wanyu/IteraTeR_full_sent huggingface dataset:
Original:  First of all , if the receiver is dynamically consistent, I show the sender cannot make any profits more than Bayesian persuasion. In other words, ambiguity plays a role in persuasion only through inducing dynamically inconsistent behaviors. On the other hand, if the receiver is dynamically inconsistent and is able to adjust the information structure by ignoring the undesirable messages. Two seemingly undesirable features of ambiguous persuasion, synonyms and dilation, are not always undesirable to the receiver. In fact, they are always undesirable if and only if the payoff-relevant states are binary. Nonetheless, I show that the optimal value of ambiguous persuasion in the interim setting%DIFDELCMD < \citep*{Beauch\^ene, Li and Li, 2019} %%% cannot be achieved in the current setting  .
Revised:  First of all , if the %DIFDELCMD < \citep*{Beauch\^ene, Li and Li, 2019} %%% cannot be achieved in the current setting .
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Original:  Nonetheless, I show that the optimal value of ambiguous persuasion in the interim setting%DIFDELCMD < \citep*{Beauch\^ene, Li and Li, 2019} %%% cannot be achieved in the current setting  .
Revised:  Nonetheless, I show that the optimal value of ambiguous persuasion in the interim setting%DIFDELCMD < \citep*{Beauch\^ene, Li and Li, 2019} %%% sender is strictly less ambiguity averse than the receiver (in the sense of alpha-MEU), then she may benefit even when facing a dynamically consistent receiver. This gain comes from extracting an .
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Original:  Nonetheless, I show that the optimal value of ambiguous persuasion in the interim setting%DIFDELCMD < \citep*{Beauch\^ene, Li and Li, 2019} %%% cannot be achieved in the current setting  .
Revised:  Nonetheless, I show that the optimal value of ambiguous persuasion in the interim setting%DIFDELCMD < \citep*{Beauch\^ene, Li and Li, 2019} %%% cannot be achieved in the current settingambiguity premium .
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Original:  Nonetheless, I show that the optimal value of ambiguous persuasion in the interim setting%DIFDELCMD < \citep*{Beauch\^ene, Li and Li, 2019} %%% cannot be achieved in the current setting  .
Revised:  Nonetheless, I show that the optimal value of ambiguous persuasion in the interim setting%DIFDELCMD < \citep*{Beauch\^ene, Li and Li, 2019} %%% cannot be achieved in the current setting by exploiting the differences in the ambiguity attitudes .
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Original:  A `` time of flight '' (ToF) mechanism, adapted from the pioneering idea of Galileo, has been used successfully very recently to explain the length control of flagella by a biflagellate green algae.
Revised:  A " time of flight '' (ToF) mechanism, adapted from the pioneering idea of Galileo, has been used successfully very recently to explain the length control of flagella by a biflagellate green algae.
Edit intent: fluency
Original:  A `` time of flight '' (ToF) mechanism, adapted from the pioneering idea of Galileo, has been used successfully very recently to explain the length control of flagella by a biflagellate green algae.
Revised:  A `` time of flight " (ToF) mechanism, adapted from the pioneering idea of Galileo, has been used successfully very recently to explain the length control of flagella by a biflagellate green algae.
Edit intent: fluency
Original:  However, there are still two issues need to consider: (1) How to effectively represent the knowledge bases and incorporate it into dialogue system. (2) How to efficiently reason the knowledge bases given queries. To solve these issues , we design a novel Transformer-based Dynamic Memory Network (DMN) with a novel Memory Mask scheme , which can dynamically generate the context-aware knowledge base representations , and reason the knowledge bases simultaneously .
Revised:  However, there are still two issues need to consider: (1) How to effectively represent the knowledge bases and incorporate that into dialogue systems. To solve this issue , we design a novel Transformer-based Dynamic Memory Network (DMN) with a novel Memory Mask scheme , which can dynamically generate the context-aware knowledge base representations , and reason the knowledge bases simultaneously .
Edit intent: coherence
Original:  To solve these issues , we design a novel Transformer-based Dynamic Memory Network (DMN) with a novel Memory Mask scheme , which can dynamically generate the context-aware knowledge base representations , and reason the knowledge bases simultaneously .
Revised:  To solve these issues , we design a novel Context-aware Memory Generation module to model the knowledge bases , which can dynamically generate the context-aware knowledge base representations , and reason the knowledge bases simultaneously .
