wanyu/IteraTeR_v2 huggingface dataset:
Original:  Problems such as maximal independent set (MIS) or maximal matching are among the most intensively studied problems in the field and logarithmic round algorithms have been known for these problems from 1980s.
Revised:  Problems such as maximal independent set (MIS) or maximal matching are among the most intensively studied problems in the field and logarithmic round algorithms have been known from 1980s.
Edit intent: clarity
Original:  However, prior to our work, no sublogarithmic MPC algorithm was known for these problems using a truly sublinear space of n^{1-\Omega(1) . Our main result is a truly sublinear algorithm that takes O(\log \alpha + \log^2\log n) rounds to compute MIS or maximal matching using an optimal total space of O(m) where m denotes the number of edges and \alpha denotes the arboricity of the input graph.
Revised:  However, prior to our work, no improved MPC algorithm was known for these problems using a truly sublinear space of n^{1-\Omega(1) . Our main result is a truly sublinear algorithm that takes O(\log \alpha + \log^2\log n) rounds to compute MIS or maximal matching using an optimal total space of O(m) where m denotes the number of edges and \alpha denotes the arboricity of the input graph.
Edit intent: clarity
Original:  However, prior to our work, no sublogarithmic MPC algorithm was known for these problems using a truly sublinear space of n^{1-\Omega(1) . Our main result is a truly sublinear algorithm that takes O(\log \alpha + \log^2\log n) rounds to compute MIS or maximal matching using an optimal total space of O(m) where m denotes the number of edges and \alpha denotes the arboricity of the input graph.
Revised:  However, prior to our work, no sublogarithmic MPC algorithm was known for these problems using a truly sublinear space per machine . Our main result is a truly sublinear algorithm that takes O(\log \alpha + \log^2\log n) rounds to compute MIS or maximal matching using an optimal total space of O(m) where m denotes the number of edges and \alpha denotes the arboricity of the input graph.
Edit intent: clarity
Original:  However, prior to our work, no sublogarithmic MPC algorithm was known for these problems using a truly sublinear space of n^{1-\Omega(1) . Our main result is a truly sublinear algorithm that takes O(\log \alpha + \log^2\log n) rounds to compute MIS or maximal matching using an optimal total space of O(m) where m denotes the number of edges and \alpha denotes the arboricity of the input graph.
Revised:  However, prior to our work, no sublogarithmic MPC algorithm was known for these problems using a truly sublinear space of n^{1-\Omega(1) . Our main result is an algorithm that takes O(\log \alpha + \log^2\log n) rounds to compute MIS or maximal matching using an optimal total space of O(m) where m denotes the number of edges and \alpha denotes the arboricity of the input graph.
Edit intent: fluency
Original:  However, prior to our work, no sublogarithmic MPC algorithm was known for these problems using a truly sublinear space of n^{1-\Omega(1) . Our main result is a truly sublinear algorithm that takes O(\log \alpha + \log^2\log n) rounds to compute MIS or maximal matching using an optimal total space of O(m) where m denotes the number of edges and \alpha denotes the arboricity of the input graph.
Revised:  However, prior to our work, no sublogarithmic MPC algorithm was known for these problems using a truly sublinear space of n^{1-\Omega(1) . Our main result is a truly sublinear algorithm that takes O(\log \alpha + \log^2\log n) rounds to compute MIS or maximal matching with a truly sublinear space of n^{1-\Omega(1) an optimal total space of O(m) where m denotes the number of edges and \alpha denotes the arboricity of the input graph.
Edit intent: coherence
Original:  ~ [arXiv:1802.06748] that finds MIS on trees in O(\log^3\log n) rounds.
Revised:  [arXiv:1802.06748] that finds MIS on trees in O(\log^3\log n) rounds.
Edit intent: clarity
Original:  ~ [FOCS'12] and Ghaffari ~ [SODA'16].
Revised:  [FOCS'12] and Ghaffari ~ [SODA'16].
Edit intent: fluency
Original:  ~ [FOCS'12] and Ghaffari ~ [SODA'16].
Revised:  ~ [FOCS'12] and Ghaffari [SODA'16].
Edit intent: fluency
Original: The success of massively parallel computation (MPC) paradigms such as MapReduce has led to a significant interest in better understanding their true computational power.
Revised: The success of modern parallel paradigms such as MapReduce has led to a significant interest in better understanding their true computational power.
Edit intent: clarity
Original:  Since trees have arboricity one, our algorithm improves and generalizes the recent algorithm of Brandt et al. arXiv:1802.06748%DIFDELCMD < ] %%% that finds MIS on trees in O(\log ^3 \log n) rounds.
Revised:  Since trees have arboricity one, our algorithm improves and generalizes the recent algorithm of Brandt et al. %DIFDELCMD < ] %%% that finds MIS on trees in O(\log ^3 \log n) rounds.
