wanyu/IteraTeR_v2 huggingface dataset:
Original:  However, Daskalakis and Pan disproved a version of this conjecture in 2014, showing that an exponentially-slow rate can occur, although their result relied on adversarial tie-breaking.
Revised:  However, Daskalakis and Pan disproved a version of this conjecture in 2014, showing that an exponentially-slow rate can occur, although their result relies on adversarial tie-breaking.
Edit intent: fluency
Original:  First, we show that if the game matrix is diagonal and ties are broken lexicographically , then FP converges at a O(1/ \epsilon^2) rate , and we also show a matching lower bound under this tie-breaking assumption .
Revised:  First, we show that if the game matrix is diagonal and ties are broken lexicographically . Specifically, we show that FP converges at a O(1/ \epsilon^2) rate , and we also show a matching lower bound under this tie-breaking assumption .
Edit intent: coherence
Original:  First, we show that if the game matrix is diagonal and ties are broken lexicographically , then FP converges at a O(1/ \epsilon^2) rate , and we also show a matching lower bound under this tie-breaking assumption .
Revised:  First, we show that if the game matrix is diagonal and ties are broken lexicographically , then FP converges at a O(1/ \sqrt{t a matching lower bound under this tie-breaking assumption .
Edit intent: clarity
Original: We study ResNet- , Time-Depth Separable ConvNets-, and Transformer-based acoustic models, trained with CTC or Seq2Seq criterions .
Revised: We study ResNet- , Time-Depth Separable ConvNets, and Transformers for speech recognition, with either CTC or Seq2Seq criterions .
Edit intent: clarity
Original:  As a significant shift from standard deep networks, which do not overtly represent any uncertainty in its structure or output , Bayesian deep networks are being currently investigated where the network is envisaged as a set of plausible models learnt by the Bayes' formulation in response to uncertainties in sensory data.
Revised:  As a significant shift from standard deep networks, which does not consider any uncertainty formulation during its training or inference , Bayesian deep networks are being currently investigated where the network is envisaged as a set of plausible models learnt by the Bayes' formulation in response to uncertainties in sensory data.
Edit intent: clarity
Original:  As a significant shift from standard deep networks, which do not overtly represent any uncertainty in its structure or output , Bayesian deep networks are being currently investigated where the network is envisaged as a set of plausible models learnt by the Bayes' formulation in response to uncertainties in sensory data.
Revised:  As a significant shift from standard deep networks, which do not overtly represent any uncertainty in its structure or output , Bayesian deep networks are being currently investigated where the network is envisaged as an ensemble of plausible models learnt by the Bayes' formulation in response to uncertainties in sensory data.
Edit intent: clarity
Original:  Bayesian deep networks formulate each synaptic weight as a sample drawn from a probability distribution with learnt mean and variance.
Revised:  Bayesian deep networks consider each synaptic weight as a sample drawn from a probability distribution with learnt mean and variance.
Edit intent: clarity
Original: We present a novel machine learning architecture for classification suggested by experiments on the insect olfactory system .
Revised: We present a novel machine learning architecture for classification suggested by experiments on olfactory systems .
Edit intent: clarity
Original:  We build this network using biophysical models of neurons with our results showing high discrimination among inputs and exceptional robustness to noise.
Revised:  We build this network using biophysical models of neurons and shows high discrimination among inputs and exceptional robustness to noise.
Edit intent: clarity
Original:  We build this network using biophysical models of neurons with our results showing high discrimination among inputs and exceptional robustness to noise.
Revised:  We build this network using biophysical models of neurons with our results showing high discrimination among inputs and robustness to noise.
Edit intent: clarity
Original:  The same circuitry accurately identifies the amplitudes of mixtures of the odors on which it has been trained .
Revised:  While using the SVM alone does not permit determination of the components of mixtures of classified inputs, the combined network is able to tell the precise concentrations of the constituent parts .
Edit intent: clarity
Original: Deep neural networks have achieved impressive performance and become  de-facto standard in many tasks.
Revised: Deep neural networks have achieved impressive performance and become the de-facto standard in many tasks.
Edit intent: fluency
Original:  However,  phenomena such as adversarial examples and fooling examples hint that the generalization they make is flawed.
Revised:  However, troubling phenomena such as adversarial examples and fooling examples hint that the generalization they make is flawed.
Edit intent: clarity
Original:  However,  phenomena such as adversarial examples and fooling examples hint that the generalization they make is flawed.
