wanyu/IteraTeR_v2 huggingface dataset:
Original:  Given the products already in a basket, a classifier identifies whether a potential product is relevant to the basket or not, based on their vector representations.
Revised:  Given the products already in a basket, a classifier identifies whether a potential product is relevant to the basket based on their vector representations.
Edit intent: coherence
Original:  The experiments demonstrate that BASTEXT can efficiently model millions of baskets , and that it outperforms the state-of-the-art methods in the next product recommendation task.
Revised:  The experiments demonstrate that BASTEXT can efficiently model millions of baskets and that it outperforms the state-of-the-art methods in the next product recommendation task.
Edit intent: fluency
Original:  Besides, we will also show that BASTEXT is a strong baseline for keyword-based product search.
Revised:  We also show that BASTEXT is a strong baseline for keyword-based product search.
Edit intent: coherence
Original:  By known reductions, we can build a string attractor such that \gamma is upper-bounded by the number r of equal-letter runs in the Burrows-Wheeler transform of S, the size z of its Lempel-Ziv 77 factorization, the sizes gand b of a grammar and a Macro Scheme generating S, or the size e of the smallest compact automaton recognizing all S's suffixes .
Revised:  By known reductions, one can build a string attractor such that \gamma is upper-bounded by the number r of equal-letter runs in the Burrows-Wheeler transform of S, the size z of its Lempel-Ziv 77 factorization, the sizes gand b of a grammar and a Macro Scheme generating S, or the size e of the smallest compact automaton recognizing all S's suffixes .
Edit intent: clarity
Original:  In this paper we show that, within O%DIFDELCMD < \left (%%% \sigma \gamma \log^\epsilon n\log(n/\gamma)/\log\log n%DIFDELCMD < \right) %%% words of space, also rank and select queries can be supported in O(\log(n/\gamma)/\log\log n) time.
Revised:  In this paper we %DIFDELCMD < \left (%%% \sigma \gamma \log^\epsilon n\log(n/\gamma)/\log\log n%DIFDELCMD < \right) %%% words of space, also rank and select queries can be supported in O(\log(n/\gamma)/\log\log n) time.
Edit intent: clarity
Original:  We provide lower bounds showing that , when \sigma \in O(polylog\ n) , these space-time upper bounds are optimal.
Revised:  We provide lower bounds showing that these query times are optimal when \sigma \in O(polylog\ n) , these space-time upper bounds are optimal.
Edit intent: clarity
Original:  This is the first result showing that rank and select querieson the text can be supported efficiently in a space bounded by the size of a compressed Burrows-Wheeler transform or a general Macro Scheme, and improves existing bounds for \left (\right)  LZ77-compressed text by a \log\log n time-factor in select queries  .
Revised:  This is the first result showing that rank and select on the text can be supported efficiently in a space bounded by the size of a compressed Burrows-Wheeler transform or a general Macro Scheme, and improves existing bounds for \left (\right) LZ77-compressed text by a \log\log n time-factor in select queries .
Edit intent: fluency
Original:  This is the first result showing that rank and select querieson the text can be supported efficiently in a space bounded by the size of a compressed Burrows-Wheeler transform or a general Macro Scheme, and improves existing bounds for \left (\right)  LZ77-compressed text by a \log\log n time-factor in select queries  .
Revised:  This is the first result showing that rank and select queries can be supported efficiently in a space bounded by the size of a compressed Burrows-Wheeler transform or a general Macro Scheme, and improves existing bounds for \left (\right) LZ77-compressed text by a \log\log n time-factor in select queries .
Edit intent: clarity
Original:  This is the first result showing that rank and select querieson the text can be supported efficiently in a space bounded by the size of a compressed Burrows-Wheeler transform or a general Macro Scheme, and improves existing bounds for \left (\right)  LZ77-compressed text by a \log\log n time-factor in select queries  .
Revised:  This is the first result showing that rank and select querieson the text to the \left (\right) LZ77-compressed text by a \log\log n time-factor in select queries .
Edit intent: clarity
Original: Let \gamma be the size of a string attractor for a string S of length n over an alphabet of size \sigma.
Revised: We study the problem of supporting queries on a string S of length n over an alphabet of size \sigma.
Edit intent: clarity
Original:  It is known that , within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space, random access on S can be performed in optimal O(\log(n/\gamma)/\log\log n) time \left ( \right)  .
Revised:  It is known that , within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space, random access on S can be supported in optimal O(\log(n/\gamma)/\log\log n) time \left ( \right) .
Edit intent: clarity
Original:  It is known that , within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space, random access on S can be performed in optimal O(\log(n/\gamma)/\log\log n) time \left ( \right)  .
Revised:  It is known that , within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space, random access on S can be performed in optimal O(\log(n/\gamma)/\log\log n) time \left ( \right) space .
