wanyu/IteraTeR_v2 huggingface dataset:
Original:  By providing theoretical rationale, we demonstrate that the broad inverted-pyramid structure of existing deep-learning based steganalyzers contradicts the well-established model diversity oriented philosophy and is not suitable for steganalysis.
Revised:  By providing theoretical rationale, we demonstrate that the broad inverted-pyramid structure of existing deep-learning based steganalyzers might contradict the well-established model diversity oriented philosophy and is not suitable for steganalysis.
Edit intent: clarity
Original:  By providing theoretical rationale, we demonstrate that the broad inverted-pyramid structure of existing deep-learning based steganalyzers contradicts the well-established model diversity oriented philosophy and is not suitable for steganalysis.
Revised:  By providing theoretical rationale, we demonstrate that the broad inverted-pyramid structure of existing deep-learning based steganalyzers contradicts the well-established model diversity oriented philosophy , and therefore is not suitable for steganalysis.
Edit intent: coherence
Original:  The experimental results show that the model structure generated by our proposed CALPA-NET can achieve comparative detection performance with less than two percent of parameters and about one third FLOPs compared to the original steganalytic model.
Revised:  The experimental results show that the model structure generated by our proposed CALPA-NET can achieve comparative performance with less than two percent of parameters and about one third FLOPs compared to the original steganalytic model.
Edit intent: clarity
Original:  Finding an algorithm better than the straightforward greedy strategy remained a major open question for a long time until a recent negative result by Gamlath et al. (forthcoming FOCS 2019 ) , who showed that no online algorithm has a competitive ratio better than 0.5 in the worst case .
Revised:  Finding an algorithm better than the straightforward greedy strategy remained a major open question for a long time until a recent negative result by Gamlath et al. FOCS 2019 ) , who showed that no online algorithm has a competitive ratio better than 0.5 in the worst case .
Edit intent: clarity
Original:  Finding an algorithm better than the straightforward greedy strategy remained a major open question for a long time until a recent negative result by Gamlath et al. (forthcoming FOCS 2019 ) , who showed that no online algorithm has a competitive ratio better than 0.5 in the worst case .
Revised:  Finding an algorithm better than the straightforward greedy strategy remained a major open question for a long time until a recent negative result by Gamlath et al. (forthcoming FOCS 2019 , who showed that no online algorithm has a competitive ratio better than 0.5 in the worst case .
Edit intent: fluency
Original:  Finding an algorithm better than the straightforward greedy strategy remained a major open question for a long time until a recent negative result by Gamlath et al. (forthcoming FOCS 2019 ) , who showed that no online algorithm has a competitive ratio better than 0.5 in the worst case .
Revised:  Finding an algorithm better than the straightforward greedy strategy remained a major open question for a long time until a recent negative result by Gamlath et al. (forthcoming FOCS 2019 ) , who showed that no online policy is better than the straightforward greedy algorithm, i.e., no online algorithm has a worst-case competitive ratio better than 0.5 in the worst case .
Edit intent: clarity
Original:  Finding an algorithm better than the straightforward greedy strategy remained a major open question for a long time until a recent negative result by Gamlath et al. (forthcoming FOCS 2019 ) , who showed that no online algorithm has a competitive ratio better than 0.5 in the worst case .
Revised:  Finding an algorithm better than the straightforward greedy strategy remained a major open question for a long time until a recent negative result by Gamlath et al. (forthcoming FOCS 2019 ) , who showed that no online algorithm has a competitive ratio better than 0.5 .
Edit intent: clarity
Original:  We give a supplementary upper bound of 2{3 &   a 2-regular stochastic subgraph, then a natural prune greedy algorithm matches at least 0.532-fraction of all vertices .
Revised:  We give a supplementary upper bound of 2{3 & a 2-regular stochastic greedy algorithm matches at least 0.532-fraction of all vertices .
Edit intent: clarity
Original:  Extensive experiments, including offline evaluations and online A/B testing on real production systems, demonstrate the effectiveness and efficiency of FLEN against the state-of-thearts . Notably, FLEN has obtained 5.19\% improvement on CTR with 1/6 of memory usage and computation time, compared to last version (i.e. NFM).
Revised:  Extensive experiments, including offline evaluations and online A/B testing on real production systems, demonstrate the effectiveness and efficiency of FLEN against the state-of-the-arts . Notably, FLEN has obtained 5.19\% improvement on CTR with 1/6 of memory usage and computation time, compared to last version (i.e. NFM).
Edit intent: fluency
Original:  While the mechanism and properties of conformational change in the model have been investigated and several reduced order models developed, the  dynamics as a function of the length of the oscillator chain is relatively less clear.
