wanyu/IteraTeR_v2 huggingface dataset:
Original:  We also employ a tensorization method to transform data to a higher-order  to enhance the performance of our algorithm.
Revised:  We also employ a tensorization method to transform data to a higher-order form to enhance the performance of our algorithm.
Edit intent: clarity
Original:  The image recovery experiment results in various cases manifest that our method outperforms other completion algorithms.
Revised:  The results of image recovery experiments in various cases manifest that our method outperforms other completion algorithms.
Edit intent: clarity
Original:  Especially when the missing rate is very high, e.g.  90 \% to 99\%, our method can achieve much better performance than other state-of-the-art \\%DIF > to 99\\%, our method is significantly better than the state-of-the-art methods.
Revised:  Especially when the missing rate is very high, e.g. , 90 \% to 99\%, our method can achieve much better performance than other state-of-the-art \\%DIF > to 99\\%, our method is significantly better than the state-of-the-art methods.
Edit intent: fluency
Original:  Especially when the missing rate is very high, e.g.  90 \% to 99\%, our method can achieve much better performance than other state-of-the-art \\%DIF > to 99\\%, our method is significantly better than the state-of-the-art methods.
Revised:  Especially when the missing rate is very high, e.g. 90 \\%DIF > to 99\\%, our method is significantly better than the state-of-the-art methods.
Edit intent: clarity
Original:  In this work, we define quantum lightning, a counter-intuitive strengthening of public key quantum money  , where no-cloning holds , even when the adversary herself generates the quantum state to be cloned.
Revised:  In this work, investigate quantum lightning, a counter-intuitive strengthening of public key quantum money , where no-cloning holds , even when the adversary herself generates the quantum state to be cloned.
Edit intent: clarity
Original:  In this work, we define quantum lightning, a counter-intuitive strengthening of public key quantum money  , where no-cloning holds , even when the adversary herself generates the quantum state to be cloned.
Revised:  In this work, we define quantum lightning, a counter-intuitive strengthening of public key quantum money , where no-cloning holds even when the adversary herself generates the quantum state to be cloned.
Edit intent: fluency
Original: This paper comments on "Asynchronous Logic Implementation Based on Factorized DIMS" [ Jour. of Circuits, Systems, and Computers, vol. 26, no. 5, 1750087 , pages 9, May 2017] with respect to two  problematic issues:
Revised: This paper comments on "Asynchronous Logic Implementation Based on Factorized DIMS" [ Journal of Circuits, Systems, and Computers, vol. 26, no. 5, 1750087 , pages 9, May 2017] with respect to two problematic issues:
Edit intent: fluency
Original: This paper comments on "Asynchronous Logic Implementation Based on Factorized DIMS" [ Jour. of Circuits, Systems, and Computers, vol. 26, no. 5, 1750087 , pages 9, May 2017] with respect to two  problematic issues:
Revised: This paper comments on "Asynchronous Logic Implementation Based on Factorized DIMS" [ Jour. of Circuits, Systems, and Computers, vol. 26, no. 5, 1750087 , pages 9, May 2017] with respect to two main problematic issues:
Edit intent: clarity
Original:  i) the gate orphan problem implicit in the factorized DIMS approach discussed in the referenced article which affects the strong-indication, and ii) how the enumeration of product terms to represent the synthesis cost is skewed in the referenced article because the logic expression contains sum of products and also product of sums.
Revised:  i) the gate orphan problem implicit in the factorized DIMS approach discussed in the referenced article which affects its strong-indication, and ii) how the enumeration of product terms to represent the synthesis cost is skewed in the referenced article because the logic expression contains sum of products and also product of sums.
Edit intent: fluency
Original:  It is observed that the referenced article has not provided a general logic synthesis algorithm excepting only an example illustration involving a 3-input AND gate function.
Revised:  It is observed that the referenced article has not provided a general logic synthesis algorithm excepting only an example illustration involving a 3-input AND logic function.
Edit intent: clarity
Original:  Moreover, the example illustration in the referenced article describes an unsafe logic decomposition which is not suitable for  strong-indication asynchronous circuit synthesis .
Revised:  Moreover, the example illustration in the referenced article describes an unsafe logic decomposition which is not suitable for strong-indication asynchronous circuits .
