wanyu/IteraTeR_v2 huggingface dataset:
Original:  To this end, we need an algorithm which can rely on more general and realistic assumptions and can decide where to go based on the past experiences  .
Revised:  To this end, we need an algorithm which can rely on general and realistic assumptions and can decide where to go based on the past experiences .
Edit intent: clarity
Original:  To this end, we need an algorithm which can rely on more general and realistic assumptions and can decide where to go based on the past experiences  .
Revised:  To this end, we need an algorithm which can rely on more general and realistic assumptions and can decide where to go based on the past experiences is required .
Edit intent: coherence
Original: Each time a learner in a self-paced online course is trying to answer an assessment question, it takes some time to submit the answer, and if multiple attempts are allowed and the first answer was incorrect, it takes some time to submit the second attempt, and so on . Here we study the distribution of such "response times ".
Revised: Each time a learner in a self-paced online course seeks to answer an assessment question, it takes some time to submit the answer, and if multiple attempts are allowed and the first answer was incorrect, it takes some time to submit the second attempt, and so on . Here we study the distribution of such "response times ".
Edit intent: clarity
Original: Each time a learner in a self-paced online course is trying to answer an assessment question, it takes some time to submit the answer, and if multiple attempts are allowed and the first answer was incorrect, it takes some time to submit the second attempt, and so on . Here we study the distribution of such "response times ".
Revised: Each time a learner in a self-paced online course is trying to answer an assessment question, it takes some time to submit the answer, and if multiple attempts are allowed and the first answer is incorrect, it takes some time to submit the second attempt, and so on . Here we study the distribution of such "response times ".
Edit intent: fluency
Original: Each time a learner in a self-paced online course is trying to answer an assessment question, it takes some time to submit the answer, and if multiple attempts are allowed and the first answer was incorrect, it takes some time to submit the second attempt, and so on . Here we study the distribution of such "response times ".
Revised: Each time a learner in a self-paced online course is trying to answer an assessment question, it takes some time to submit the answer, and if multiple attempts are allowed and the first answer was incorrect, it takes some time to provide a second answer . Here we study the distribution of such "response times ".
Edit intent: clarity
Original: Each time a learner in a self-paced online course is trying to answer an assessment question, it takes some time to submit the answer, and if multiple attempts are allowed and the first answer was incorrect, it takes some time to submit the second attempt, and so on . Here we study the distribution of such "response times ".
Revised: Each time a learner in a self-paced online course is trying to answer an assessment question, it takes some time to submit the answer, and if multiple attempts are allowed and the first answer was incorrect, it takes some time to submit the second attempt, and so on . Here we study the distribution of such "response times ."
Edit intent: fluency
Original:  We find that the log-normal statistical model for such times, previously suggested in the literature, holds for online courses qualitatively .
Revised:  We find that the log-normal statistical model for such times, previously suggested in the literature, holds for online courses .
Edit intent: clarity
Original:  Users who, according to this model, tend to take longer on submits are more likely to complete the course, have a higher level of engagement  and achieve a higher grade.
Revised:  Users who, according to this model, tend to take longer on submits are more likely to complete the course, have a higher level of engagement , and achieve a higher grade.
Edit intent: fluency
Original:  This finding can be the basis for designing interventions in online courses, such as MOOCs, which would encourage some users to slow down.
Revised:  This finding can be the basis for designing interventions in online courses, such as MOOCs, which would encourage "fast" users to slow down.
Edit intent: clarity
Original:  If multiple attempts are allowed  and the first answer is incorrect, it takes some time to provide a second answer.
Revised:  If multiple attempts are allowed , and the first answer is incorrect, it takes some time to provide a second answer.
Edit intent: fluency
Original:  For this reason, deep neural networks can be used for learning better representations of the data .
Revised:  For this reason, deep neural networks have proven promising for clustering real-world data because of their high representational power .
Edit intent: clarity
Original:  Our experimental evaluation on image datasets shows that the method approaches state-of-the-art clustering quality , and performs better in some cases.
Revised:  Our experimental evaluation on image datasets shows that the method approaches state-of-the-art clustering quality and surpasses it in some cases.
Edit intent: clarity
Original:  After discretization, optimal transportation becomes a large-scale linear program, which typically is infeasible to solve efficiently on triangle meshes, graphs, point clouds, and other domains encountered in graphics and machine learning.
Revised:  After discretization, optimal transport becomes a large-scale linear program, which typically is infeasible to solve efficiently on triangle meshes, graphs, point clouds, and other domains encountered in graphics and machine learning.
