wanyu/IteraTeR_v2 huggingface dataset:
Original: Nevertheless, the identical relevance assumption does not allow analyzing systems where some vertices represent strategic physical locations (e.g. hospitals, administrative building, schools, water and energy sources, etc.) or representative persons (e.g. presidents, prime ministers, intellectuals, etc.) for which the relevance cannot be derived from the connectivity network structure without embedding the information about the vertex intrinsic relevance .
Revised: Nevertheless, the identical relevance assumption does not allow analyzing systems where some vertices represent strategic physical locations (e.g. hospitals, administrative building, schools, water and energy sources, etc.) or representative persons (e.g. presidents, prime ministers, intellectuals, etc.) for which the relevance cannot be derived from the connectivity network structure and vertex intrinsic relevance .
Edit intent: clarity
Original:  However, these approaches have not been investigated for separating mixtures of arbitrary sounds of different types, a task we refer to as universal sound separation, and it is unknown whether performance on speech tasks carries over to non-speech tasks.
Revised:  However, these approaches have not been investigated for separating mixtures of arbitrary sounds of different types, a task we refer to as universal sound separation, and it is unknown how performance on speech tasks carries over to non-speech tasks.
Edit intent: clarity
Original:  To study this question, we develop a universal dataset of mixtures containing arbitrary sounds, and use it to investigate the space of mask-based separation architectures, varying both the overall network architecture and the framewise analysis-synthesis basis for signal transformations.
Revised:  To study this question, we develop a dataset of mixtures containing arbitrary sounds, and use it to investigate the space of mask-based separation architectures, varying both the overall network architecture and the framewise analysis-synthesis basis for signal transformations.
Edit intent: clarity
Original:  In terms of the framewise analysis-synthesis basis, we explore using either a short-time Fourier transform (STFT) or a learnable basis, as used in ConvTasNet , and for both of these bases, we  examine the effect of window size.
Revised:  In terms of the framewise analysis-synthesis basis, we explore both a short-time Fourier transform (STFT) or a learnable basis, as used in ConvTasNet , and for both of these bases, we examine the effect of window size.
Edit intent: fluency
Original:  In terms of the framewise analysis-synthesis basis, we explore using either a short-time Fourier transform (STFT) or a learnable basis, as used in ConvTasNet , and for both of these bases, we  examine the effect of window size.
Revised:  In terms of the framewise analysis-synthesis basis, we explore using either a short-time Fourier transform (STFT) and a learnable basis, as used in ConvTasNet , and for both of these bases, we examine the effect of window size.
Edit intent: fluency
Original:  In terms of the framewise analysis-synthesis basis, we explore using either a short-time Fourier transform (STFT) or a learnable basis, as used in ConvTasNet , and for both of these bases, we  examine the effect of window size.
Revised:  In terms of the framewise analysis-synthesis basis, we explore using either a short-time Fourier transform (STFT) or a learnable basis, as used in ConvTasNet . For both of these bases, we examine the effect of window size.
Edit intent: coherence
Original:  In terms of the framewise analysis-synthesis basis, we explore using either a short-time Fourier transform (STFT) or a learnable basis, as used in ConvTasNet , and for both of these bases, we  examine the effect of window size.
Revised:  In terms of the framewise analysis-synthesis basis, we explore using either a short-time Fourier transform (STFT) or a learnable basis, as used in ConvTasNet , and for both of these bases, we also examine the effect of window size.
Edit intent: clarity
Original:  Then, based on this IFP framework, the distributed algorithms are studied over directed and uniformly jointly strongly connected (UJSC) weight-balanced topologies , and convergence conditions of a suitable coupling gain are derived for the IFP-based algorithm .
Revised:  Then, based on this IFP framework, convergence conditions of a suitable coupling gain are derived for the IFP-based algorithm over directed and uniformly jointly strongly connected (UJSC) weight-balanced topologies , and convergence conditions of a suitable coupling gain are derived for the IFP-based algorithm .
