m the number of edges, \Delta the maximum resting time and \tau the maximum timestamp of an input temporal graph. In addition, we prove that the algorithms presented for the restless reachability problems in vertex-colored temporal graphs are optimal under plausible complexity-theoretic assumptions. Finally, with an open-source implementation we demonstrate that the algorithm can scale to large temporal graphs with tens of millions of edges, despite the problem being NP-hard.
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Original:  The implementation is efficiently engineered and highly optimized. For instance, we can solve the restless reachability problem by restricting the path length to 9 in a real-world graph dataset with over 36 million directed edges in less than one hour on a 4-core Haswell desktop .
Revised:  Specifically, we present extensive experiments to evaluate our scalability claims both on synthetic and real-world graph dataset with over 36 million directed edges in less than one hour on a 4-core Haswell desktop .
Edit intent: clarity
Original: Pretrained Language Models (LMs) generate text with remarkable quality, novelty,and coherence. Yet applying LMs to the problems of paraphrasing and infilling currently requires direct supervision, since these tasks break the left-to-right generation setup of pretrained LMs. We present Reflective Decoding, a novel unsupervised approach to apply the capabilities of pretrained LMs to non-sequential tasks.
Revised: Pretrained Language Models (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks that break the unidirectional assumption, such as paraphrasing or text-infilling, necessitating task-specific supervision. In this paper, we present Reflective Decoding, a novel unsupervised approach to apply the capabilities of pretrained LMs to non-sequential tasks.
Edit intent: clarity
Original:  In the contextualization step, we use LMs to generate many left and right contexts which collectively capture the meaning of the input sentence. Then , in the reflection step we decode in the semantic neighborhood of the input, conditioning on an ensemble of generated contexts with the reverse direction LM.
Revised:  In the contextualization step, we use LMs to generate many left and right contexts which collectively capture the input (e.g. the source sentence for paraphrasing). Second , in the reflection step we decode in the semantic neighborhood of the input, conditioning on an ensemble of generated contexts with the reverse direction LM.
Edit intent: meaning-changed
Original:  Then , in the reflection step we decode in the semantic neighborhood of the input, conditioning on an ensemble of generated contexts with the reverse direction LM. We reflect through the generated contexts, effectively using themas an intermediate meaning representation to generate conditional output. Empirical results demonstrate that Reflective Decoding outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods.
Revised:  Then , in the reflection step , we condition on these "context ensembles", generating outputs that are compatible with them. Comprehensive empirical results demonstrate that Reflective Decoding outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods.
Edit intent: clarity
Original:  We propose two approaches for improving the generalizability of utterance classification models: (1) example-driven training and (2) observers. Example-driven traininglearns to classify utterances by comparing to examples, thereby using the underlying encoder as a sentence similarity model . Prior work has shown that BERT-like models tend to attribute a significant amount of attention to the [CLS] token, which we hypothesize results in diluted representations.
Revised:  We propose two approaches for improving the generalizability of utterance classification models: (1) example-driven training and (2) example-driven training . Prior work has shown that BERT-like models tend to attribute a significant amount of attention to the [CLS] token, which we hypothesize results in diluted representations.
Edit intent: coherence
Original:  Observers are tokens that are not attended to, and are an alternative to the [CLS] token . The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWU ) in both the full data and few-shot (10 examples per intent) settings.
Revised:  Observers are tokens that are not attended to, and are an alternative to the [CLS] token as a semantic representation of utterances. Example-driven training learns to classify utterances by comparing to examples, thereby using the underlying encoder as a sentence similarity model. These methods are complementary; improving the representation through observers allows the example-driven model to better measure sentence similarities. When combined, the proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWU ) in both the full data and few-shot (10 examples per intent) settings.
Edit intent: meaning-changed
Original:  The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWU ) in both the full data and few-shot (10 examples per intent) settings.
Revised:  The proposed methods attain state-of-the-art results on three intent prediction datasets ( , and HWU ) in both the full data and few-shot (10 examples per intent) settings.
Edit intent: meaning-changed
Original:  The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWU ) in both the full data and few-shot (10 examples per intent) settings.
Revised:  The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc banking77 ) in both the full data and few-shot (10 examples per intent) settings.
Edit intent: meaning-changed
Original:  The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWU ) in both the full data and few-shot (10 examples per intent) settings.