Revised:  However, phenomena such as adversarial and fooling examples hint that the generalization they make is flawed.
Edit intent: fluency
Original:  However,  phenomena such as adversarial examples and fooling examples hint that the generalization they make is flawed.
Revised:  However, phenomena such as adversarial examples and fooling examples suggest that the generalization they make is flawed.
Edit intent: clarity
Original: We initiate the study of multi-stage episodic reinforcement learning under adversarial manipulations in both the rewards and the transition probabilities of the underlying system .
Revised: We initiate the study of multi-stage episodic reinforcement learning under adversarial corruptions in both the rewards and the transition probabilities of the underlying system .
Edit intent: clarity
Original:  Existing efficient algorithms heavily rely on the "optimism under uncertainty" principle which dictates their behavior and does not allow flexibility to perform corruption-robust exploration.
Revised:  Existing efficient algorithms heavily rely on the "optimism in the face of uncertainty" principle which dictates their behavior and does not allow flexibility to perform corruption-robust exploration.
Edit intent: clarity
Original:  Our algorithm enjoys near-optimal guarantees in the absence of adversarial manipulations, has performance that degrades gracefully as the amount of corruption increases, and does not need to know this amount.
Revised:  Our algorithm enjoys near-optimal regret in the absence of adversarial manipulations, has performance that degrades gracefully as the amount of corruption increases, and does not need to know this amount.
Edit intent: clarity
Original:  Note that for unweighted Congestion Games on single-commodity series parallel networks with general latency functions,  a PNE can be computed by a simple greedy algorithm .
Revised:  Note that for unweighted Congestion Games on single-commodity series parallel networks with general latency functions, a simple greedy algorithm .
Edit intent: clarity
Original:  Meanwhile, an overseeing adversary observes the effects of the agent's past actions and tries to predict the goal .
Revised:  Meanwhile, an overseeing adversary observes the agent's past actions and tries to predict the goal .
Edit intent: clarity
Original:  Meanwhile, an overseeing adversary observes the effects of the agent's past actions and tries to predict the goal .
Revised:  Meanwhile, an overseeing adversary observes the effects of the agent's entire trajectory and tries to predict the goal .
Edit intent: clarity
Original:  Meanwhile, an overseeing adversary observes the effects of the agent's past actions and tries to predict the goal .
Revised:  Meanwhile, an overseeing adversary observes the effects of the agent's past actions and tries to identify her goal among those vertices traversed .
Edit intent: clarity
Original:  We are interested in understanding the probability that the adversary predicts the goal correctly (prediction risk) as a function of the time it takes the agent to reach her goal (delay).
Revised:  We are interested in understanding the probability that the adversary predicts the goal correctly (prediction risk) as a function of the time it takes the agent to reach it (delay).
Edit intent: clarity
Original:  First, we establish an asymptotically tight characterization  in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive  overhead for the agent, even as her delay  tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Revised:  We establish an asymptotically tight characterization in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive overhead for the agent, even as her delay tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Edit intent: coherence
Original:  First, we establish an asymptotically tight characterization  in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive  overhead for the agent, even as her delay  tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Revised:  First, we establish an asymptotically tight characterization in complete graphs, showing that (1) any level of intrinsic uncertainty will lead to a strictly positive overhead for the agent, even as her delay tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Edit intent: clarity
Original:  First, we establish an asymptotically tight characterization  in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive  overhead for the agent, even as her delay  tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Revised:  First, we establish an asymptotically tight characterization in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive delay overhead for the agent, even as her delay tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Edit intent: fluency
Original:  First, we establish an asymptotically tight characterization  in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive  overhead for the agent, even as her delay  tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Revised:  First, we establish an asymptotically tight characterization in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive overhead for the agent, even as her delay tends to infinity, and (2) a carefully designed decision policy the overhead can be only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Edit intent: clarity
Original:  First, we establish an asymptotically tight characterization  in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive  overhead for the agent, even as her delay  tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Revised:  First, we establish an asymptotically tight characterization in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive overhead for the agent, even as her delay tends to infinity, and (2) under a carefully designed routing policy can ensure the overhead is only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Edit intent: clarity
Original:  First, we establish an asymptotically tight characterization  in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive  overhead for the agent, even as her delay  tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the noise level , and thus incur an asymptotically negligible effect on system performance .
Revised:  First, we establish an asymptotically tight characterization in complete graphs, showing that (1) intrinsic uncertainty always leads to a strictly positive overhead for the agent, even as her delay tends to infinity, and (2) under a carefully designed decision policy the overhead can be only additive with respect to the level of the uncertainty .