Edit intent: fluency
Original:  In this paper  we extend this functionality to rank and select queries  .
Revised:  In this paper , we extend this functionality to rank and select queries .
Edit intent: fluency
Original:  In this paper  we extend this functionality to rank and select queries  .
Revised:  In this paper we extend this result to rank and select queries .
Edit intent: clarity
Original:  Our solutions use a factor \sigma more space than the solution foraccess , but preserve the same query times.
Revised:  Our solutions access , but preserve the same query times.
Edit intent: clarity
Original:  Our solutions use a factor \sigma more space than the solution foraccess , but preserve the same query times.
Revised:  Our solutions use a factor \sigma more space than the solution for , but preserve the same query times.
Edit intent: clarity
Original:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank and\emph{select to the\emph{partial sum problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Revised:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{ and\emph{select to the\emph{partial sum problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Edit intent: fluency
Original:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank and\emph{select to the\emph{partial sum problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Revised:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank \emph{select to the\emph{partial sum problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Edit intent: coherence
Original:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank and\emph{select to the\emph{partial sum problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Revised:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank and\emph{ to the\emph{partial sum problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Edit intent: clarity
Original:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank and\emph{select to the\emph{partial sum problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Revised:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank and\emph{select \emph{partial sum problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Edit intent: clarity
Original:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank and\emph{select to the\emph{partial sum problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Revised:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank and\emph{select to the\emph{ problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Edit intent: clarity
Original:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank and\emph{select to the\emph{partial sum problem, for which we give a provably optimal-time solution within O%DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Revised:  Differently from previous solutions on dictionary-compressed text, we reduce\emph{rank and\emph{select to the\emph{partial sum %DIFDELCMD < \left (%%% \gamma \log^\epsilon n\log(n/\gamma) /\log\log n%DIFDELCMD < \right) %%% words of space.
Edit intent: clarity
Original:  Our solutions are the first of their kind for the run-length BWT, macro schemes, and collage systems, and match the fastest known for grammars\emph{ \emph{  .
Revised:  Our solutions are the first of their kind for the run-length BWT, macro schemes, and collage systems, and match the fastest known for grammars\emph{ and\emph{ .
Edit intent: coherence
Original:  A Segment Segment Tree divides the range into disjoint segments and merges them together to perform range queries and range updates elegantly.
Revised:  A Segment Tree divides the range into disjoint segments and merges them together to perform range queries and range updates elegantly.
Edit intent: clarity
Original:  Lazy Propagation enables the operations to be computed in O(logn) time in  single dimension.
Revised:  Lazy Propagation enables the operations to be computed in O(logn) time in a single dimension.
Edit intent: fluency
Original:  However, the concept of lazy propagation could not be translated to higher dimensional cases, which imposes a time complexity of O(n^{k-1} \;
Revised:  However, the concept of lazy propagation could not be translated to higher-dimensional cases, which imposes a time complexity of O(n^{k-1} \;
Edit intent: fluency
Original:  In this paper , we have made an attempt to emulate the idea of lazy propagation differently so that it can be applied for 2-dimensional cases.
Revised:  In this work , we have made an attempt to emulate the idea of lazy propagation differently so that it can be applied for 2-dimensional cases.
Edit intent: clarity
Original:  Moreover, the proposed modification is capable of performing any general aggregate function similar to the original Segment Tree, and can also be extended to even higher dimensions.
Revised:  Moreover, the proposed modification should be capable of performing any general aggregate function similar to the original Segment Tree, and can also be extended to even higher dimensions.
Edit intent: clarity
Original: We show that spacetime diagrams of linear cellular automata \Phi  } }\to {  with (-p)-automatic initial conditions are automatic.
Revised: We show that spacetime diagrams of linear cellular automata \Phi : } }\to { with (-p)-automatic initial conditions are automatic.
Edit intent: fluency
Original:  We also construct, for the Ledrappier cellular automaton, a family of nontrivial (\sigma, \Phi)-invariant measures  }.
Revised:  We construct, for the Ledrappier cellular automaton, a family of nontrivial (\sigma, \Phi)-invariant measures }.
Edit intent: clarity
Original:  We also construct, for the Ledrappier cellular automaton, a family of nontrivial (\sigma, \Phi)-invariant measures  }.
Revised:  We also construct, for the Ledrappier cellular automaton, a family of nontrivial (\sigma, \Phi)-invariant measures on }.
Edit intent: fluency
Original:  We construct, for the Ledrappier cellular automaton , a family of nontrivial (\sigma, \Phi)-invariant measures on {\mathbb F}_3^{\mathbb Z}.
Revised:  For the Ledrappier cellular automaton , a family of nontrivial (\sigma, \Phi)-invariant measures on {\mathbb F}_3^{\mathbb Z}.