Edit intent: clarity
Original:  While most works for directed topologies require the knowledge of the smallest nonzero eigenvalue of the graph Laplacian, the passivated algorithm is independent of any graph information and robust over UJSC weight-balanced digraphs with any positive coupling gain  .
Revised:  While most works on directed topologies require the knowledge of the smallest nonzero eigenvalue of the graph Laplacian, the passivated algorithm is independent of any graph information and robust over UJSC weight-balanced digraphs with any positive coupling gain .
Edit intent: fluency
Original:  It is shown that the error system of the proposed algorithm can be interpreted as output feedback interconnections of a group of  input feedforward passive (IFP) systems  . Based on this IFP framework, convergence conditions of a suitable coupling gain are derived for the IFP-based algorithm over directed and uniformly jointly strongly connected (UJSC) weight-balanced topologies.
Revised:  It is shown that the error system of the proposed algorithm can be decomposed into a group of input feedforward passive (IFP) systems . Based on this IFP framework, convergence conditions of a suitable coupling gain are derived for the IFP-based algorithm over directed and uniformly jointly strongly connected (UJSC) weight-balanced topologies.
Edit intent: clarity
Original:  It is shown that the error system of the proposed algorithm can be interpreted as output feedback interconnections of a group of  input feedforward passive (IFP) systems  . Based on this IFP framework, convergence conditions of a suitable coupling gain are derived for the IFP-based algorithm over directed and uniformly jointly strongly connected (UJSC) weight-balanced topologies.
Revised:  It is shown that the error system of the proposed algorithm can be interpreted as output feedback interconnections of a group of input feedforward passive (IFP) systems . Based on this IFP framework, convergence conditions of a suitable coupling gain are derived over weight-balanced and uniformly jointly strongly connected (UJSC) weight-balanced topologies.
Edit intent: clarity
Original:  While most works on directed topologies require the knowledge of the smallest nonzero eigenvalue of  the graph Laplacian, the derivative feedback algorithm is fully distributed, namely, it is robust against randomly changing weight-balanced digraphs with any positive coupling gain and without knowing any graph information.
Revised:  While most works on directed topologies require knowledge of the graph Laplacian, the derivative feedback algorithm is fully distributed, namely, it is robust against randomly changing weight-balanced digraphs with any positive coupling gain and without knowing any graph information.
Edit intent: clarity
Original:  While most works on directed topologies require the knowledge of the smallest nonzero eigenvalue of  the graph Laplacian, the derivative feedback algorithm is fully distributed, namely, it is robust against randomly changing weight-balanced digraphs with any positive coupling gain and without knowing any graph information.
Revised:  While most works on directed topologies require the knowledge of the smallest nonzero eigenvalue of the graph Laplacian, the derivative feedback algorithm is fully distributed, namely, it is robust against randomly changing weight-balanced digraphs with any positive coupling gain and without knowing any global information.
Edit intent: clarity
Original:  Finally, numerical examples are presented to demonstrate the proposed distributed algorithms.
Revised:  Finally, numerical examples are presented to illustrate the proposed distributed algorithms.
Edit intent: fluency
Original:  We demonstrate that our methods have (almost) linear observed theoretical complexity.
Revised:  We demonstrate that our methods have (almost) linear observed complexity.
Edit intent: clarity
Original:  Reinforcement learning  has been introduced to design the autonomous packet routing policy namely Q-routing only using local information available to each router .
Revised:  Reinforcement learning (RL) has been introduced to design the autonomous packet routing policy namely Q-routing only using local information available to each router .
Edit intent: clarity
Original:  Reinforcement learning  has been introduced to design the autonomous packet routing policy namely Q-routing only using local information available to each router .
Revised:  Reinforcement learning has been introduced to design autonomous packet routing policy namely Q-routing only using local information available to each router .
Edit intent: fluency
Original:  Inspired by recent success of deep reinforcement learning (DRL) , we embed deep neural networks in \emph{  multi-agent Q-routing.
Revised:  Inspired by recent success ofmulti-agent deep reinforcement learning (DRL) , we embed deep neural networks in \emph{ multi-agent Q-routing.