Edit intent: clarity
Original:  We further apply these insights to studying network topologies generated by random graphs, and to designing private networks .
Revised:  We further extend the results to networks generated by random graphs, and to designing private networks .
Edit intent: clarity
Original:  We further apply these insights to studying network topologies generated by random graphs, and to designing private networks .
Revised:  We further apply these insights to studying network topologies generated by random graph models, and settings where the network structure can be designed .
Edit intent: clarity
Original:  In both cases, we show how to achieve an additive overhead even for relatively sparse, non-complete networks.
Revised:  In both cases, we show it is possible to achieve an additive overhead even for relatively sparse, non-complete networks.
Edit intent: clarity
Original:  Our analysis is centered around a new class of " noise-harvesting " agent strategies which adaptively combines intrinsic uncertainty with additional artificial randomization to achieve  efficient obfuscation.
Revised:  Our analysis is centered around a new class of " noise-harnessing " agent strategies which adaptively combines intrinsic uncertainty with additional artificial randomization to achieve efficient obfuscation.
Edit intent: fluency
Original:  Our analysis is centered around a new class of " noise-harvesting " agent strategies which adaptively combines intrinsic uncertainty with additional artificial randomization to achieve  efficient obfuscation.
Revised:  Our analysis is centered around a new class of " noise-harvesting " agent strategies which adaptively combines intrinsic uncertainty with additional artificial randomization to achieve provably efficient obfuscation.
Edit intent: clarity
Original: It is popular approaches to use surrogate models to speed up the computational procedure for Bayesian inverse problems (BIPs).
Revised: In Bayesian inverse problems, surrogate models are often constructed to speed up the computational procedure for Bayesian inverse problems (BIPs).
Edit intent: clarity
Original:  To this end, we present in this work an adaptive multi-fidelity  deep neural networks (DNNs) based surrogate modeling for large-scale BIPs , motivated by the facts that the DNNs can potentially handle functions with limited regularity and are powerful tools for high dimensional problems . More precisely, we begin with a low fidelity DNN-surrogate and thencorrect it adaptively using online high fidelity data.
Revised:  To this end, we present in this work an adaptive multi-fidelity surrogate modeling framework based on deep neural networks (DNNs) based surrogate modeling for large-scale BIPs , motivated by the facts that the DNNs can potentially handle functions with limited regularity and are powerful tools for high dimensional problems . More precisely, we begin with a low fidelity DNN-surrogate and thencorrect it adaptively using online high fidelity data.
Edit intent: clarity
Original:  To this end, we present in this work an adaptive multi-fidelity  deep neural networks (DNNs) based surrogate modeling for large-scale BIPs , motivated by the facts that the DNNs can potentially handle functions with limited regularity and are powerful tools for high dimensional problems . More precisely, we begin with a low fidelity DNN-surrogate and thencorrect it adaptively using online high fidelity data.
Revised:  To this end, we present in this work an adaptive multi-fidelity deep neural networks (DNNs) , motivated by the facts that the DNNs can potentially handle functions with limited regularity and are powerful tools for high dimensional problems . More precisely, we begin with a low fidelity DNN-surrogate and thencorrect it adaptively using online high fidelity data.
Edit intent: clarity
Original:  To this end, we present in this work an adaptive multi-fidelity  deep neural networks (DNNs) based surrogate modeling for large-scale BIPs , motivated by the facts that the DNNs can potentially handle functions with limited regularity and are powerful tools for high dimensional problems . More precisely, we begin with a low fidelity DNN-surrogate and thencorrect it adaptively using online high fidelity data.
Revised:  To this end, we present in this work an adaptive multi-fidelity deep neural networks (DNNs) based surrogate modeling for large-scale BIPs , motivated by the facts that the DNNs can potentially handle functions with limited regularity and are powerful tools for high dimensional approximations . More precisely, we begin with a low fidelity DNN-surrogate and thencorrect it adaptively using online high fidelity data.
Edit intent: clarity
Original:  The key idea is to view the low fidelity surrogate as an input variable into the DNN-surrogate of the next iteration -- yielding a composite DNN that combine two surrogates between two iterations.
Revised:  The key idea is to view the low fidelity surrogate as an input variable -- yielding a composite DNN that combine two surrogates between two iterations.