Revised:  The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWU, ) in both the full data and few-shot (10 examples per intent) settings.
Edit intent: fluency
Original:  The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWU ) in both the full data and few-shot (10 examples per intent) settings.
Revised:  The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWUclinc150 ) in both the full data and few-shot (10 examples per intent) settings.
Edit intent: meaning-changed
Original:  The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWU ) in both the full data and few-shot (10 examples per intent) settings.
Revised:  The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWUhwu64 ) in both the full data and few-shot (10 examples per intent) settings.
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Original: Constrained motion planning is a challenging field of research, aiming for computationally efficient methods that can find a collision-free path connecting a given start and goal by transversing zero-volume constraint manifolds for a given planning problem .
Revised: Constrained motion planning is a challenging field of research, aiming for computationally efficient methods that can find a collision-free path on the constraint manifolds between a given start and goal by transversing zero-volume constraint manifolds for a given planning problem .
Edit intent: clarity
Original:  Overall, this study demonstrates that tauRAMD combines both computational efficiency and the ability to capture the main features determining the relative dissociation rates, thus providing insights that could be exploited in drug design .
Revised:  Overall, this study demonstrates that tauRAMD combines both computational efficiency and the ability to capture the main features determining the relative dissociation rates, thus providing insights that could be exploited in the rational optimization of the kinetic properties of drug candidates .
Edit intent: clarity
Original: The computation of dissociation rates from modeling of macromolecule-ligand unbinding processes is a topic of recent and increasing interest for drug design applications. However, the complexity of the high-dimensional space explored during dissociation on timescales that often exceed those accessible to conventional molecular dynamics (MD) simulations make this task highly challenging.
Revised: The protein-ligand residence time, tau, influences molecular function in biological networks and has been recognized as an important determinant of drug efficacy. To predict tau, computational methods must overcome the problem that tau often exceeds the timescales accessible to conventional molecular dynamics (MD) simulations make this task highly challenging.
Edit intent: clarity
Original:  However, the complexity of the high-dimensional space explored during dissociation on timescales that often exceed those accessible to conventional molecular dynamics (MD) simulations make this task highly challenging. Introduction of a scaling potential or additional force can accelerate the dissociation process, but may affect the reliability of simulated egress paths and thus observed mechanisms and computed residence times. We address this problem by exploring the dissociation of benzene and indole from T4 lysozyme mutants. We employ the tau-Random Acceleration Molecular Dynamics (tauRAMD) method , in which a small randomly oriented force is applied to the ligand to facilitate its unbinding during MD simulations.
Revised:  However, the complexity of the high-dimensional space explored during dissociation on timescales that often exceed those accessible to conventional molecular dynamics (MD) simulation. Here, we apply the tau-Random Acceleration Molecular Dynamics (tauRAMD) method , in which a small randomly oriented force is applied to the ligand to facilitate its unbinding during MD simulations.
Edit intent: coherence
Original:  We employ the tau-Random Acceleration Molecular Dynamics (tauRAMD) method , in which a small randomly oriented force is applied to the ligand to facilitate its unbinding during MD simulations. We find that tauRAMD yields the ranking of dissociation rates for the simulated ligands, protein mutants and temperature conditions in good accordance with experiments . Comparison of the unbinding paths and metastable states of benzene from the L99A mutant reveals a good agreement of tauRAMD with much more computationally demanding conventional MD simulations.
Revised:  We employ the tau-Random Acceleration Molecular Dynamics (tauRAMD) method to a set of kinetically characterized complexes of T4 lysozyme mutants with engineered binding cavities. tauRAMD yields relative ligand dissociation rates in good accordance with experiments . Comparison of the unbinding paths and metastable states of benzene from the L99A mutant reveals a good agreement of tauRAMD with much more computationally demanding conventional MD simulations.
Edit intent: clarity
Original:  We find that tauRAMD yields the ranking of dissociation rates for the simulated ligands, protein mutants and temperature conditions in good accordance with experiments . Comparison of the unbinding paths and metastable states of benzene from the L99A mutant reveals a good agreement of tauRAMD with much more computationally demanding conventional MD simulations. Moreover, mapping of the egress routes for all the systems studied shows that visiting more metastable states slows dissociation and indicates that accurate dissociation rates can be computed without exhaustive sampling, as long as the main egress route is sampled. Overall, this study provides physical insights that could be exploited in the rational optimization of the kinetic properties of drug candidates.