Edit intent: meaning-changed
Original:  To solve these issues , we design a novel Transformer-based Dynamic Memory Network (DMN) with a novel Memory Mask scheme , which can dynamically generate the context-aware knowledge base representations , and reason the knowledge bases simultaneously .
Revised:  To solve these issues , we design a novel Transformer-based Dynamic Memory Network (DMN) with a novel Memory Mask scheme , which can generate context-aware knowledge base representations , and reason the knowledge bases simultaneously .
Edit intent: clarity
Original:  To solve these issues , we design a novel Transformer-based Dynamic Memory Network (DMN) with a novel Memory Mask scheme , which can dynamically generate the context-aware knowledge base representations , and reason the knowledge bases simultaneously .
Revised:  To solve these issues , we design a novel Transformer-based Dynamic Memory Network (DMN) with a novel Memory Mask scheme , which can dynamically generate the context-aware entity representations with perceiving relevant entities .
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Original:  The Koopman representation of the nonlinear system is then used to synthesize LQRfeedback .
Revised:  Using the inverted pendulum system, we illustrate the robustness of the error bounds given noisy measurements of unknown dynamics, where the derivatives are estimated numerically. When combined with control, the Koopman representation of the nonlinear system is then used to synthesize LQRfeedback .
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Original:  The Koopman representation of the nonlinear system is then used to synthesize LQRfeedback .
Revised:  The Koopman representation of the nonlinear system has marginally better performance than competing nonlinear modeling methods, such as SINDy and NARX. In addition, as a linear model, the Koopman approach lends itself readily to efficient control design tools, such as LQR, whereas the other modeling approaches require nonlinear control methods .
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Original: We show that for every integer n\geq 1 there exists a graph G_n with n ^{1 + o(1)} edges such that every n-vertex planar graph is isomorphic to a subgraph of G_n.
Revised: We show that for every integer n\geq 1 there exists a graph G_n with (1+o(1))n vertices and n ^{1 + o(1)} edges such that every n-vertex planar graph is isomorphic to a subgraph of G_n.
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Original:  The best previous bound on the number of edges was O(n^{3/2}), proved by Babai,  Erdos, Chung, Graham, and Spencer in 1982.  vertices and edges that contains induced copies of every n-vertex planar graph.
Revised:  The best previous bound on the number of edges was O(n^{3/2}), proved by Babai, Chung, Erdos, Chung, Graham, and Spencer in 1982. vertices and edges that contains induced copies of every n-vertex planar graph.
Edit intent: fluency
Original:  The best previous bound on the number of edges was O(n^{3/2}), proved by Babai,  Erdos, Chung, Graham, and Spencer in 1982.  vertices and edges that contains induced copies of every n-vertex planar graph.
Revised:  The best previous bound on the number of edges was O(n^{3/2}), proved by Babai, Erdos, Graham, and Spencer in 1982. vertices and edges that contains induced copies of every n-vertex planar graph.
Edit intent: meaning-changed
Original:  The best previous bound on the number of edges was O(n^{3/2}), proved by Babai,  Erdos, Chung, Graham, and Spencer in 1982.  vertices and edges that contains induced copies of every n-vertex planar graph.
Revised:  The best previous bound on the number of edges was O(n^{3/2}), proved by Babai, Erdos, Chung, Graham, and Spencer in 1982. We then show that for every integer n\geq 1 there is a graph U_n with n^{1 + o(1) vertices and edges that contains induced copies of every n-vertex planar graph.
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Original:  We provide an introduction to topological summaries of time-varying metric spaces including vineyards [ 17 ], crocker plots [ 52 ], and multiparameter rank functions [ 34 ].
Revised:  We provide an introduction to topological summaries of time-varying metric spaces including vineyards [ 19 ], crocker plots [ 52 ], and multiparameter rank functions [ 34 ].
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Original:  We provide an introduction to topological summaries of time-varying metric spaces including vineyards [ 17 ], crocker plots [ 52 ], and multiparameter rank functions [ 34 ].
Revised:  We provide an introduction to topological summaries of time-varying metric spaces including vineyards [ 17 ], crocker plots [ 56 ], and multiparameter rank functions [ 34 ].
Edit intent: meaning-changed
Original:  We provide an introduction to topological summaries of time-varying metric spaces including vineyards [ 17 ], crocker plots [ 52 ], and multiparameter rank functions [ 34 ].