Edit intent: clarity
Original:  Moreover, the n-dependency of our algorithm exponentially improves over the corresponding O(\log \alpha + \sqrt{\log n FOCS'12%DIFDELCMD < ] %%% and Ghaffari SODA'16 .
Revised:  Moreover, the n-dependency of our algorithm exponentially improves over the corresponding O(\log \alpha + \sqrt{\log n FOCS'12%DIFDELCMD < ] %%% SODA'16 .
Edit intent: coherence
Original:  Merlin is a daemon that connects to your favourite text editor and provides services that require a fine-grained understanding of the programming language syntax and static semantics: instant feedback on warnings and errors, autocompletion, `` type of the code under the cursor '', `` go to definition '' , etc. Language servers need to handle incomplete and partially-incorrect programs, and try to be incremental to minimize recomputation after small editing actions.
Revised:  Merlin is a daemon that connects to your favourite text editor and provides services that require a fine-grained understanding of the programming language syntax and static semantics: instant feedback on warnings and errors, autocompletion, ` type of the code under the cursor '', `` go to definition '' , etc. Language servers need to handle incomplete and partially-incorrect programs, and try to be incremental to minimize recomputation after small editing actions.
Edit intent: fluency
Original:  Merlin is a daemon that connects to your favourite text editor and provides services that require a fine-grained understanding of the programming language syntax and static semantics: instant feedback on warnings and errors, autocompletion, `` type of the code under the cursor '', `` go to definition '' , etc. Language servers need to handle incomplete and partially-incorrect programs, and try to be incremental to minimize recomputation after small editing actions.
Revised:  Merlin is a daemon that connects to your favourite text editor and provides services that require a fine-grained understanding of the programming language syntax and static semantics: instant feedback on warnings and errors, autocompletion, `` type of the code under the cursor ', ` go to definition '' , etc. Language servers need to handle incomplete and partially-incorrect programs, and try to be incremental to minimize recomputation after small editing actions.
Edit intent: fluency
Original:  Merlin is a daemon that connects to your favourite text editor and provides services that require a fine-grained understanding of the programming language syntax and static semantics: instant feedback on warnings and errors, autocompletion, `` type of the code under the cursor '', `` go to definition '' , etc. Language servers need to handle incomplete and partially-incorrect programs, and try to be incremental to minimize recomputation after small editing actions.
Revised:  Merlin is a daemon that connects to your favourite text editor and provides services that require a fine-grained understanding of the programming language syntax and static semantics: instant feedback on warnings and errors, autocompletion, `` type of the code under the cursor '', `` go to definition ' , etc. Language servers need to handle incomplete and partially-incorrect programs, and try to be incremental to minimize recomputation after small editing actions.
Edit intent: fluency
Original:  Utilizing sample-based profiling, BOLT boosts the performance of real-world x86 64-bit ELF applications even for highly optimized binaries built with both feedback-driven optimizations (FDO) and link-time optimizations (LTO).
Revised:  Utilizing sample-based profiling, BOLT boosts the performance of real-world applications even for highly optimized binaries built with both feedback-driven optimizations (FDO) and link-time optimizations (LTO).
Edit intent: clarity
Original:  We have also applied BOLT to GCC and Clang binaries, and our evaluation shows that BOLT speeds up these binaries by up to 15.3 \% on top of FDO and LTO, and up to 35.5 \% if the binaries are built without FDO and LTO.
Revised:  For the GCC and Clang binaries, and our evaluation shows that BOLT speeds up these binaries by up to 15.3 \% on top of FDO and LTO, and up to 35.5 \% if the binaries are built without FDO and LTO.
Edit intent: clarity
Original:  We have also applied BOLT to GCC and Clang binaries, and our evaluation shows that BOLT speeds up these binaries by up to 15.3 \% on top of FDO and LTO, and up to 35.5 \% if the binaries are built without FDO and LTO.
Revised:  We have also applied BOLT to GCC and Clang compilers, our evaluation shows that BOLT speeds up these binaries by up to 15.3 \% on top of FDO and LTO, and up to 35.5 \% if the binaries are built without FDO and LTO.
Edit intent: clarity
Original:  We have also applied BOLT to GCC and Clang binaries, and our evaluation shows that BOLT speeds up these binaries by up to 15.3 \% on top of FDO and LTO, and up to 35.5 \% if the binaries are built without FDO and LTO.
Revised:  We have also applied BOLT to GCC and Clang binaries, and our evaluation shows that BOLT speeds up their binaries by up to 15.3 \% on top of FDO and LTO, and up to 35.5 \% if the binaries are built without FDO and LTO.
Edit intent: clarity
Original: Near-neutrality of competition has been proposed to facilitate coexistence of species because it slows down competitive exclusion, thus making it easier for equalizing mechanisms to maintain diverse communities.