Edit intent: clarity
Original:  We construct, for the Ledrappier cellular automaton , a family of nontrivial (\sigma, \Phi)-invariant measures on {\mathbb F}_3^{\mathbb Z}.
Revised:  We construct, for the Ledrappier cellular automaton we construct a family of nontrivial (\sigma, \Phi)-invariant measures on {\mathbb F}_3^{\mathbb Z}.
Edit intent: clarity
Original: Fooling deep neural networks with adversarial input have exposed a significant vulnerability in  current state-of-the-art systems in multiple domains.
Revised: Fooling deep neural networks with adversarial input have exposed a significant vulnerability in the current state-of-the-art systems in multiple domains.
Edit intent: fluency
Original:  In this work, we use a multi-objective genetic algorithm based approach to perform both targeted and un-targeted black-box attacks on automatic speech recognition (ASR) systems.
Revised:  In this work, we propose a framework which uses multi-objective genetic algorithm based approach to perform both targeted and un-targeted black-box attacks on automatic speech recognition (ASR) systems.
Edit intent: clarity
Original:  In this work, we use a multi-objective genetic algorithm based approach to perform both targeted and un-targeted black-box attacks on automatic speech recognition (ASR) systems.
Revised:  In this work, we use a multi-objective evolutionary optimization to perform both targeted and un-targeted black-box attacks on automatic speech recognition (ASR) systems.
Edit intent: clarity
Original:  In this work, we use a multi-objective genetic algorithm based approach to perform both targeted and un-targeted black-box attacks on automatic speech recognition (ASR) systems.
Revised:  In this work, we use a multi-objective genetic algorithm based approach to perform both targeted and un-targeted black-box attacks on Automatic Speech Recognition (ASR) systems.
Edit intent: fluency
Original: We show that if a system of degree-k polynomial inequalities on n Boolean variables has a Sums-of-Squares (SOS) proof of unsatisfiability with at most  s many monomials, then it also has one whose degree is of the order of the square root of  n \log s plus  k.
Revised: We show that if a system of degree-k polynomial constraints on~ n Boolean variables has a Sums-of-Squares (SOS) proof of unsatisfiability with at most s many monomials, then it also has one whose degree is of the order of the square root of n \log s plus k.
Edit intent: clarity
Original: We show that if a system of degree-k polynomial inequalities on n Boolean variables has a Sums-of-Squares (SOS) proof of unsatisfiability with at most  s many monomials, then it also has one whose degree is of the order of the square root of  n \log s plus  k.
Revised: We show that if a system of degree-k polynomial inequalities on n Boolean variables has a Sums-of-Squares (SOS) proof of unsatisfiability with at most s many monomials, then it also has one whose degree is of the order of the square root of ~ n \log s plus k.
Edit intent: clarity
Original: We show that if a system of degree-k polynomial inequalities on n Boolean variables has a Sums-of-Squares (SOS) proof of unsatisfiability with at most  s many monomials, then it also has one whose degree is of the order of the square root of  n \log s plus  k.
Revised: We show that if a system of degree-k polynomial inequalities on n Boolean variables has a Sums-of-Squares (SOS) proof of unsatisfiability with at most s many monomials, then it also has one whose degree is of the order of the square root of n \log s plus ~ k.
Edit intent: clarity
Original: The explosive growth of data and its related energy consumption is pushing the need to develop novel, brain-inspired and energy-efficient  schemes and materials for data processing and storage.
Revised: The explosive growth of data and its related energy consumption is pushing the need to develop energy-efficient schemes and materials for data processing and storage.
Edit intent: coherence
Original: In this paper we find sufficient conditions for the continuity of the  utility maximization problem from terminal wealth under convergence in distribution of the underlying processes.
Revised: In this paper we find sufficient conditions for the continuity of the utility maximization problem from terminal wealth with respect to the convergence in distribution of the underlying processes.
Edit intent: clarity
Original:  Finally, we apply our continuity results to numerical computations of the shortfall risk in the Heston model  .
Revised:  Finally, we apply our results to the computation of the minimum shortfall in the Heston model .
Edit intent: clarity
Original:  We provide several examples which illustrate that without these conditions, we cannot generally expect continuity to hold .
Revised:  We also establish a weak convergence result for the terminal wealths of the optimal portfolios .
Edit intent: clarity
Original:  Berman et al. (STOC 2014) , with linear dependence on d. (2) Sublinear in d:For the n=2 hypercube case, there are testers with o(d) query complexity%DIFDELCMD < [%%% Chakrabarty, Seshadhri (STOC 2013);
Revised:  Berman et al. (STOC 2014) %DIFDELCMD < [%%% Chakrabarty, Seshadhri (STOC 2013);
Edit intent: coherence
Original:  We describe\to )-query monotonicity tester for f: n%DIFDELCMD < ]%%% ^d%DIFDELCMD < \to %%% \{0,1\ . Our main technical result is a domain reduction theorem for monotonicity.