Revised:  We provide an introduction to topological summaries of time-varying metric spaces including vineyards [ 17 ], crocker plots [ 52 ], and multiparameter rank functions [ 37 ].
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Original:  In this paper, we investigate the two aspects of adaptivity and parameterization in sublinear-time algorithms for LIS estimation . We first show that adaptivity helps in LIS estimation, specifically , for every constant \epsilon \in (0,1), every nonadaptive algorithm that outputs an estimate of the length of the LIS in an array of length n to within an additive error of \epsilon \cdot n has to make \log^{\Omega(\log (1/\epsilon))} n) queries.
Revised:  In this paper, we investigate the two aspects of adaptivity and parameterization in sublinear-time algorithms for LIS estimation than the obvious bounds implied by testing monotonicity (for adaptive or nonadaptive algorithms). In this paper, we give the first nontrivial lower bound on the complexity of LIS estimation, specifically , for every constant \epsilon \in (0,1), every nonadaptive algorithm that outputs an estimate of the length of the LIS in an array of length n to within an additive error of \epsilon \cdot n has to make \log^{\Omega(\log (1/\epsilon))} n) queries.
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Original:  We first show that adaptivity helps in LIS estimation, specifically , for every constant \epsilon \in (0,1), every nonadaptive algorithm that outputs an estimate of the length of the LIS in an array of length n to within an additive error of \epsilon \cdot n has to make \log^{\Omega(\log (1/\epsilon))} n) queries.
Revised:  We first show that adaptivity helps in LIS estimation, and also provide novel algorithms that complement our lower bound. Specifically , for every constant \epsilon \in (0,1), every nonadaptive algorithm that outputs an estimate of the length of the LIS in an array of length n to within an additive error of \epsilon \cdot n has to make \log^{\Omega(\log (1/\epsilon))} n) queries.
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Original:  This is the first lower bound on LIS estimation that is significantly larger than the query complexity of testing sortedness. In contrast, there is an adaptive algorithm (Saks, and Seshadhri; 2017) for the same problem with query complexity polylogarithmic in n. Next, we design nonadaptive LIS estimation algorithms whose complexity is parameterized in terms of the number of distinct values r in the array  .
Revised:  Next, we design nonadaptive LIS estimation algorithms whose complexity is parameterized in terms of the number of distinct values r in the array .
Edit intent: coherence
Original:  2017) for the same problem with query complexity polylogarithmic in n. Next, we design nonadaptive LIS estimation algorithms whose complexity is parameterized in terms of the number of distinct values r in the array  .
Revised:  2017) for the same problem with query complexity polylogarithmic in n. Next, we design nonadaptive LIS estimation algorithms whose complexity decreases as the the number of distinct values r in the array .
Edit intent: clarity
Original:  2017) for the same problem with query complexity polylogarithmic in n. Next, we design nonadaptive LIS estimation algorithms whose complexity is parameterized in terms of the number of distinct values r in the array  .
Revised:  2017) for the same problem with query complexity polylogarithmic in n. Next, we design nonadaptive LIS estimation algorithms whose complexity is parameterized in terms of the number of distinct values , r, in the array .
Edit intent: fluency
Original:  2017) for the same problem with query complexity polylogarithmic in n. Next, we design nonadaptive LIS estimation algorithms whose complexity is parameterized in terms of the number of distinct values r in the array  .
Revised:  2017) for the same problem with query complexity polylogarithmic in n. Next, we design nonadaptive LIS estimation algorithms whose complexity is parameterized in terms of the number of distinct values r in the array decreases .
Edit intent: fluency
Original:  We then use it to construct a nonadaptive algorithm with query complexity O(r  /\lambda ^2) that, for an array in which the LIS is of length at least \lambda n, outputs a O (\lambda) multiplicative approximation to the length of the LIS . Our algorithm improves upon state of the art nonadaptive algorithms for LIS estimation (for r=n) in terms of approximation guarantee.
Revised:  We then use it to construct a nonadaptive algorithm with query complexity O(r \cdot \text{poly /\lambda ^2) that, for an array in which the LIS is of length at least \lambda n, outputs a O (\lambda) multiplicative approximation to the length of the LIS . Our algorithm improves upon state of the art nonadaptive algorithms for LIS estimation (for r=n) in terms of approximation guarantee.