Edit intent: clarity
Original:  Further, a logic synthesis method which safely decomposes the DIMS solution to synthesize  strong-indication asynchronous circuits is already available in the existing literature, which was neither cited nor taken up for comparison in the referenced article, which is another drawback.
Revised:  Further, a logic synthesis method which safely decomposes the DIMS solution to synthesize strong-indication asynchronous circuits is available in the existing literature, which was neither cited nor taken up for comparison in the referenced article, which is another drawback.
Edit intent: coherence
Original:  Subsequently, it is concluded that the referenced article has not advanced existing knowledge in the field but  has caused confusions.
Revised:  Subsequently, it is concluded that the referenced article has not advanced existing knowledge in the field but on the contrary, has caused confusions.
Edit intent: coherence
Original:  Hence, in the interest of avid readers, this paper additionally highlights some important and relevant literature which provide valuable information about robust asynchronous circuit synthesis techniques which employ delay-insensitive codes for data representation and processing and the 4-phase return-to-zero handshake protocol for data communication.
Revised:  Hence, in the interest of readers, this paper additionally highlights some important and relevant literature which provide valuable information about robust asynchronous circuit synthesis techniques which employ delay-insensitive codes for data representation and processing and the 4-phase return-to-zero handshake protocol for data communication.
Edit intent: clarity
Original:  The field has evolved rapidly in recent years from traditional multi-arm bandit and collaborative filtering techniques, with new methods integrating Deep Learning models that enable to capture non-linear feature interactions . Despite progress, the dynamic nature of online recommendations still poses great challenges, such as finding the delicate balance between exploration and exploitation.
Revised:  The field has evolved rapidly in recent years from traditional multi-arm bandit and collaborative filtering techniques, with new methods employing Deep Learning models that enable to capture non-linear feature interactions . Despite progress, the dynamic nature of online recommendations still poses great challenges, such as finding the delicate balance between exploration and exploitation.
Edit intent: clarity
Original:  The field has evolved rapidly in recent years from traditional multi-arm bandit and collaborative filtering techniques, with new methods integrating Deep Learning models that enable to capture non-linear feature interactions . Despite progress, the dynamic nature of online recommendations still poses great challenges, such as finding the delicate balance between exploration and exploitation.
Revised:  The field has evolved rapidly in recent years from traditional multi-arm bandit and collaborative filtering techniques, with new methods integrating Deep Learning models to capture non-linearities . Despite progress, the dynamic nature of online recommendations still poses great challenges, such as finding the delicate balance between exploration and exploitation.
Edit intent: clarity
Original:  We show the usefulness of using DDN online in a real world content recommendation system that serves billions of recommendations per day, and present online and offline results to evaluate the benefit of using DDN  .
Revised:  We show the usefulness of using DDN online in a real world content recommendation system that serves billions of recommendations per day, and show the benefit of using DDN .
Edit intent: clarity
Original:  A significant number of algorithms and models have been proposed to learn knowledge from traffic data and improve the predictionaccuracy .
Revised:  A significant number of algorithms and models have been put forward to analyse traffic data and improve the predictionaccuracy .
Edit intent: clarity
Original:  A significant number of algorithms and models have been proposed to learn knowledge from traffic data and improve the predictionaccuracy .
Revised:  A significant number of algorithms and models have been proposed to learn knowledge from traffic data and make prediction .
Edit intent: clarity
Original:  In the recent big data era, the relevant research enthusiasm remains and deep learning has been exploited to extract the useful information in depth .
Revised:  In the recent big data era, deep learning has been exploited to extract the useful information in depth .
Edit intent: coherence
Original:  In the recent big data era, the relevant research enthusiasm remains and deep learning has been exploited to extract the useful information in depth .
Revised:  In the recent big data era, the relevant research enthusiasm remains and deep learning has been exploited to mine the profound information hidden in the data .
Edit intent: clarity
Original:  In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted significant attentions due to  the long-range dependency embedded in the sequential traffic data.
Revised:  In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to the long-range dependency embedded in the sequential traffic data.
Edit intent: clarity
Original:  In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted significant attentions due to  the long-range dependency embedded in the sequential traffic data.
Revised:  In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted significant attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data.