Revised:  While the mechanism and properties of conformational change in the model have been investigated and several reduced order models developed, the conformational dynamics as a function of the length of the oscillator chain is relatively less clear.
Edit intent: fluency
Original:  These results should help to understand the role that the length of a DNA molecule plays in influencing its conformational dynamics.
Revised:  These results should help to understand the role of the length of a DNA molecule plays in influencing its conformational dynamics.
Edit intent: fluency
Original:  These results should help to understand the role that the length of a DNA molecule plays in influencing its conformational dynamics.
Revised:  These results should help to understand the role that the length of a DNA molecule in influencing its conformational dynamics.
Edit intent: clarity
Original: Recently very deep transformers start showing outperformed performance to traditional bi-directional long short-term memory networks by a large margin  .
Revised: Recently very deep transformers have outperformed conventional bi-directional long short-term memory networks by a large margin .
Edit intent: clarity
Original:  However, to put it into production usage, inference computation cost and latency are still serious concerns in real scenarios.
Revised:  However, to put it into production usage, inference computation cost is still a serious concern in real scenarios.
Edit intent: clarity
Original:  In this paper, we study a novel non-autoregressive transformers structure for speech recognition, which is originally introduced in machine translation . During training  input tokens fed to the decoder are randomly replaced by a special mask token .
Revised:  In this paper, we study a novel non-autoregressive transformers structure for speech recognition, which is originally introduced in machine translation . During training input tokens fed to the decoder are randomly replaced by special mask tokens .
Edit intent: fluency
Original:  The network is required to predict  those mask tokens by taking both  context and input speech into consideration.
Revised:  The network is required to predict the tokens corresponding to those mask tokens by taking both context and input speech into consideration.
Edit intent: clarity
Original:  During inference, we start from all mask tokens and the network gradually predicts all tokens based on partial results.
Revised:  During inference, we start from all mask tokens and the network iteratively predicts missing tokens based on partial results.
Edit intent: clarity
Original:  We show  this framework can support different decoding strategies, including traditional left-to-right.
Revised:  We show that this framework can support different decoding strategies, including traditional left-to-right.
Edit intent: fluency
Original:  A new decoding strategy is proposed as an example, which starts from the easiest predictions to  difficult ones.
Revised:  A new decoding strategy is proposed as an example, which starts from the easiest predictions to the most difficult ones.
Edit intent: clarity
Original:  Some preliminary results on Aishell and CSJ benchmarks show the possibility to train such a non-autoregressive network for ASR. Especially in Aishell, the proposed method outperformed Kaldi nnet3 and chain model setup and is quite closed to the performance of the start-of-the-art end-to-end model .
Revised:  Some preliminary results on Aishell and CSJ benchmarks show the possibility to train such a non-autoregressive network for ASR. Especially in Aishell, the proposed method outperformed the Kaldi ASR system and it matches the performance of the start-of-the-art end-to-end model .
Edit intent: clarity
Original: The flourishing assessments of fairness measure in machine learning algorithms have shown that dimension reduction methods such as PCA  treat data from different sensitive groups unfairly.
Revised: It has been shown that dimension reduction methods such as PCA treat data from different sensitive groups unfairly.
Edit intent: coherence
Original:  In this paper, in pursuit of achieving fairness criteria in PCA, we introduce a more efficient notion of Pareto fairness, cast the Pareto fair dimensionality reduction as a multi-objective optimization problem, and propose an adaptive gradient-based algorithm to solve it.
Revised:  In this paper, in pursuit of achieving fairness criteria in PCA, we introduce a more efficient notion of Pareto optimality, we propose an adaptive gradient-based algorithm to solve it.
Edit intent: coherence
Original:  Using the notion of Pareto optimality, we can guarantee that the solution of our proposed algorithm belongs to the Pareto frontier for all groups, which achieves the optimal trade-off between those aforementioned conflicting objectives.
Revised:  Using the notion of Pareto optimality, we can guarantee that the solution of the proposed algorithm belongs to the Pareto frontier for all groups, which achieves the optimal trade-off between those aforementioned conflicting objectives.
Edit intent: fluency
Original:  Using the notion of Pareto optimality, we can guarantee that the solution of our proposed algorithm belongs to the Pareto frontier for all groups, which achieves the optimal trade-off between those aforementioned conflicting objectives.
Revised:  Using the notion of Pareto optimality, we can guarantee that the solution of our proposed algorithm belongs to the Pareto frontier for all sensitive groups; thereby, the optimal trade-off between those aforementioned conflicting objectives.
Edit intent: coherence
Original:  We provide convergence analysis of our algorithm for both convex and non-convex objectives and show its efficacy through empirical studies on different datasets,  in comparison with the state-of-the-art algorithm .
