wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we proposeLeadCache,\texttt{  an online caching policy based on the \textsf{Follow the Perturbed Leader (FTPL) paradigm.
Revised:  In this paper, we proposeLeadCache,\texttt{ - an online caching policy based on the \textsf{Follow the Perturbed Leader (FTPL) paradigm.
Edit intent: fluency
Original:  In this paper, we proposeLeadCache,\texttt{  an online caching policy based on the \textsf{Follow the Perturbed Leader (FTPL) paradigm.
Revised:  In this paper, we proposeLeadCache,\texttt{ an online caching policy based on the \textsf{Follow the Perturbed Leader Follow-the-Perturbed-Leader paradigm.
Edit intent: clarity
Original:  We show that \textsf{LeadCache is regret optimal up to a multiplicative factor of \tilde{O}(n^{ 0.375 }) . As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area.
Revised:  We show that \textsf{ is regret optimal up to a multiplicative factor of \tilde{O}(n^{ 0.375 }) . As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area.
Edit intent: coherence
Original:  We show that \textsf{LeadCache is regret optimal up to a multiplicative factor of \tilde{O}(n^{ 0.375 }) . As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area.
Revised:  We show that \textsf{LeadCache the policy is regret-optimal up to a multiplicative factor of \tilde{O}(n^{ 0.375 }) . As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area.
Edit intent: meaning-changed
Original:  We show that \textsf{LeadCache is regret optimal up to a multiplicative factor of \tilde{O}(n^{ 0.375 }) . As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area.
Revised:  We show that \textsf{LeadCache is regret optimal up to a factor of \tilde{O}(n^{ 0.375 }) . As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area.
Edit intent: clarity
Original:  We show that \textsf{LeadCache is regret optimal up to a multiplicative factor of \tilde{O}(n^{ 0.375 }) . As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area.
Revised:  We show that \textsf{LeadCache is regret optimal up to a multiplicative factor of \tilde{O}(n^{ 3/8 }) . As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area.
Edit intent: meaning-changed
Original:  We show that \textsf{LeadCache is regret optimal up to a multiplicative factor of \tilde{O}(n^{ 0.375 }) . As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area.
Revised:  We show that \textsf{LeadCache is regret optimal up to a multiplicative factor of \tilde{O}(n^{ 0.375 }) , where n is the number of users. We implement the policy by designing a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area.
Edit intent: meaning-changed
Original:  We show that \textsf{LeadCache is regret optimal up to a multiplicative factor of \tilde{O}(n^{ 0.375 }) . As a byproduct of our analysis, we design a new linear-time deterministic Pipage rounding procedure for the LP relaxation of a well-known NP-hard combinatorial optimization problem in this area. Our new rounding algorithmsubstantially improves upon the currently best-known complexity for this problem. Moreover , we show the surprising result that under mild Strong-Law-type assumptions on the file request sequence, the rate of file fetches to the caches approaches to zero under the\textsf{LeadCache policy.
Revised:  We show that \textsf{LeadCache is regret optimal up to a multiplicative factor of \tilde{O}(n^{ 0.375 }) . As a byproduct of our analysis, we design a new linear-time Pipage rounding algorithm. With an additional Strong-Law-type assumption , we show the surprising result that under mild Strong-Law-type assumptions on the file request sequence, the rate of file fetches to the caches approaches to zero under the\textsf{LeadCache policy.
Edit intent: coherence
Original:  Moreover , we show the surprising result that under mild Strong-Law-type assumptions on the file request sequence, the rate of file fetches to the caches approaches to zero under the\textsf{LeadCache policy.
Revised:  Moreover , we show the surprising result that under mild Strong-Law-type assumptions on the file request sequence, the rate of file fetches to the caches approaches to zero under the\textsf{ policy.
Edit intent: clarity
Original:  Moreover , we show the surprising result that under mild Strong-Law-type assumptions on the file request sequence, the rate of file fetches to the caches approaches to zero under the\textsf{LeadCache policy. Finally\texttt{  , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Revised:  Moreover , we show the surprising result that under mild Strong-Law-type assumptions on the file request sequence, the rate of file fetches to the caches approaches to zero under the\textsf{LeadCache under\texttt{ , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Edit intent: clarity
Original:  Finally\texttt{  , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Revised:  Finally\texttt{LeadCache , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Edit intent: meaning-changed
Original:  Finally\texttt{  , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Revised:  Finally\texttt{ remains almost surely finite. Additionally , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Edit intent: meaning-changed
Original:  Finally\texttt{  , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Revised:  Finally\texttt{ , we derive a tight regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Edit intent: clarity
Original:  Finally\texttt{  , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Revised:  Finally\texttt{ , we derive a tight universal regret lower bound \textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Edit intent: fluency
Original:  Finally\texttt{  , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Revised:  Finally\texttt{ , we derive a tight universal regret lower bound for the\textsf{ problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Edit intent: coherence
Original:  Finally\texttt{  , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Revised:  Finally\texttt{ , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching using results from graph coloring theory and certifies the announced approximation ratio .