Revised: Similarity of competitors has been proposed to facilitate coexistence of species because it slows down competitive exclusion, thus making it easier for equalizing mechanisms to maintain diverse communities.
Edit intent: clarity
Original:  An unrelated line of work has shown that chaos can promote coexistence of many speciesin super-saturated communities.
Revised:  On the other hand, chaos can promote coexistence of many speciesin super-saturated communities.
Edit intent: clarity
Original:  We show that near-neutrality of competition at the prey's trophic level , in the presence of interactions with natural enemies , increases the chances of developing chaotic dynamics  .
Revised:  We show that near-neutrality of competition of prey , in the presence of interactions with natural enemies , increases the chances of developing chaotic dynamics .
Edit intent: clarity
Original:  We show that near-neutrality of competition at the prey's trophic level , in the presence of interactions with natural enemies , increases the chances of developing chaotic dynamics  .
Revised:  We show that near-neutrality of competition at the prey's trophic level , in the presence of predators , increases the chances of developing chaotic dynamics .
Edit intent: clarity
Original:  We show that near-neutrality of competition at the prey's trophic level , in the presence of interactions with natural enemies , increases the chances of developing chaotic dynamics  .
Revised:  We show that near-neutrality of competition at the prey's trophic level , in the presence of interactions with natural enemies , increases the chance of developing chaotic dynamics .
Edit intent: fluency
Original:  Up to constant factors in the exponent, we settle the complexity of {\sc Euclidean TSP} by giving a 2^{O(n^{1-1/d})} algorithm and by showing that an 2^{o(n^{1-1/d})} algorithm does not exist unless ETH fails.
Revised:  Up to constant factors in the exponent, we settle the complexity of {\sc Euclidean TSP} by giving a 2^{O(n^{1-1/d})} algorithm and by showing that a 2^{o(n^{1-1/d})} algorithm does not exist unless ETH fails.
Edit intent: fluency
Original: We use a semigroup-theoretic construction by Peter Higgins in order to produce, for each even n, an n-state and 3-letter synchronizing automaton with the following two features: 1) all its input letters act as idempotent selfmaps of rank at most n2;
Revised: We use a semigroup-theoretic construction by Peter Higgins in order to produce, for each even n, an n-state and 3-letter synchronizing automaton with the following two features: 1) all its input letters act as idempotent selfmaps of rank n2;
Edit intent: clarity
Original:  The minimum weight k-cut can be computed in O(n^{ 2 ( k-1 )}) randomized time Karger and Stein 1996%DIFDELCMD < ] %%% and O(n^{2k + O(1) Thorup 2008 , but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Holdschmidt and Hochbaum 1994].
Revised:  The minimum weight k-cut can be computed in O(n^{ 2 ( k )}) randomized time Karger and Stein 1996%DIFDELCMD < ] %%% and O(n^{2k + O(1) Thorup 2008 , but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Holdschmidt and Hochbaum 1994].
Edit intent: clarity
Original:  The minimum weight k-cut can be computed in O(n^{ 2 ( k-1 )}) randomized time Karger and Stein 1996%DIFDELCMD < ] %%% and O(n^{2k + O(1) Thorup 2008 , but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Holdschmidt and Hochbaum 1994].
Revised:  The minimum weight k-cut can be computed in O(n^{ 2 ( k-1 )}) Karger and Stein 1996%DIFDELCMD < ] %%% and O(n^{2k + O(1) Thorup 2008 , but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Holdschmidt and Hochbaum 1994].
Edit intent: clarity
Original:  The minimum weight k-cut can be computed in O(n^{ 2 ( k-1 )}) randomized time Karger and Stein 1996%DIFDELCMD < ] %%% and O(n^{2k + O(1) Thorup 2008 , but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Holdschmidt and Hochbaum 1994].
Revised:  The minimum weight k-cut can be computed in O(n^{ 2 ( k-1 )}) randomized time %DIFDELCMD < ] %%% and O(n^{2k + O(1) Thorup 2008 , but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Holdschmidt and Hochbaum 1994].
Edit intent: coherence
Original:  The minimum weight k-cut can be computed in O(n^{ 2 ( k-1 )}) randomized time Karger and Stein 1996%DIFDELCMD < ] %%% and O(n^{2k + O(1) Thorup 2008 , but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Holdschmidt and Hochbaum 1994].
Revised:  The minimum weight k-cut can be computed in O(n^{ 2 ( k-1 )}) randomized time Karger and Stein 1996%DIFDELCMD < ] %%% Thorup 2008 , but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Holdschmidt and Hochbaum 1994].