Edit intent: clarity
Original:  Compared to the conventional LSTM, RCLSTM achieves a significant breakthrough in the architecture formation of neural network, whose connectivity is determined in a stochastic manner rather than full connected.
Revised:  Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the architecture formation of neural network, whose connectivity is determined in a stochastic manner rather than full connected.
Edit intent: clarity
Original:  Compared to the conventional LSTM, RCLSTM achieves a significant breakthrough in the architecture formation of neural network, whose connectivity is determined in a stochastic manner rather than full connected.
Revised:  Compared to the conventional LSTM, RCLSTM achieves a significant breakthrough in the formation of neural network, whose connectivity is determined in a stochastic manner rather than full connected.
Edit intent: clarity
Original:  Compared to the conventional LSTM, RCLSTM achieves a significant breakthrough in the architecture formation of neural network, whose connectivity is determined in a stochastic manner rather than full connected.
Revised:  Compared to the conventional LSTM, RCLSTM achieves a significant breakthrough in the architecture formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected.
Edit intent: clarity
Original:  So, the neural network in RCLSTMcan exhibit certain sparsity, which means many neural connections are absent (distinguished from the full connectivity) and thus the number of parameters to be trained is reduced and much fewer computations are required .
Revised:  So, the RCLSTM, with certain intrinsic sparsity, have many neural connections are absent (distinguished from the full connectivity) and thus the number of parameters to be trained is reduced and much fewer computations are required .
Edit intent: clarity
Original:  So, the neural network in RCLSTMcan exhibit certain sparsity, which means many neural connections are absent (distinguished from the full connectivity) and thus the number of parameters to be trained is reduced and much fewer computations are required .
Revised:  So, the neural network in RCLSTMcan exhibit certain sparsity, which means many neural connections absent (distinguished from the full connectivity) and thus the number of parameters to be trained is reduced and much fewer computations are required .
Edit intent: fluency
Original:  So, the neural network in RCLSTMcan exhibit certain sparsity, which means many neural connections are absent (distinguished from the full connectivity) and thus the number of parameters to be trained is reduced and much fewer computations are required .
Revised:  So, the neural network in RCLSTMcan exhibit certain sparsity, which means many neural connections are absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained is reduced and much fewer computations are required .
Edit intent: clarity
Original:  So, the neural network in RCLSTMcan exhibit certain sparsity, which means many neural connections are absent (distinguished from the full connectivity) and thus the number of parameters to be trained is reduced and much fewer computations are required .
Revised:  So, the neural network in RCLSTMcan exhibit certain sparsity, which means many neural connections are absent (distinguished from the full connectivity) and thus the number of parameters to be trained and the computational cost .
Edit intent: clarity
Original:  We apply the RCLSTM solution to predict traffic and validate that the RCLSTM with even 35\% neural connectivity still shows a strong capability in traffic prediction.
Revised:  We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35\% neural connectivity still shows a strong capability in traffic prediction.
Edit intent: clarity
Original:  Also, along with increasing the number of training samples, the performance of RCLSTM becomes  closer to the conventional LSTM.
Revised:  Also, along with increasing the number of training samples, the performance of RCLSTM becomes increasingly closer to the conventional LSTM.
Edit intent: clarity
Original:  Also, along with increasing the number of training samples, the performance of RCLSTM becomes  closer to the conventional LSTM.
Revised:  Also, along with increasing the number of training samples, the performance of RCLSTM becomes closer to the baseline LSTM.
Edit intent: clarity
Original:  Moreover, the RCLSTM exhibits even superior prediction accuracy than the conventional LSTMwhen the length of input traffic sequences increases .
Revised:  Moreover, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM .
Edit intent: clarity
Original: = \sqrt{(1/|\mathcal{S|)\sum_{S \in S} \left(u_i \in S \chi(u_i)\right)^2}} |)\sum_{S \in S} \left(u_i \in S\chi(u_i)\right)^2}} . Breakthrough work by Bansal FOCS'10 gave a polynomial time algorithm, based on rounding an SDP, for finding a coloring \chi such that disc_\infty(\mathcal{S},\chi) = O(\lg n \cdot \textrm{herdisc}_\infty(\mathcal{S})) where \textrm{herdisc}_\infty(\mathcal{S}) is the hereditary \ell_\infty-discrepancy of \mathcal{S}.