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Original:  We then use it to construct a nonadaptive algorithm with query complexity O(r  /\lambda ^2) that, for an array in which the LIS is of length at least \lambda n, outputs a O (\lambda) multiplicative approximation to the length of the LIS . Our algorithm improves upon state of the art nonadaptive algorithms for LIS estimation (for r=n) in terms of approximation guarantee.
Revised:  We then use it to construct a nonadaptive algorithm with query complexity O(r /\lambda )) that, for an array in which the LIS is of length at least \lambda n, outputs a O (\lambda) multiplicative approximation to the length of the LIS . Our algorithm improves upon state of the art nonadaptive algorithms for LIS estimation (for r=n) in terms of approximation guarantee.
Edit intent: clarity
Original:  We then use it to construct a nonadaptive algorithm with query complexity O(r  /\lambda ^2) that, for an array in which the LIS is of length at least \lambda n, outputs a O (\lambda) multiplicative approximation to the length of the LIS . Our algorithm improves upon state of the art nonadaptive algorithms for LIS estimation (for r=n) in terms of approximation guarantee.
Revised:  We then use it to construct a nonadaptive algorithm with query complexity O(r /\lambda ^2) that, for an array with LIS length at least \lambda n, outputs a O (\lambda) multiplicative approximation to the length of the LIS . Our algorithm improves upon state of the art nonadaptive algorithms for LIS estimation (for r=n) in terms of approximation guarantee.
Edit intent: clarity
Original:  We then use it to construct a nonadaptive algorithm with query complexity O(r  /\lambda ^2) that, for an array in which the LIS is of length at least \lambda n, outputs a O (\lambda) multiplicative approximation to the length of the LIS . Our algorithm improves upon state of the art nonadaptive algorithms for LIS estimation (for r=n) in terms of approximation guarantee.
Revised:  We then use it to construct a nonadaptive algorithm with query complexity O(r /\lambda ^2) that, for an array in which the LIS is of length at least \lambda n, outputs a multiplicative \Omega (\lambda) multiplicative approximation to the length of the LIS . Our algorithm improves upon state of the art nonadaptive algorithms for LIS estimation (for r=n) in terms of approximation guarantee.
Edit intent: clarity
Original:  We then use it to construct a nonadaptive algorithm with query complexity O(r  /\lambda ^2) that, for an array in which the LIS is of length at least \lambda n, outputs a O (\lambda) multiplicative approximation to the length of the LIS . Our algorithm improves upon state of the art nonadaptive algorithms for LIS estimation (for r=n) in terms of approximation guarantee.
Revised:  We then use it to construct a nonadaptive algorithm with query complexity O(r /\lambda ^2) that, for an array in which the LIS is of length at least \lambda n, outputs a O (\lambda) -approximation to the LIS length.
Edit intent: clarity
Original:  Our result implies that nonadaptive tolerant testing is strictly harder than nonadaptive erasure-resilient testing for the natural property of sortedness, thereby making progress towards solving an open question (Raskhodnikova, Ron-Zewi, and Varma; 2019) .
Revised:  Our result implies that nonadaptive tolerant testing is strictly harder than nonadaptive erasure-resilient testing for the natural property of monotonicity .
Edit intent: clarity
Original:  The greedy algorithm of Birkhoff and von Neumann , which starts with the given fractional perfect matching and successively "removes" from it perfect matchings, with appropriate multipliers, fails in non-bipartite graphs -- removing perfect matchings arbitrarily can lead to a graph that is non-empty but has no perfect matchings.
Revised:  The algorithm of Birkhoff and von Neumann , which starts with the given fractional perfect matching and successively "removes" from it perfect matchings, with appropriate multipliers, fails in non-bipartite graphs -- removing perfect matchings arbitrarily can lead to a graph that is non-empty but has no perfect matchings.
Edit intent: clarity
Original:  The greedy algorithm of Birkhoff and von Neumann , which starts with the given fractional perfect matching and successively "removes" from it perfect matchings, with appropriate multipliers, fails in non-bipartite graphs -- removing perfect matchings arbitrarily can lead to a graph that is non-empty but has no perfect matchings.
Revised:  The greedy algorithm of Birkhoff and von Neumann is greedy; it starts with the given fractional perfect matching and successively "removes" from it perfect matchings, with appropriate multipliers, fails in non-bipartite graphs -- removing perfect matchings arbitrarily can lead to a graph that is non-empty but has no perfect matchings.