Edit intent: fluency
Original:  Inspired by recent success of deep reinforcement learning (DRL) , we embed deep neural networks in \emph{  multi-agent Q-routing.
Revised:  Inspired by recent success of deep reinforcement learning (DRL) in which each router possess an \emph{ multi-agent Q-routing.
Edit intent: clarity
Original:  Inspired by recent success of deep reinforcement learning (DRL) , we embed deep neural networks in \emph{  multi-agent Q-routing.
Revised:  Inspired by recent success of deep reinforcement learning (DRL) , we embed deep neural networks inindependent \emph{ multi-agent Q-routing.
Edit intent: fluency
Original:  For them, the TCP three-way handshake represents a significant delay overhead.
Revised:  For them, the TCP three-way handshake induces significant delay overhead.
Edit intent: clarity
Original:  The TCP Fast Open (TFO) protocol provides zero round-trip time (0-RTT) handshakes for subsequent TCP connections to the same host.
Revised:  The TCP Fast Open (TFO) protocol can significantly decrease this delay via zero round-trip time (0-RTT) handshakes for subsequent TCP connections to the same host.
Edit intent: clarity
Original:  We found that a client revisiting a web site for the first time fails to use an abbreviated TFO handshake about 40\% of the time due to web server load-balancing  .
Revised:  We found that a client revisiting a web site for the first time fails to use an abbreviated TFO handshake in 40\% of the time due to web server load-balancing .
Edit intent: clarity
Original:  We found that a client revisiting a web site for the first time fails to use an abbreviated TFO handshake about 40\% of the time due to web server load-balancing  .
Revised:  We found that a client revisiting a web site for the first time fails to use an abbreviated TFO handshake about 40\% of all cases due to web server load-balancing .
Edit intent: clarity
Original:  Our analysis further reveals significant privacy problems in the protocol design and implementation.
Revised:  Our analysis further reveals significant privacy problems of the protocol design and implementation.
Edit intent: clarity
Original:  Network-based attackers and online trackers can exploit these shortcomings to track the online activities of users.
Revised:  Network-based attackers and online trackers can exploit TFO to track the online activities of users.
Edit intent: clarity
Original:  It overcomes the performance and privacy limitations of TLS over TFO by utilizing a custom TLS extension.
Edit intent: coherence
Original:  TCP FOP prevents tracking by network attackers and impedes third-party tracking, while still allowing for 0-RTT handshakes as in TFO.
Revised:  TCP FOP prevents tracking by network attackers and impedes third-party tracking, while still allowing 0-RTT handshakes as in TFO.
Edit intent: fluency
Original:  To obtain the result, we give a lemma of independent interest on merges of typical sequences, a notion that was introduced in 1991 [Lagergren and Arnborg, Bodlaender and Kloks, both ICALP '91 ] to obtain constructive linear time parameterized algorithms for treewidth and pathwidth  ] .
Revised:  To obtain the result, we give a lemma of independent interest on merges of typical sequences, a notion that was introduced in 1991 [Lagergren and Arnborg, Bodlaender and Kloks, both ICALP 1991 ] to obtain constructive linear time parameterized algorithms for treewidth and pathwidth ] .
Edit intent: clarity
Original:  However, we apply the lemma to show that the scheduling of straight-line code (a basic block) to minimize the number of registers is solvable in quadratic time for series parallel digraphs . Previously, a polynomial-time algorithm was known only for trees Sethi and Ullman, JACM 1970%DIFDELCMD < ]%%% . We also show that the Cutwidth, Modified Cutwidth, and Vertex Separation problems can be solved in O(n^2) time for series parallel digraphs on n vertices .
Revised:  However, we apply the lemma to show that the cutwidth and the modified cutwidth of series parallel digraphs . Previously, a polynomial-time algorithm was known only for trees Sethi and Ullman, JACM 1970%DIFDELCMD < ]%%% . We also show that the Cutwidth, Modified Cutwidth, and Vertex Separation problems can be solved in O(n^2) time for series parallel digraphs on n vertices .