Edit intent: clarity
Original:  Numerical experiments confirm that the proposed approach can obtain accurate posterior information with  limited number of forward simulations.
Revised:  Numerical examples are presented to confirm that the proposed approach can obtain accurate posterior information with limited number of forward simulations.
Edit intent: clarity
Original:  Numerical experiments confirm that the proposed approach can obtain accurate posterior information with  limited number of forward simulations.
Revised:  Numerical experiments confirm that the proposed approach can obtain accurate posterior information with a limited number of forward simulations.
Edit intent: fluency
Original:  After pairwise comparing the spatial entities in the same block, we propose the SkyRank algorithm that ranks the compared pairs using Pareto optimality .
Revised:  After pairwise comparing the spatial entities in the same block, ranks the pairs using Pareto optimality .
Edit intent: clarity
Original:  We introduce the SkyEx-* family of algorithms that can classify the pairs with 0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of 777,452 pairs.
Revised:  We introduce the SkyEx-* family of algorithms that yield 0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of 777,452 pairs.
Edit intent: clarity
Original:  Finally, QuadSky provides the best trade-off between precision and recall  and the best F-measure compared to the existing baselines  .
Revised:  Finally, QuadSky provides the best trade-off between precision and recall , and the best F-measure compared to the existing baselines .
Edit intent: fluency
Original:  By leveraging the supreme performance , OCChas become a promising solution to meet the stringent requirements of vehicular communication to support ultra-reliable and low-latency communication (uRLLC)  .
Revised:  By leveraging the supreme performance of OCC, we can meet the stringent requirements of vehicular communication to support ultra-reliable and low-latency communication (uRLLC) .
Edit intent: clarity
Original:  By leveraging the supreme performance , OCChas become a promising solution to meet the stringent requirements of vehicular communication to support ultra-reliable and low-latency communication (uRLLC)  .
Revised:  By leveraging the supreme performance , OCChas become a promising solution to meet the stringent requirements of ultra-reliable and low-latency communication (uRLLC) .
Edit intent: clarity
Original:  In this paper, we introduce a novel approach of capacity maximization in vehicular OCC through the optimization of capacity, power allocation , and adaptive modulation schemes while guaranteeing reliability and latency requirements.
Revised:  In this paper, we introduce a rate optimization approach in vehicular OCC through the optimization of capacity, power allocation , and adaptive modulation schemes while guaranteeing reliability and latency requirements.
Edit intent: clarity
Original: Path Graphs are intersection graphs of paths in a tree.
Revised: Path graphs are intersection graphs of paths in a tree.
Edit intent: coherence
Original:  Path Graphs are closed under taking induced subgraphs and, answering a question posed by Renz P.L. Renz, Intersection Representations of Graphs by Arcs, Pacific J. Math., 34 (1970), 501-510%DIFDELCMD < ]%%% , L\'{ev\^{e}que, Maffray and Preissmann }%DIFDELCMD < [%%% B. L\'{ev\^{e}que, F. Maffray, and M. Preissmann, Characterizing Path Graphs by Forbidden Induced Subgraphs, J. Graph Theory, 62:
Revised:  P.L. Renz, Intersection Representations of Graphs by Arcs, Pacific J. Math., 34 (1970), 501-510%DIFDELCMD < ]%%% , L\'{ev\^{e}que, Maffray and Preissmann }%DIFDELCMD < [%%% B. L\'{ev\^{e}que, F. Maffray, and M. Preissmann, Characterizing Path Graphs by Forbidden Induced Subgraphs, J. Graph Theory, 62:
Edit intent: coherence
Original:  Path Graphs are closed under taking induced subgraphs and, answering a question posed by Renz P.L. Renz, Intersection Representations of Graphs by Arcs, Pacific J. Math., 34 (1970), 501-510%DIFDELCMD < ]%%% , L\'{ev\^{e}que, Maffray and Preissmann }%DIFDELCMD < [%%% B. L\'{ev\^{e}que, F. Maffray, and M. Preissmann, Characterizing Path Graphs by Forbidden Induced Subgraphs, J. Graph Theory, 62:
Revised:  Path Graphs are closed under taking induced subgraphs and, answering a question posed by Renz %DIFDELCMD < ]%%% , L\'{ev\^{e}que, Maffray and Preissmann }%DIFDELCMD < [%%% B. L\'{ev\^{e}que, F. Maffray, and M. Preissmann, Characterizing Path Graphs by Forbidden Induced Subgraphs, J. Graph Theory, 62:
Edit intent: coherence