Edit intent: clarity
Original:  The greedy algorithm of Birkhoff and von Neumann , which starts with the given fractional perfect matching and successively "removes" from it perfect matchings, with appropriate multipliers, fails in non-bipartite graphs -- removing perfect matchings arbitrarily can lead to a graph that is non-empty but has no perfect matchings.
Revised:  The greedy algorithm of Birkhoff and von Neumann , which starts with the given fractional perfect matching and successively "removes" from it perfect matchings, with appropriate coefficients. This fails in non-bipartite graphs -- removing perfect matchings arbitrarily can lead to a graph that is non-empty but has no perfect matchings.
Edit intent: clarity
Original:  Here , we require perturbation strengths w such that 1/N\ll w\ll 1 for typical expanding populations of size N%DIFDELCMD < \to %%% \infty;
Revised:  For typical populations , we require perturbation strengths w such that 1/N\ll w\ll 1 for typical expanding populations of size N%DIFDELCMD < \to %%% \infty;
Edit intent: fluency
Original:  Here , we require perturbation strengths w such that 1/N\ll w\ll 1 for typical expanding populations of size N%DIFDELCMD < \to %%% \infty;
Revised:  Here , we require perturbation strengths satisfying 1/N\ll w\ll 1 for typical expanding populations of size N%DIFDELCMD < \to %%% \infty;
Edit intent: clarity
Original:  Here , we require perturbation strengths w such that 1/N\ll w\ll 1 for typical expanding populations of size N%DIFDELCMD < \to %%% \infty; this range is in contrast to w=\mathcal O(1/N) for the diffusion approximations of the model. The first main result proves that the density processes of strategy converge to the replicator equation in the limit of a large finite population subject to appropriate, but general, conditions.
Revised:  Here , we require perturbation strengths w such that 1/N\ll w\ll 1 for typical expanding populations of size N%DIFDELCMD < \to %%% 1. Under appropriate conditions on the space, the limiting density processes of strategy converge to the replicator equation in the limit of a large finite population subject to appropriate, but general, conditions.
Edit intent: clarity
Original:  The first main result proves that the density processes of strategy converge to the replicator equation in the limit of a large finite population subject to appropriate, but general, conditions. Multiple strategies and mutations are allowed. Moreover, after normalization, the limiting fluctuations are shown to be Gaussian with a covariance function in the replicator equation's solution through the Wright--Fisher noise coefficient.
Revised:  The first main result proves that the density processes of strategy converge to the replicator equation , and the normalized fluctuations converge to a Gaussian process with the Wright-Fisher covariance function in the replicator equation's solution through the Wright--Fisher noise coefficient.
Edit intent: clarity
Original:  Moreover, after normalization, the limiting fluctuations are shown to be Gaussian with a covariance function in the replicator equation's solution through the Wright--Fisher noise coefficient. In particular, to the degree of using constants that may depend implicitly on space, we prove a conjecture from the biological literature that the expected density processes approximate the replicator equation on many non-regular graphs.
Revised:  Moreover, after normalization, the limiting fluctuations are shown to be Gaussian with a covariance function in the limiting densities. As an application, we resolve in the positive a conjecture from the biological literature that the expected density processes approximate the replicator equation on many non-regular graphs.
Edit intent: clarity
Original:  We show that the latency and accuracy of streaming ASR significantly benefit from weight sharing and joint training of full-context ASR, especially with inplace knowledge distillation . The Universal ASR framework can be applied to recent state-of-the-art convolution-based and transformer-based ASR networks.
Revised:  We show that the latency and accuracy of streaming ASR significantly benefit from weight sharing and joint training of full-context ASR, especially with inplace knowledge distillation during the training. The Dual-mode ASR framework can be applied to recent state-of-the-art convolution-based and transformer-based ASR networks.
Edit intent: meaning-changed
Original:  We present extensive experiments with two state-of-the-art ASR networks, ContextNet and Conformer, on two datasets, a widely used public dataset LibriSpeech and an internal large-scale dataset MultiDomain.
Revised:  We present extensive experiments with two state-of-the-art ASR networks, ContextNet and Conformer, on two datasets, a widely used public dataset LibriSpeech and a large-scale dataset MultiDomain.
Edit intent: clarity