Edit intent: clarity
Original:  However, we apply the lemma to show that the scheduling of straight-line code (a basic block) to minimize the number of registers is solvable in quadratic time for series parallel digraphs . Previously, a polynomial-time algorithm was known only for trees Sethi and Ullman, JACM 1970%DIFDELCMD < ]%%% . We also show that the Cutwidth, Modified Cutwidth, and Vertex Separation problems can be solved in O(n^2) time for series parallel digraphs on n vertices .
Revised:  However, we apply the lemma to show that the scheduling of straight-line code (a basic block) to minimize the number of registers is solvable in quadratic time for series parallel digraphs Sethi and Ullman, JACM 1970%DIFDELCMD < ]%%% . We also show that the Cutwidth, Modified Cutwidth, and Vertex Separation problems can be solved in O(n^2) time for series parallel digraphs on n vertices .
Edit intent: coherence
Original:  However, we apply the lemma to show that the scheduling of straight-line code (a basic block) to minimize the number of registers is solvable in quadratic time for series parallel digraphs . Previously, a polynomial-time algorithm was known only for trees Sethi and Ullman, JACM 1970%DIFDELCMD < ]%%% . We also show that the Cutwidth, Modified Cutwidth, and Vertex Separation problems can be solved in O(n^2) time for series parallel digraphs on n vertices .
Revised:  However, we apply the lemma to show that the scheduling of straight-line code (a basic block) to minimize the number of registers is solvable in quadratic time for series parallel digraphs . Previously, a polynomial-time algorithm was known only for trees %DIFDELCMD < ]%%% . We also show that the Cutwidth, Modified Cutwidth, and Vertex Separation problems can be solved in O(n^2) time for series parallel digraphs on n vertices .
Edit intent: coherence
Original:  However, we apply the lemma to show that the scheduling of straight-line code (a basic block) to minimize the number of registers is solvable in quadratic time for series parallel digraphs . Previously, a polynomial-time algorithm was known only for trees Sethi and Ullman, JACM 1970%DIFDELCMD < ]%%% . We also show that the Cutwidth, Modified Cutwidth, and Vertex Separation problems can be solved in O(n^2) time for series parallel digraphs on n vertices .
Revised:  However, we apply the lemma to show that the scheduling of straight-line code (a basic block) to minimize the number of registers is solvable in quadratic time for series parallel digraphs . Previously, a polynomial-time algorithm was known only for trees Sethi and Ullman, JACM 1970%DIFDELCMD < ]%%% can be computed in O(n^2) time for series parallel digraphs on n vertices .
Edit intent: clarity
Original:  However, we apply the lemma to show that the scheduling of straight-line code (a basic block) to minimize the number of registers is solvable in quadratic time for series parallel digraphs . Previously, a polynomial-time algorithm was known only for trees Sethi and Ullman, JACM 1970%DIFDELCMD < ]%%% . We also show that the Cutwidth, Modified Cutwidth, and Vertex Separation problems can be solved in O(n^2) time for series parallel digraphs on n vertices .
Revised:  However, we apply the lemma to show that the scheduling of straight-line code (a basic block) to minimize the number of registers is solvable in quadratic time for series parallel digraphs . Previously, a polynomial-time algorithm was known only for trees Sethi and Ullman, JACM 1970%DIFDELCMD < ]%%% . We also show that the Cutwidth, Modified Cutwidth, and Vertex Separation problems can be solved in O(n^2) time .
Edit intent: clarity
Original:  The best such deterministic quantile summary to date, by Greenwald and Khanna ( ACM SIGMOD '01), stores at most O(1{\varepsilon}\cdot \log \varepsilon N) items, where N is the number of items in the stream.
Revised:  The best such deterministic quantile summary to date, due to Greenwald and Khanna ( ACM SIGMOD '01), stores at most O(1{\varepsilon}\cdot \log \varepsilon N) items, where N is the number of items in the stream.
Edit intent: clarity
Original:  The best such deterministic quantile summary to date, by Greenwald and Khanna ( ACM SIGMOD '01), stores at most O(1{\varepsilon}\cdot \log \varepsilon N) items, where N is the number of items in the stream.