Revised:  We provide convergence analysis of our algorithm and show its efficacy through empirical studies on different datasets, in comparison with the state-of-the-art algorithm .
Edit intent: clarity
Original:  We provide convergence analysis of our algorithm for both convex and non-convex objectives and show its efficacy through empirical studies on different datasets,  in comparison with the state-of-the-art algorithm .
Revised:  We provide convergence analysis of our algorithm for both convex and non-convex objectives and show its efficacy through empirical studies on different datasets, in comparison with state-of-the-art algorithm .
Edit intent: fluency
Original:  We show that under this bijection , the Stasheff-Tamari orders  naturally translate to subgraph inclusion  .
Revised:  We show that under this bijection the Stasheff-Tamari orders naturally translate to subgraph inclusion .
Edit intent: fluency
Original:  Moreover, we describe a connection to the second higher Bruhat order B( n-2 , 2).
Revised:  Moreover, we describe a connection to the second higher Bruhat order B( n , 2).
Edit intent: clarity
Original:  We also give an algorithm to efficiently enumerate all persistent graphs on n-2 vertices and thus all triangulations of C(n  , 3).
Revised:  We additionally give an algorithm to efficiently enumerate all persistent graphs on n-2 vertices and thus all triangulations of C(n , 3).
Edit intent: fluency
Original: Given two square matrices A and B, we propose a new algorithm for computing the smallest value \varepsilon \geq 0 such that A+E and A+F share an eigenvalue, where \|E\|=\|F\|=\varepsilon.
Revised: Given two square matrices A and B, we propose a new approach for computing the smallest value \varepsilon \geq 0 such that A+E and A+F share an eigenvalue, where \|E\|=\|F\|=\varepsilon.
Edit intent: clarity
Original:  Furthermore, our algorithm consists of computations which are also embarrassingly parallel , and so  can be further accelerated on multi-core hardware.
Revised:  Moreover, our method consists of many "embarrassingly parallel" computations , and so can be further accelerated on multi-core hardware.
Edit intent: clarity
Original:  Furthermore, our algorithm consists of computations which are also embarrassingly parallel , and so  can be further accelerated on multi-core hardware.
Revised:  Furthermore, our algorithm consists of computations which are also embarrassingly parallel , and so it can be further accelerated on multi-core hardware.
Edit intent: clarity
Original:  Our new algorithm can be orders of magnitude faster and can scale up to problems where A and B are of moderate size.
Revised:  Our new algorithm can be orders of magnitude faster and can solve problems where A and B are of moderate size.
Edit intent: clarity
Original:  HyPar-Flow exposes a simple API to offer data, model, and hybrid (model + data) parallel training for models defined using the Keras API.
Revised:  HyPar-Flow provides a single API that can be used to perform data, model, and hybrid (model + data) parallel training for models defined using the Keras API.
Edit intent: clarity
Original:  HyPar-Flow provides 110x speed up on 128 nodes of the Stampede2 cluster at TACC for hybrid-parallel training of ResNet-1001 .
Revised:  HyPar-Flow provides 110x speed up on 128 Stampede2 cluster at TACC for hybrid-parallel training of ResNet-1001 .
Edit intent: clarity
Original:   IP packets with incorrect source addresses would help to improve the situation.
Revised:  Detecting IP packets with incorrect source addresses would help to improve the situation.
Edit intent: clarity
Original:  Unfortunately, our analysis shows that the current state of art does introduce a methodologythat does not comply with common real-world deployment .
Revised:  Unfortunately, our further analysis reveals structural problems of the state of the art methodology, which are not easy to overcome .
Edit intent: clarity
Original: We apply the power-of-two-choices paradigm to  random walk on a graph: rather than moving to a uniform random neighbour at each step, a controller is allowed to choose from two independent uniform random neighbours.
Revised: We apply the power-of-two-choices paradigm to a random walk on a graph: rather than moving to a uniform random neighbour at each step, a controller is allowed to choose from two independent uniform random neighbours.
Edit intent: fluency
Original:  In particular, we show that the cover time becomes linear in the number n of vertices on discrete tori and bounded degree trees, of order O(n \log \log n) on  expanders, and of order O(n (\log \log n)^2) on the Erdos-R\'{e}nyi random graph in a certain sparsely connected regime.
Revised:  In particular, we show that the cover time becomes linear in the number n of vertices on discrete tori and bounded degree trees, of order O(n \log \log n) on bounded degree expanders, and of order O(n (\log \log n)^2) on the Erdos-R\'{e}nyi random graph in a certain sparsely connected regime.