Revised:  We find that tauRAMD yields the ranking of dissociation rates for the simulated ligands, protein mutants and temperature conditions in good accordance with experiments and thereby allows a comprehensive characterization of the ligand egress routes and determinants of tau. Although ligand dissociation by multiple egress routes is observed, we find that egress via the predominant route determines the value of tau. We also find that the presence of metastable states along egress pathways slows down protein-ligand dissociation. These physical insights could be exploited in the rational optimization of the kinetic properties of drug candidates.
Edit intent: coherence
Original:  We aim to support two classes of misinformation detection problems over Twitter: verifying free-text claims (called claim-level verification) and verifying claims expressed in tweets (called tweet-level verification).
Revised:  ArCOV19-Rumors supports two levels of misinformation detection problems over Twitter: verifying free-text claims (called claim-level verification) and verifying claims expressed in tweets (called tweet-level verification).
Edit intent: meaning-changed
Original:  Our dataset covers, in addition to health, claims related to other topical categories that were influenced by COVID-19, namely, social, politics, sports, entertainment, and religious  .
Revised:  Our dataset covers, in addition to health, claims related to other topical categories that were influenced by COVID-19, namely, social, politics, sports, entertainment, and religious . Moreover, we present benchmarking results for tweet-level verification on the dataset. We experimented with SOTA models of versatile approaches that either exploit content, user profiles features, temporal features and propagation structure of the conversational threads for tweet verification .
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Original:  The best known algorithm in the average-case total setting is due to Wagner (following the approach of Blum-Kalai-Wasserman), and achieves a run-time of u^{O(1/\log k)} , which beats the known (conditional) lower bounds for worst-case k-SUM . One could wonder whether this can be improved even further.
Revised:  The best known algorithm in the average-case total setting is due to Wagner (following the approach of Blum-Kalai-Wasserman), and achieves a run-time of u^{O(1/\log k)} . This beats the known (conditional) lower bounds for worst-case k-SUM . One could wonder whether this can be improved even further.
Edit intent: coherence
Original:  The best known algorithm in the average-case total setting is due to Wagner (following the approach of Blum-Kalai-Wasserman), and achieves a run-time of u^{O(1/\log k)} , which beats the known (conditional) lower bounds for worst-case k-SUM . One could wonder whether this can be improved even further.
Revised:  The best known algorithm in the average-case total setting is due to Wagner (following the approach of Blum-Kalai-Wasserman), and achieves a run-time of u^{O(1/\log k)} , which beats the known (conditional) lower bounds for worst-case k-SUM , raising the natural question of whether it can be improved even further.
Edit intent: clarity
Original:  But , we show a matching average-case lower-bound, by showing a reduction from worst-case lattice problems, thus introducing a new family of techniques into the field of fine-grained complexity.
Revised:  However, in this work , we show a matching average-case lower-bound, by showing a reduction from worst-case lattice problems, thus introducing a new family of techniques into the field of fine-grained complexity.
Edit intent: clarity
Original: Decomposition methods are often used to produce counterfactual predictions of outcomes when a policy variable changes . The methods project the observed relationship between the policy variable and the endogenous variable to a counterfactual environment. However, when the endogenous variable is generated by an agent's strategy in a game-theoretic setting and the agent has an incentive to deviate from his strategy after the policy, such predictions are hard to justify .
Revised: When a government announces a new policy in a strategic setting, such as changes to taxes or tariffs, it creates additional signals through media reports or studies on the policy which may be used by economic agents. This may result in a new predicted outcome that is not captured by the analysis based on Bayes Nash equilibria .
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Original:  In this paper, we use a generic model of a Bayesian game with the solution concept of Bayes Correlated Equilibria of Bergemann and Morris (2016) and show that if the information structure is rich enough and the equilibrium selection rule satisfies a weak invariance property, decomposition-based predictions are identical to equilibrium-based predictions.
Revised:  In this paper, we adopt Bayes Correlated Equilibrium of Bergemann and Morris (2016) and show that if the information structure is rich enough and the equilibrium selection rule satisfies a weak invariance property, decomposition-based predictions are identical to equilibrium-based predictions.