Revised:  The best such deterministic quantile summary to date, by Greenwald and Khanna ( SIGMOD '01), stores at most O(1{\varepsilon}\cdot \log \varepsilon N) items, where N is the number of items in the stream.
Edit intent: clarity
Original:  We prove that this space bound is optimal by providing a matching lower bound.
Revised:  We prove that this space bound is optimal by showing a matching lower bound.
Edit intent: clarity
Original:  Our result thus rules out the possibility of constructing a deterministic comparison-based quantile summary in space f(\varepsilon)\cdot o(\log N), for any function f that does not depend on N. A consequence of our results is also to show a lower bound for randomized algorithms\pm  .
Revised:  Our result thus rules out the possibility of constructing a deterministic comparison-based quantile summary in space f(\varepsilon)\cdot o(\log N), for any function f that does not depend on N. As a corollary, we improve the lower bound for randomized algorithms\pm .
Edit intent: clarity
Original:  In the special case of the weakly and hypersingular operators on a line segment or  screen, our approach gives a unified, independent proof for a series of recent results by Hiptmair, Jerez-Hanckes, N\'{e}d\'{e}lec and Urz\'{u}a-Torres.
Revised:  In the special case of the weakly and hypersingular operators on a line segment or a screen, our approach gives a unified, independent proof for a series of recent results by Hiptmair, Jerez-Hanckes, N\'{e}d\'{e}lec and Urz\'{u}a-Torres.
Edit intent: fluency
Original:  In this paper, we consider a scenario that contains several slices in one base station on sharing the same bandwidth.
Revised:  In this paper, we consider a scenario that contains several slices in radio access networks with base stations sharing the same bandwidth.
Edit intent: clarity
Original:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying  demands and the allocated bandwidth as the environment state andaction , respectively.
Revised:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying demands and the allocated bandwidth as the environment andaction , respectively.
Edit intent: clarity
Original:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying  demands and the allocated bandwidth as the environment state andaction , respectively.
Revised:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying demands and the allocated bandwidth as the environment state action , respectively.
Edit intent: clarity
Original:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying  demands and the allocated bandwidth as the environment state andaction , respectively.
Revised:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying demands and the allocated bandwidth as the environment state andstate and action , respectively.
Edit intent: fluency
Original:  In order to obtain better quality of experience (QoE) satisfaction ratio and spectrum efficiency (SE), we propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of state-action values.
Revised:  In order to tackle the annoying randomness and noise embedded in the received quality of experience (QoE) satisfaction ratio and spectrum efficiency (SE), we propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of state-action values.
Edit intent: clarity
Original:  In order to obtain better quality of experience (QoE) satisfaction ratio and spectrum efficiency (SE), we propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of state-action values.
Revised:  In order to obtain better quality of experience (QoE) satisfaction ratio and spectrum efficiency (SE), we propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of action values.
Edit intent: clarity
Original:  Furthermore, we estimate the distributions by approximating a full quantile function, which can make the training error more controllable.
Revised:  Furthermore, we estimate the distributions by approximating a full quantile function, so as to make the training error more controllable.
Edit intent: coherence
Original:  In order to protect the stability of GAN-DDQN's training process from the widely-spanning utility values, we also put forward a reward-clipping mechanism.
Revised:  For the sake of protecting the stability of GAN-DDQN's training process from the widely-spanning utility values, we also put forward a reward-clipping mechanism.
Edit intent: coherence
Original: Network slicing is a key technology in 5G communications system , which aims to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure.
Revised: Network slicing is a key technology in 5G communications system . Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure.
Edit intent: coherence
Original:  In this paper, we consider a scenario that contains several slices in radio access networks with base stations sharing the same bandwidth.
Revised:  In this paper, we consider a scenario that contains several slices in a radio access network with base stations sharing the same bandwidth.
Edit intent: fluency
Original:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying service demands  and the allocated bandwidth as the environment state and action, respectively .
Revised:  Deep reinforcement learning (DRL) to solve this problem by regarding the varying service demands and the allocated bandwidth as the environment state and action, respectively .
Edit intent: clarity