Edit intent: clarity
Original:  Finally\texttt{  , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring theory and certifies the announced approximation ratio .
Revised:  Finally\texttt{ , we derive a tight universal regret lower bound for the\textsf{Bipartite Caching problem, which critically makes use of results from graph coloring . Our conclusion is that the proposed learning-based caching policy decisively outperforms the classical policies both theoretically and empirically .
Edit intent: meaning-changed
Original: Given a weighted undirected graph G=(V,E,w), a hopset H of hopbound \beta and stretch (1+\epsilon) is a set of edges such that for any pair of nodes u, v \in V, there is a path in G \cup H of at most \beta hops, whose length is within a (1+\epsilon) factor from the distance between u and v in G. We provide a decremental algorithm for maintaining hopsets with a polylogarithmic hopbound , with a total update time that matches the best known static algorithm up to polylogarithmic factors.
Revised: Given a weighted undirected graph G=(V,E,w), a hopset H of hopbound \beta and stretch (1+\epsilon) is a set of edges such that for any pair of nodes u, v \in V, there is a path in G \cup H of at most \beta hops, whose length is within a (1+\epsilon) factor from the distance between u and v in G. We show the first efficient decremental algorithm for maintaining hopsets with a polylogarithmic hopbound , with a total update time that matches the best known static algorithm up to polylogarithmic factors.
Edit intent: clarity
Original: Given a weighted undirected graph G=(V,E,w), a hopset H of hopbound \beta and stretch (1+\epsilon) is a set of edges such that for any pair of nodes u, v \in V, there is a path in G \cup H of at most \beta hops, whose length is within a (1+\epsilon) factor from the distance between u and v in G. We provide a decremental algorithm for maintaining hopsets with a polylogarithmic hopbound , with a total update time that matches the best known static algorithm up to polylogarithmic factors.
Revised: Given a weighted undirected graph G=(V,E,w), a hopset H of hopbound \beta and stretch (1+\epsilon) is a set of edges such that for any pair of nodes u, v \in V, there is a path in G \cup H of at most \beta hops, whose length is within a (1+\epsilon) factor from the distance between u and v in G. We provide a decremental algorithm for maintaining hopsets with a polylogarithmic hopbound . The update time of our algorithm matches the best known static algorithm up to polylogarithmic factors.
Edit intent: clarity
Original:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -(1+\epsilon) -approximate single source shortest paths in amortized update time }  of 2^{ (\sqrt{\log n } . This improves super-polynomially over the best known amortized update time of 2^{\tilde{O(\log^{3/4} n)} by } [  HKN, FOCS'14  ].
Revised:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -(1+\epsilon) -approximate single source shortest paths in amortized update time } All the previous decremental hopset constructions had a superpolylogarithmic (but subpolynomial) hopbound of 2^{ (\sqrt{\log n } . This improves super-polynomially over the best known amortized update time of 2^{\tilde{O(\log^{3/4} n)} by } [ HKN, FOCS'14 ].
Edit intent: meaning-changed
Original:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -(1+\epsilon) -approximate single source shortest paths in amortized update time }  of 2^{ (\sqrt{\log n } . This improves super-polynomially over the best known amortized update time of 2^{\tilde{O(\log^{3/4} n)} by } [  HKN, FOCS'14  ].
Revised:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -(1+\epsilon) -approximate single source shortest paths in amortized update time } of 2^{ \log^{\Omega(1) } . This improves super-polynomially over the best known amortized update time of 2^{\tilde{O(\log^{3/4} n)} by } [ HKN, FOCS'14 ].
Edit intent: meaning-changed
Original:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -(1+\epsilon) -approximate single source shortest paths in amortized update time }  of 2^{ (\sqrt{\log n } . This improves super-polynomially over the best known amortized update time of 2^{\tilde{O(\log^{3/4} n)} by } [  HKN, FOCS'14  ].
Revised:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -(1+\epsilon) -approximate single source shortest paths in amortized update time } of 2^{ (\sqrt{\log n } (\log^{3/4} n)} by } [ HKN, FOCS'14 ].
Edit intent: coherence
Original:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -(1+\epsilon) -approximate single source shortest paths in amortized update time }  of 2^{ (\sqrt{\log n } . This improves super-polynomially over the best known amortized update time of 2^{\tilde{O(\log^{3/4} n)} by } [  HKN, FOCS'14  ].