Edit intent: fluency
Original:  Recent breakthroughs in numerical optimal transportation enable scalability to orders-of-magnitude larger problems, solvable in a fraction of a second.
Revised:  Recent breakthroughs in numerical optimal transport, however, enable scalability to orders-of-magnitude larger problems, solvable in a fraction of a second.
Edit intent: coherence
Original:  In these lecture notes , we discuss advances in numerical optimal transport that leverage understanding of both discrete and smooth aspects of the problem.
Revised:  Here , we discuss advances in numerical optimal transport that leverage understanding of both discrete and smooth aspects of the problem.
Edit intent: clarity
Original:  State-of-the-art techniques in discrete optimal transportation combine insight from partial differential equations (PDE) with convex analysis to reformulate, discretize, and optimize transportation problems.
Revised:  State-of-the-art techniques in discrete optimal transport combine insight from partial differential equations (PDE) with convex analysis to reformulate, discretize, and optimize transportation problems.
Edit intent: fluency
Original:  The sets of nodes strongly connected internally are called communities .
Revised:  A set of nodes is called a community if the nodes have strong interrelations .
Edit intent: clarity
Original:  Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper customer services.
Revised:  Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper and accurate services.
Edit intent: clarity
Original:  However, all community detection methods are motivated by solving some concrete problems, while the applicabilities in other fields are open to question.
Revised:  However, all the current detection algorithms are motivated by solving some concrete problems, while the applicabilities in other fields are open to question.
Edit intent: clarity
Original:  However, all community detection methods are motivated by solving some concrete problems, while the applicabilities in other fields are open to question.
Revised:  However, all community detection methods are motivated by the practical problems, whose applicabilities in other fields are open to question.
Edit intent: clarity
Original:  Therefore, confronting a new community problem, researchers need to derive algorithms ad hoc, which is time-consuming and even unnecessary.
Revised:  Thence, for a new community problem, researchers need to derive algorithms ad hoc, which is time-consuming and even unnecessary.
Edit intent: clarity
Original:  Therefore, confronting a new community problem, researchers need to derive algorithms ad hoc, which is time-consuming and even unnecessary.
Revised:  Therefore, confronting a new community problem, researchers need to derive algorithms ad hoc, which is arduous and even unnecessary.
Edit intent: clarity
Original:  In this paper, we represent a general procedure to find community structures in concrete problems .
Revised:  In this paper, we represent a general procedure to find community structures in practice .
Edit intent: clarity
Original: Recent deep learning based approaches have shown promising results on image inpainting for the challenging task of filling in large missing regions in an image.
Revised: Recent deep learning based approaches have shown promising results for the challenging task of filling in large missing regions in an image.
Edit intent: clarity
Original: Recent deep learning based approaches have shown promising results on image inpainting for the challenging task of filling in large missing regions in an image.
Revised: Recent deep learning based approaches have shown promising results on image inpainting for the challenging task of inpainting large missing regions in an image.
Edit intent: clarity
Original:  Code and trained models will be released.
Revised:  Code , demo and models are available at: URL
Edit intent: coherence
Original: We investigate the performance of two common lift-and-project techniques , the Sherali-Adams (SA) and the Balas, Ceria and Cornu\'ejols (BCC) hierarchies, on the natural linear relaxation of the Bounded Color Matching polyhedron and generalizations.
Revised: We investigate the performance of two common lift-and-project technique , the Sherali-Adams (SA) and the Balas, Ceria and Cornu\'ejols (BCC) hierarchies, on the natural linear relaxation of the Bounded Color Matching polyhedron and generalizations.
Edit intent: fluency
Original: We investigate the performance of two common lift-and-project techniques , the Sherali-Adams (SA) and the Balas, Ceria and Cornu\'ejols (BCC) hierarchies, on the natural linear relaxation of the Bounded Color Matching polyhedron and generalizations.
Revised: We investigate the performance of two common lift-and-project techniques , the Sherali-Adams (SA) hierarchy, on the natural linear relaxation of the Bounded Color Matching polyhedron and generalizations.
Edit intent: coherence
Original: We investigate the performance of two common lift-and-project techniques , the Sherali-Adams (SA) and the Balas, Ceria and Cornu\'ejols (BCC) hierarchies, on the natural linear relaxation of the Bounded Color Matching polyhedron and generalizations.