Revised: = |)\sum_{S \in S} \left(u_i \in S \chi(u_i)\right)^2}} |)\sum_{S \in S} \left(u_i \in S\chi(u_i)\right)^2}} . Breakthrough work by Bansal FOCS'10 gave a polynomial time algorithm, based on rounding an SDP, for finding a coloring \chi such that disc_\infty(\mathcal{S},\chi) = O(\lg n \cdot \textrm{herdisc}_\infty(\mathcal{S})) where \textrm{herdisc}_\infty(\mathcal{S}) is the hereditary \ell_\infty-discrepancy of \mathcal{S}.
Edit intent: coherence
Original: = \sqrt{(1/|\mathcal{S|)\sum_{S \in S} \left(u_i \in S \chi(u_i)\right)^2}} |)\sum_{S \in S} \left(u_i \in S\chi(u_i)\right)^2}} . Breakthrough work by Bansal FOCS'10 gave a polynomial time algorithm, based on rounding an SDP, for finding a coloring \chi such that disc_\infty(\mathcal{S},\chi) = O(\lg n \cdot \textrm{herdisc}_\infty(\mathcal{S})) where \textrm{herdisc}_\infty(\mathcal{S}) is the hereditary \ell_\infty-discrepancy of \mathcal{S}.
Revised: = \sqrt{(1/|\mathcal{S|)\sum_{S \in S} \left(u_i \in S \chi(u_i)\right)^2}} \sqrt{(1/|\mathcal{S|)\sum_{S \in S} \left(u_i \in S\chi(u_i)\right)^2}} . Breakthrough work by Bansal FOCS'10 gave a polynomial time algorithm, based on rounding an SDP, for finding a coloring \chi such that disc_\infty(\mathcal{S},\chi) = O(\lg n \cdot \textrm{herdisc}_\infty(\mathcal{S})) where \textrm{herdisc}_\infty(\mathcal{S}) is the hereditary \ell_\infty-discrepancy of \mathcal{S}.
Edit intent: coherence
Original:  Our inequalities greatly improve over previous work by Chazelle and Lvov SCG'00 and we believe they are of independent interest as powerful tools for proving hereditary discrepancy lower bounds.
Revised:  Our inequalities improve over previous work by Chazelle and Lvov SCG'00 and we believe they are of independent interest as powerful tools for proving hereditary discrepancy lower bounds.
Edit intent: clarity
Original:  Our inequalities greatly improve over previous work by Chazelle and Lvov SCG'00 and we believe they are of independent interest as powerful tools for proving hereditary discrepancy lower bounds.
Revised:  Our inequalities greatly improve over previous work by Chazelle and Lvov and we believe they are of independent interest as powerful tools for proving hereditary discrepancy lower bounds.
Edit intent: clarity
Original:  We complement his work by giving a clean and simple O((m+n)n^2) time algorithm for finding a coloring \chi such \textrm{disc}_2(\mathcal{S},\chi) = O(\sqrt{\lg n} \cdot \textrm{herdisc}_2(\mathcal{S})) where \textrm{herdisc}_2(\mathcal{S}) is the hereditary \ell_2-discrepancy of \mathcal{S}.
Revised:  We complement his work by giving a simple O((m+n)n^2) time algorithm for finding a coloring \chi such \textrm{disc}_2(\mathcal{S},\chi) = O(\sqrt{\lg n} \cdot \textrm{herdisc}_2(\mathcal{S})) where \textrm{herdisc}_2(\mathcal{S}) is the hereditary \ell_2-discrepancy of \mathcal{S}.
Edit intent: clarity
Original:  Interestingly, our algorithm avoids solving an SDP and instead relies simply on computing eigendecompositions of matrices.
Revised:  Interestingly, our algorithm avoids solving an SDP and instead relies on computing eigendecompositions of matrices.
Edit intent: clarity
Original:   ] To prove that our algorithm has the claimed guarantees, we also prove new inequalities relating both \textrm{herdisc}_\infty and \textrm{herdisc}_2 to the eigenvalues of the incidence matrix corresponding to \mathcal{S}.