Revised:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -(1+\epsilon) -approximate single source shortest paths in amortized update time } of 2^{ (\sqrt{\log n } . This improves super-polynomially over the best known amortized update time of 2^{\tilde{O(\log^{3/4} n)} by } [ Bernstein, FOCS'09; HKN, FOCS'14 ].
Edit intent: meaning-changed
Original:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -(1+\epsilon) -approximate single source shortest paths in amortized update time }  of 2^{ (\sqrt{\log n } . This improves super-polynomially over the best known amortized update time of 2^{\tilde{O(\log^{3/4} n)} by } [  HKN, FOCS'14  ].
Revised:  Our decremental hopset algorithm allows us to obtain the following improved decremental algorithms for maintaining shortest paths. -(1+\epsilon) -approximate single source shortest paths in amortized update time } of 2^{ (\sqrt{\log n } . This improves super-polynomially over the best known amortized update time of 2^{\tilde{O(\log^{3/4} n)} by } [ HKN, FOCS'14 ; Chechik, FOCS'18 ].
Edit intent: meaning-changed
Original:  - ( 1+\epsilon)-approximate shortest paths from a set of s sources in (s) amortized update time, assuming that s= n^{\Omega(1), and |E|= n^{1+\Omega(1)}.
Revised:  By applying our decremental hopset construction, we get improved or near optimal bounds for several distance problems. Most importantly, we show how to decrementally maintain ( 1+\epsilon)-approximate shortest paths from a set of s sources in (s) amortized update time, assuming that s= n^{\Omega(1), and |E|= n^{1+\Omega(1)}.
Edit intent: meaning-changed
Original:  - ( 1+\epsilon)-approximate shortest paths from a set of s sources in (s) amortized update time, assuming that s= n^{\Omega(1), and |E|= n^{1+\Omega(1)}.
Revised:  - ( (s) amortized update time, assuming that s= n^{\Omega(1), and |E|= n^{1+\Omega(1)}.
Edit intent: clarity
Original:  - ( 1+\epsilon)-approximate shortest paths from a set of s sources in (s) amortized update time, assuming that s= n^{\Omega(1), and |E|= n^{1+\Omega(1)}.
Revised:  - ( 1+\epsilon)-approximate shortest paths from a set of s sources in , and |E|= n^{1+\Omega(1)}.
Edit intent: clarity
Original:  This improves over the best-known amortized update time of%DIFDELCMD < \tilde{O}%%% (n^{1/k)\cdot(1/\epsilon)^{O(\sqrt{\log n})} }  [Chechik, FOCS'18] . Moreover, we reduce the query time from O(\log \log(nW)) to a constant O(k), and hence eliminate the dependence on nand the aspect ratio W\cdot(\frac{1}{\epsilon})^{\tilde{O}(\sqrt{\log n})}.
Revised:  This %DIFDELCMD < \tilde{O}%%% (n^{1/k)\cdot(1/\epsilon)^{O(\sqrt{\log n})} } [Chechik, FOCS'18] . Moreover, we reduce the query time from O(\log \log(nW)) to a constant O(k), and hence eliminate the dependence on nand the aspect ratio W\cdot(\frac{1}{\epsilon})^{\tilde{O}(\sqrt{\log n})}.
Edit intent: coherence
Original:  This improves over the best-known amortized update time of%DIFDELCMD < \tilde{O}%%% (n^{1/k)\cdot(1/\epsilon)^{O(\sqrt{\log n})} }  [Chechik, FOCS'18] . Moreover, we reduce the query time from O(\log \log(nW)) to a constant O(k), and hence eliminate the dependence on nand the aspect ratio W\cdot(\frac{1}{\epsilon})^{\tilde{O}(\sqrt{\log n})}.
Revised:  This improves over the best-known amortized update time of%DIFDELCMD < \tilde{O}%%% )\cdot(1/\epsilon)^{O(\sqrt{\log n})} } [Chechik, FOCS'18] . Moreover, we reduce the query time from O(\log \log(nW)) to a constant O(k), and hence eliminate the dependence on nand the aspect ratio W\cdot(\frac{1}{\epsilon})^{\tilde{O}(\sqrt{\log n})}.
Edit intent: coherence
Original:  This improves over the best-known amortized update time of%DIFDELCMD < \tilde{O}%%% (n^{1/k)\cdot(1/\epsilon)^{O(\sqrt{\log n})} }  [Chechik, FOCS'18] . Moreover, we reduce the query time from O(\log \log(nW)) to a constant O(k), and hence eliminate the dependence on nand the aspect ratio W\cdot(\frac{1}{\epsilon})^{\tilde{O}(\sqrt{\log n})}.