Edit intent: coherence
Original:  The minimum weight k-cut can be computed in O(n^{ 2 ( k-1 )}) randomized time Karger and Stein 1996%DIFDELCMD < ] %%% and O(n^{2k + O(1) Thorup 2008 , but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Holdschmidt and Hochbaum 1994].
Revised:  The minimum weight k-cut can be computed in O(n^{ 2 ( k-1 )}) randomized time Karger and Stein 1996%DIFDELCMD < ] %%% and O(n^{2k + O(1) time , but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Holdschmidt and Hochbaum 1994].
Edit intent: clarity
Original:  For poly (m,n,k)-time algorithms, the best possible approximation factor is essentially 2 under the small set expansion hypothesis [Manurangsi 2017].
Revised:  For (m,n,k)-time algorithms, the best possible approximation factor is essentially 2 under the small set expansion hypothesis [Manurangsi 2017].
Edit intent: fluency
Original:  Saran and Vazirani [1995] showed that a (2 - 2/k)-approximately minimum weight k-cut can be computed by O(k) minimum cuts, which implies an O(mk) randomized running time and a (k m n) deterministic running ] ]  time.
Revised:  Saran and Vazirani [1995] showed that a (2 - 2/k)-approximately minimum weight k-cut can be computed by O(k) minimum cuts, which implies an O(mk) randomized running time (k m n) deterministic running ] ] time.
Edit intent: coherence
Original:  Second, can 2-approximate  k-cuts be computed as fast as the (exact) minimum cut - in \tilde{O}(m) randomized time or%DIFDELCMD < \tilde{O}%%% (mn) deterministic time ?
Revised:  Second, can 2-approximate k-cuts be computed as fast as the (exact) minimum cut - in \tilde{O}(m) randomized time %DIFDELCMD < \tilde{O}%%% (mn) deterministic time ?
Edit intent: fluency
Original:  Second, can 2-approximate  k-cuts be computed as fast as the (exact) minimum cut - in \tilde{O}(m) randomized time or%DIFDELCMD < \tilde{O}%%% (mn) deterministic time ?
Revised:  Second, can 2-approximate k-cuts be computed as fast as the (exact) minimum cut - in \tilde{O}(m) randomized time or%DIFDELCMD < \tilde{O}%%% ?
Edit intent: clarity
Original:  We make progress on both of these questions with a deterministic approximation algorithm that computes (2 + \epsilon)-minimum k-cuts in O(m \log^3(n) / \epsilon^2) time, via a (1 + \epsilon)-approximate for an LP relaxation of k-cut.
Revised:  We make progress on these questions with a deterministic approximation algorithm that computes (2 + \epsilon)-minimum k-cuts in O(m \log^3(n) / \epsilon^2) time, via a (1 + \epsilon)-approximate for an LP relaxation of k-cut.
Edit intent: clarity
Original:  The only known polynomial time algorithm by Annamalai et al. gives a 12.33-approximation algorithm and is based on a modification of Haxell's hypergraph matching argument.
Revised:  A polynomial time algorithm by Annamalai et al. gives a 12.33-approximation algorithm and is based on a modification of Haxell's hypergraph matching argument.
Edit intent: clarity
Original:  The only known polynomial time algorithm by Annamalai et al. gives a 12.33-approximation algorithm and is based on a modification of Haxell's hypergraph matching argument.
Revised:  The only known polynomial time algorithm by Annamalai et al. gives a 12.33-approximation and is based on a modification of Haxell's hypergraph matching argument.
Edit intent: fluency
Original:  This factor compares to the value of an exponential sizeconfiguration LP . In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Revised:  configuration LP . In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Edit intent: clarity
Original:  This factor compares to the value of an exponential sizeconfiguration LP . In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Revised:  This factor compares to the value of an exponential size . In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Edit intent: clarity
Original:  This factor compares to the value of an exponential sizeconfiguration LP . In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Revised:  This factor compares to the value of an exponential sizeconfiguration LP In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Edit intent: fluency
Original:  This factor compares to the value of an exponential sizeconfiguration LP . In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Revised:  This factor compares to the value of an exponential sizeconfiguration LP . In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Edit intent: fluency
Original:  This factor compares to the value of an exponential sizeconfiguration LP . In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Revised:  This factor compares to the value of an exponential sizeconfiguration LP . In this paper, we introduce a matroid version of the Santa Claus problem . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Edit intent: coherence
Original:  This factor compares to the value of an exponential sizeconfiguration LP . In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmentation tree, but despite the greater generality, it is cleaner than previous methods.
Revised:  This factor compares to the value of an exponential sizeconfiguration LP . In this paper, we introduce a matroid version of the Santa Claus problem with unit size presents and design an algorithm which gives a polynomial time (3+\varepsilon)-approximation compared to a natural, compact LP . Our algorithm is also based on Haxell's augmenting tree, but despite the greater generality, it is cleaner than previous methods.
Edit intent: fluency