Edit intent: clarity
Original:  In this paper, we use a generic model of a Bayesian game with the solution concept of Bayes Correlated Equilibria of Bergemann and Morris (2016) and show that if the information structure is rich enough and the equilibrium selection rule satisfies a weak invariance property, decomposition-based predictions are identical to equilibrium-based predictions.
Revised:  In this paper, we use a generic model of a Bayesian game with the solution concept of Bayes Correlated Equilibria of Bergemann and Morris (2016) and develop a method of counterfactual analysis that can be used for such situations. We show that, under an invariance condition for the equilibrium selection rule satisfies a weak invariance property, decomposition-based predictions are identical to equilibrium-based predictions.
Edit intent: meaning-changed
Original:  In this paper, we use a generic model of a Bayesian game with the solution concept of Bayes Correlated Equilibria of Bergemann and Morris (2016) and show that if the information structure is rich enough and the equilibrium selection rule satisfies a weak invariance property, decomposition-based predictions are identical to equilibrium-based predictions.
Revised:  In this paper, we use a generic model of a Bayesian game with the solution concept of Bayes Correlated Equilibria of Bergemann and Morris (2016) and show that if the information structure is rich enough and the equilibrium selection rule , the equilibrium-based predictions.
Edit intent: clarity
Original:  In this paper, we use a generic model of a Bayesian game with the solution concept of Bayes Correlated Equilibria of Bergemann and Morris (2016) and show that if the information structure is rich enough and the equilibrium selection rule satisfies a weak invariance property, decomposition-based predictions are identical to equilibrium-based predictions. This result opens up the possibility for applying a decomposition method for counterfactual analysis in various strategic settingswithout specifying and estimating details of the game.
Revised:  In this paper, we use a generic model of a Bayesian game with the solution concept of Bayes Correlated Equilibria of Bergemann and Morris (2016) and show that if the information structure is rich enough and the equilibrium selection rule satisfies a weak invariance property, decomposition-based predictions are identical to equilibrium-based prediction in this setting is interval-identified by the decomposition-based prediction which can be estimated without specifying the details of the game such as payoffs or distribution of unobserved heterogeneity. This approach greatly simplifies the counterfactual analysis in various strategic settingswithout specifying and estimating details of the game.
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Original:  As opposed to a conventional Model-Predictive Control (MPC) approach  that formulates a hierarchy of optimization problems, the proposed work formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC).
Revised:  As opposed to a conventional Model Predictive Control (MPC) approach that formulates a hierarchy of optimization problems, the proposed work formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC).
Edit intent: fluency
Original:  As opposed to a conventional Model-Predictive Control (MPC) approach  that formulates a hierarchy of optimization problems, the proposed work formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC).
Revised:  As opposed to a conventional Model-Predictive Control (MPC) approach to locomotion that formulates a hierarchy of optimization problems, the proposed work formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC).
Edit intent: meaning-changed
Original:  As opposed to a conventional Model-Predictive Control (MPC) approach  that formulates a hierarchy of optimization problems, the proposed work formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC).
Revised:  As opposed to a conventional Model-Predictive Control (MPC) approach that formulates a hierarchical sequence of optimization problems, the proposed work formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC).
Edit intent: meaning-changed
Original:  MHPC is formulated as a multi-phase receding-horizon Trajectory Optimization (TO) problem, and is solved by an efficient solvercalled Hybrid Systems Differential Dynamic Programming (HSDDP) . MHPC is benchmarked in simulation on a quadruped, a biped, and a quadrotor, demonstrating control performance on par or exceeding whole-body MPC while maintaining a lower computational cost in each case.
Revised:  MHPC is formulated as a multi-phase receding-horizon Trajectory Optimization (TO) problem, and can be implemented using any general multi-phase TO solver . MHPC is benchmarked in simulation on a quadruped, a biped, and a quadrotor, demonstrating control performance on par or exceeding whole-body MPC while maintaining a lower computational cost in each case.
Edit intent: clarity
Original:  A preliminary bounding experiment is conducted on the MIT Mini Cheetah with the control policy generated offline, demonstrating the physical validity of the generated trajectories and motivating online MHPC in future work.
Revised:  A preliminary gap jumping experiment is conducted on the MIT Mini Cheetah with the control policy generated offline, demonstrating the physical validity of the generated trajectories and motivating online MHPC in future work.
Edit intent: meaning-changed