Revised:  This improves over the best-known amortized update time of%DIFDELCMD < \tilde{O}%%% (n^{1/k)\cdot(1/\epsilon)^{O(\sqrt{\log n})} } significantly improves (by a polynomial factor) over the update-time of the best previously known decremental algorithm in the constant query time regime. Moreover, it improves over the result of [Chechik, FOCS'18] . Moreover, we reduce the query time from O(\log \log(nW)) to a constant O(k), and hence eliminate the dependence on nand the aspect ratio W\cdot(\frac{1}{\epsilon})^{\tilde{O}(\sqrt{\log n})}.
Edit intent: meaning-changed
Original:  This improves over the best-known amortized update time of%DIFDELCMD < \tilde{O}%%% (n^{1/k)\cdot(1/\epsilon)^{O(\sqrt{\log n})} }  [Chechik, FOCS'18] . Moreover, we reduce the query time from O(\log \log(nW)) to a constant O(k), and hence eliminate the dependence on nand the aspect ratio W\cdot(\frac{1}{\epsilon})^{\tilde{O}(\sqrt{\log n})}.
Revised:  This improves over the best-known amortized update time of%DIFDELCMD < \tilde{O}%%% (n^{1/k)\cdot(1/\epsilon)^{O(\sqrt{\log n})} } [Chechik, FOCS'18] that has a query time of O(\log \log(nW)) to a constant O(k), and hence eliminate the dependence on nand the aspect ratio W\cdot(\frac{1}{\epsilon})^{\tilde{O}(\sqrt{\log n})}.
Edit intent: coherence
Original:  This improves over the best-known amortized update time of%DIFDELCMD < \tilde{O}%%% (n^{1/k)\cdot(1/\epsilon)^{O(\sqrt{\log n})} }  [Chechik, FOCS'18] . Moreover, we reduce the query time from O(\log \log(nW)) to a constant O(k), and hence eliminate the dependence on nand the aspect ratio W\cdot(\frac{1}{\epsilon})^{\tilde{O}(\sqrt{\log n})}.
Revised:  This improves over the best-known amortized update time of%DIFDELCMD < \tilde{O}%%% (n^{1/k)\cdot(1/\epsilon)^{O(\sqrt{\log n})} } [Chechik, FOCS'18] . Moreover, we reduce the query time from O(\log \log(nW)) , where W is the aspect ratio, and the amortized update time is n^{1/k\cdot(\frac{1}{\epsilon})^{\tilde{O}(\sqrt{\log n})}.
Edit intent: meaning-changed
Original:  To tackle this challenge, we propose a deliberate self-attention deep neural network model, named as FEDAR, for the MARP problem, which can achieve competitive performance while also being able to interpret the predictions made.
Revised:  To tackle this challenge, we propose a deliberate self-attention-based deep neural network model, named as FEDAR, for the MARP problem, which can achieve competitive performance while also being able to interpret the predictions made.
Edit intent: clarity
Original:  To tackle this challenge, we propose a deliberate self-attention deep neural network model, named as FEDAR, for the MARP problem, which can achieve competitive performance while also being able to interpret the predictions made.
Revised:  To tackle this challenge, we propose a deliberate self-attention deep neural network model, namely FEDAR, for the MARP problem, which can achieve competitive performance while also being able to interpret the predictions made.
Edit intent: clarity
Original:  To tackle this challenge, we propose a deliberate self-attention deep neural network model, named as FEDAR, for the MARP problem, which can achieve competitive performance while also being able to interpret the predictions made.
Revised:  To tackle this challenge, we propose a deliberate self-attention deep neural network model, named as FEDAR, for the DMSC problem, which can achieve competitive performance while also being able to interpret the predictions made.
Edit intent: meaning-changed
Original:   Since crowdsourcing annotation can be an alternate way to recover missing ratings of reviews, we propose a LEcture-AuDience (LEAD) strategy to estimate model uncertainty in the context of multi-task learning, so that valuable human resources can focus on the most uncertain predictions.
Revised:  These keywords are significant for rating predictions by FEDAR. Since crowdsourcing annotation can be an alternate way to recover missing ratings of reviews, we propose a LEcture-AuDience (LEAD) strategy to estimate model uncertainty in the context of multi-task learning, so that valuable human resources can focus on the most uncertain predictions.
Edit intent: meaning-changed
Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer-E ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Revised:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose T ransducer-E ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Edit intent: coherence
Original:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED,T ransducer-E ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Revised:  Inspired by neuroscience, humans have perception systems and cognitive systems to process different information, we propose TED, ransducer-E ncoder-D ecoder , a unified framework with triple supervision to decouple the end-to-end speech-to-text translation task.
Edit intent: fluency