Revised:  We describe\to )-query monotonicity tester for n%DIFDELCMD < ]%%% ^d%DIFDELCMD < \to %%% \{0,1\ . Our main technical result is a domain reduction theorem for monotonicity.
Edit intent: clarity
Original:  We describe\to )-query monotonicity tester for f: n%DIFDELCMD < ]%%% ^d%DIFDELCMD < \to %%% \{0,1\ . Our main technical result is a domain reduction theorem for monotonicity.
Revised:  We describe\to )-query monotonicity tester for f: %DIFDELCMD < ]%%% ^d%DIFDELCMD < \to %%% \{0,1\ . Our main technical result is a domain reduction theorem for monotonicity.
Edit intent: clarity
Original:  We describe\to )-query monotonicity tester for f: n%DIFDELCMD < ]%%% ^d%DIFDELCMD < \to %%% \{0,1\ . Our main technical result is a domain reduction theorem for monotonicity.
Revised:  We describe\to )-query monotonicity tester for f: n%DIFDELCMD < ]%%% %DIFDELCMD < \to %%% \{0,1\ . Our main technical result is a domain reduction theorem for monotonicity.
Edit intent: clarity
Original:  For any function f , ]\to  of the function on a random [k]^d sub-hypergrid of the original domain.
Revised:  For any function f : ]\to of the function on a random [k]^d sub-hypergrid of the original domain.
Edit intent: fluency
Original:  For any function f , ]\to  of the function on a random [k]^d sub-hypergrid of the original domain.
Revised:  For any function f , ]^d\to of the function on a random [k]^d sub-hypergrid of the original domain.
Edit intent: fluency
Original:  Our result }  follows by applying the d^{5/6}\cdot \text{poly}(1/\epsilon,\log n, \log d)-query hypergrid tester of Black-Chakrabarty-Seshadhri (SODA 2018)  ] .
Revised:  Our result } then follows by applying the d^{5/6}\cdot \text{poly}(1/\epsilon,\log n, \log d)-query hypergrid tester of Black-Chakrabarty-Seshadhri (SODA 2018) ] .
Edit intent: coherence
Original:  We give approximation algorithms for every function g which is approximately monotone and satisfies an approximate triangle inequality, and we show both of these conditions are necessary.
Revised:  In this work we give approximation algorithms for every function g which is approximately monotone and satisfies an approximate triangle inequality, and we show both of these conditions are necessary.
Edit intent: clarity
Original:  We give approximation algorithms for every function g which is approximately monotone and satisfies an approximate triangle inequality, and we show both of these conditions are necessary.
Revised:  We give approximation algorithms for every function g which is approximately monotone and satisfies an approximate triangle inequality, and we show both of these conditions are necessary.
Edit intent: fluency
Original: When observing the actions of others, humans carry out inferences about why the others acted as they did, and what this implies about their view of the world.
Revised: When observing the actions of others, humans make inferences about why the others acted as they did, and what this implies about their view of the world.
Edit intent: clarity
Original:  Together with the public belief, this Bayesian update effectively defines a new Markov decision process, the public belief MDP, in which the action space consists of deterministic partial policies, parameterised by deep neural networks, that can be sampled for a given public state.
Revised:  Together with the public belief, this Bayesian update effectively defines a new Markov decision process, the public belief MDP, in which the action space consists of deterministic partial policies, parameterised by neural networks, that can be sampled for a given public state.
Edit intent: clarity
Original:  It exploits the fact that an agent acting only on this public belief state can still learn to use its private information if the action space is augmented to be over partial policies mapping private information into environment actions.
Revised:  BAD exploits the fact that an agent acting only on this public belief state can still learn to use its private information if the action space is augmented to be over partial policies mapping private information into environment actions.
Edit intent: fluency
Original:  We first validate BAD on a proof-of-principle two-step matrix game, where it outperforms traditional policy gradient methods.
Revised:  We first validate BAD on a proof-of-principle two-step matrix game, where it outperforms policy gradient methods.
Edit intent: clarity
Original: When observing the actions of others, humans make inferences about why the others acted as they did, and what this implies about their view of the world.
Revised: When observing the actions of others, humans make inferences about why they acted as they did, and what this implies about their view of the world.
Edit intent: clarity
Original:  Humans also use the fact that their actions will be interpreted in this manner when observed by others , allowing them to act informatively and thereby communicate efficiently with others.
Revised:  Humans also use the fact that their actions will be interpreted in this manner , allowing them to act informatively and thereby communicate efficiently with others.
Edit intent: clarity