Edit intent: fluency
Original:  Likewise, data set size has a direct impact of model predictivityindependently of comprehensive hyperparameter model tuning.
Revised:  Likewise, dataset size has a direct impact of model predictivityindependently of comprehensive hyperparameter model tuning.
Edit intent: clarity
Original:  Likewise, data set size has a direct impact of model predictivityindependently of comprehensive hyperparameter model tuning.
Revised:  Likewise, data set size has a direct impact on model predictivity, independent of comprehensive hyperparameter model tuning.
Edit intent: fluency
Original:  DeepChem uncertainty quantification (UQ) analysis may help identify model error;
Revised:  Lastly, we found that uncertainty quantification (UQ) analysis may help identify model error;
Edit intent: coherence
Original:  An efficient output error estimator plays a key role in the adaptive scheme.
Revised:  An efficient output error indicator plays a key role in the adaptive scheme.
Edit intent: clarity
Original:  We also propose an improved output error estimator based on previous work.
Revised:  We also propose an improved output error indicator based on previous work.
Edit intent: clarity
Original:  Upon convergence of the POD-Greedy algorithm, the new error estimator is shown to be sharper than the existing ones, implicating that a more reliable reduced-order model can be constructed.
Revised:  Upon convergence of the POD-Greedy algorithm, the new error indicator is shown to be sharper than the existing ones, implicating that a more reliable reduced-order model can be constructed.
Edit intent: clarity
Original:  We show that this criterion extends standard cross-entropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn-Knopp algorithm.
Revised:  We show that this criterion extends standard crossentropy minimization to an optimal transport problem, which we solve efficiently for millions of input images and thousands of labels using a fast variant of the Sinkhorn-Knopp algorithm.
Edit intent: fluency
Original: Self-organized bistability  is the counterpart of " self-organized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Revised: Self-organized bistability is the counterpart of ' self-organized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Edit intent: fluency
Original: Self-organized bistability  is the counterpart of " self-organized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Revised: Self-organized bistability is the counterpart of " self-organized criticality ' (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Edit intent: fluency
Original: Self-organized bistability  is the counterpart of " self-organized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Revised: Self-organized bistability is the counterpart of " self-organized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous phase transition, rather than to the critical point of a continuous /second order one.
Edit intent: clarity
Original: Self-organized bistability  is the counterpart of " self-organized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Revised: Self-organized bistability is the counterpart of " self-organized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous one.
Edit intent: clarity
Original:  The equations defining the  theory of SOB turn out to be very similar to a mesoscopic (Landau-Ginzburg) theory recently proposed to describe the dynamics in the cerebral cortex.
Revised:  The equations defining the theory of SOB turn out to bear strong resemblance to a (Landau-Ginzburg) theory recently proposed to describe the dynamics in the cerebral cortex.
Edit intent: clarity
Original:  The equations defining the  theory of SOB turn out to be very similar to a mesoscopic (Landau-Ginzburg) theory recently proposed to describe the dynamics in the cerebral cortex.
Revised:  The equations defining the theory of SOB turn out to be very similar to a mesoscopic (Landau-Ginzburg) theory recently proposed to analyze the dynamics of the cerebral cortex.
Edit intent: clarity
Original:  This theory describes the bistable/oscillating neuronal activity of coupled mesoscopic patches of cortex, homeostatically regulated by short-term synaptic plasticity.
Revised:  This theory describes the neuronal activity of coupled mesoscopic patches of cortex, homeostatically regulated by short-term synaptic plasticity.
Edit intent: clarity
Original:  However, the theory for cortex dynamics entails  significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former  there is a parameter that controls the overall system state (in blatant contrast with the very idea of self-organization).
Revised:  The theory for cortex dynamics entails significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former there is a parameter that controls the overall system state (in blatant contrast with the very idea of self-organization).
Edit intent: coherence
Original:  However, the theory for cortex dynamics entails  significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former  there is a parameter that controls the overall system state (in blatant contrast with the very idea of self-organization).
Revised:  However, the theory for cortex dynamics entails , however, some significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former there is a parameter that controls the overall system state (in blatant contrast with the very idea of self-organization).
Edit intent: coherence
Original:  However, the theory for cortex dynamics entails  significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales between driving anddissipation and , the fact that in the former  there is a parameter that controls the overall system state (in blatant contrast with the very idea of self-organization).
Revised:  However, the theory for cortex dynamics entails significant differences with respect to SOB, including the lack of a (bulk) conservation law, the absence of a perfect separation of timescales and , the fact that in the former there is a parameter that controls the overall system state (in blatant contrast with the very idea of self-organization).
Edit intent: clarity