Revised:  ]. To prove that our algorithm has the claimed guarantees, we also prove new inequalities relating both \textrm{herdisc}_\infty and \textrm{herdisc}_2 to the eigenvalues of the incidence matrix corresponding to \mathcal{S}.
Edit intent: fluency
Original:   ] To prove that our algorithm has the claimed guarantees, we also prove new inequalities relating both \textrm{herdisc}_\infty and \textrm{herdisc}_2 to the eigenvalues of the incidence matrix corresponding to \mathcal{S}.
Revised:  ] To prove that our algorithm has the claimed guarantees, we show new inequalities relating both \textrm{herdisc}_\infty and \textrm{herdisc}_2 to the eigenvalues of the incidence matrix corresponding to \mathcal{S}.
Edit intent: clarity
Original:   ] To prove that our algorithm has the claimed guarantees, we also prove new inequalities relating both \textrm{herdisc}_\infty and \textrm{herdisc}_2 to the eigenvalues of the incidence matrix corresponding to \mathcal{S}.
Revised:  ] To prove that our algorithm has the claimed guarantees, we also prove new inequalities relating \textrm{herdisc}_\infty and \textrm{herdisc}_2 to the eigenvalues of the incidence matrix corresponding to \mathcal{S}.
Edit intent: clarity
Original:   ] To prove that our algorithm has the claimed guarantees, we also prove new inequalities relating both \textrm{herdisc}_\infty and \textrm{herdisc}_2 to the eigenvalues of the incidence matrix corresponding to \mathcal{S}.
Revised:  ] To prove that our algorithm has the claimed guarantees, we also prove new inequalities relating both \textrm{herdisc}_\infty and \textrm{herdisc}_2 to the eigenvalues of the matrix corresponding to \mathcal{S}.
Edit intent: clarity
Original:  Our inequalities improve over previous work by Chazelle and Lvov, and by Matousek , Nikolov and Talwar .
Revised:  Our inequalities improve over previous work by Chazelle and Lvov, and by Matousek et al .
Edit intent: clarity
Original:  Finally, we also implement our algorithm and show that it far outperforms random sampling of colorings in practice .
Revised:  Finally, we also implement our algorithm and show that it far outperforms random sampling .
Edit intent: clarity
Original: In this work, we consider a class of wireless powered devices employing Hybrid Automatic Repeat reQuest (HARQ) to ensure reliable end-to-end communications over a  time-varying channel.
Revised: We consider a class of wireless powered devices employing Hybrid Automatic Repeat reQuest (HARQ) to ensure reliable end-to-end communications over a time-varying channel.
Edit intent: clarity
Original:  We cast the problem in a Markovian framework, and we show that the optimal policy minimizing the expected number of re-transmissions utilizes the incoming RF signal to either exclusively harvest energy or to accumulate mutual information.
Revised:  We cast the problem in a Markovian framework, and we show that there exists an optimal policy utilizing the incoming RF signal to either exclusively harvest energy or to accumulate mutual information.
Edit intent: clarity
Original:  We cast the problem in a Markovian framework, and we show that the optimal policy minimizing the expected number of re-transmissions utilizes the incoming RF signal to either exclusively harvest energy or to accumulate mutual information.
Revised:  We cast the problem in a Markovian framework, and we show that the optimal policy minimizing the expected number of re-transmissions utilizes the incoming RF signal solely to harvest energy or to accumulate mutual information.
Edit intent: clarity
Original:  For independent and identically distributed channels, we also prove that practically simple-to-implement policies such as {\em harvest-first-store-later type of policies are optimal .
Revised:  For independent and identically distributed channels, we prove the optimality of a simple-to-implement policies such as {\em harvest-first-store-later type of policies are optimal .
Edit intent: clarity
Original:  For independent and identically distributed channels, we also prove that practically simple-to-implement policies such as {\em harvest-first-store-later type of policies are optimal .
Revised:  For independent and identically distributed channels, we also prove that practically simple-to-implement {\em harvest-first-store-later type of policies are optimal .
Edit intent: clarity
Original:  For independent and identically distributed channels, we also prove that practically simple-to-implement policies such as {\em harvest-first-store-later type of policies are optimal .
Revised:  For independent and identically distributed channels, we also prove that practically simple-to-implement policies such as {\em harvest-first-store-later type policy .
Edit intent: clarity