Revised: We investigate the performance of two common lift-and-project techniques , the Sherali-Adams (SA) and the Balas, Ceria and Cornu\'ejols (BCC) hierarchies, on the natural linear relaxation of the Bounded Color Matching polyhedron and generalizations.
Edit intent: fluency
Original:  We prove the following unconditional inapproximability results  : even a large family of linear programs, generated by an asymptotically linear number of rounds of the Sherali-Adams hierarchy or a linear number of rounds of the BCC hierarchy, is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs.
Revised:  We prove the following unconditional : even a large family of linear programs, generated by an asymptotically linear number of rounds of the Sherali-Adams hierarchy or a linear number of rounds of the BCC hierarchy, is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs.
Edit intent: clarity
Original:  We prove the following unconditional inapproximability results  : even a large family of linear programs, generated by an asymptotically linear number of rounds of the Sherali-Adams hierarchy or a linear number of rounds of the BCC hierarchy, is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs.
Revised:  We prove the following unconditional inapproximability results : even a large family of linear programs, generated by an asymptotically linear number of rounds of the Sherali-Adams hierarchy or a linear number of rounds of the BCC hierarchy, is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs.
Edit intent: fluency
Original:  We prove the following unconditional inapproximability results  : even a large family of linear programs, generated by an asymptotically linear number of rounds of the Sherali-Adams hierarchy or a linear number of rounds of the BCC hierarchy, is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs.
Revised:  We prove the following unconditional inapproximability results : even a large family of linear programs, generated by an asymptotically linear number of rounds of the Sherali-Adams hierarchy is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs.
Edit intent: clarity
Original:  We complement these results by showing in a non-algorithmic way   that if we exclude certain  sub-structures from our instance graphs, then the integrality gap of the natural linear formulation  improves.
Revised:  We complement these results by showing in a non-algorithmic way , that if we exclude certain sub-structures from our instance graphs, then the integrality gap of the natural linear formulation improves.
Edit intent: fluency
Original:  We prove the followingunconditional inapproximability result: even a large family of linear programs, generated by anasymptotically linear number of rounds of the Sherali-Adams hierarchy is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs .
Revised:  We prove the following inapproximability result: even a large family of linear programs, generated by anasymptotically linear number of rounds of the Sherali-Adams hierarchy is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs .
Edit intent: clarity
Original:  We prove the followingunconditional inapproximability result: even a large family of linear programs, generated by anasymptotically linear number of rounds of the Sherali-Adams hierarchy is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs .
Revised:  We prove the followingunconditional asymptotically linear number of rounds of the Sherali-Adams hierarchy is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs .
Edit intent: clarity
Original:  We prove the followingunconditional inapproximability result: even a large family of linear programs, generated by anasymptotically linear number of rounds of the Sherali-Adams hierarchy is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs .
Revised:  We prove the followingunconditional inapproximability result: even a large family of linear programs, generated by an number of rounds of the Sherali-Adams hierarchy is not enough to improve the integrality gap of the natural LP relaxation even in bipartite graphs .
Edit intent: clarity
Original:  We complement these results by showing , adapting for our purposes the results of F\"{u}redi [Combinatorica, 1(2):155-162, 1981] , that if we exclude certain simple sub-structures from our instance graphs, then the integrality gap of the natural linear formulation strictly improves .
Revised:  We complement these results by showing that if we exclude certain simple sub-structures from our input graphs, then the integrality gap of the natural linear formulation strictly improves. To prove this, we adapt for our purposes the results of F\"{u}redi [Combinatorica, 1(2):155-162, 1981] , that if we exclude certain simple sub-structures from our instance graphs, then the integrality gap of the natural linear formulation strictly improves .
Edit intent: coherence
Original: Bar charts are an effective way for humans to convey information to each other , but today's algorithms cannot parse them.
Revised: Bar charts are an effective way to convey numeric information , but today's algorithms cannot parse them.
Edit intent: clarity
Original:  Existing methods fail when faced with  minor variations in appearance.
Revised:  Existing methods fail when faced with even minor variations in appearance.
Edit intent: clarity
Original: We propose a efficient method to calculate "the minimal annihilating polynomials" for all the unit vectors, of square matrix over the integers or the rational numbers.
Revised: We propose an efficient method to calculate "the minimal annihilating polynomials" for all the unit vectors, of square matrix over the integers or the rational numbers.
Edit intent: fluency
Original:  We propose an efficient algorithm for calculating the minimal annihilating polynomials for all the unit vectors via pseudo annihilating polynomials with the key idea of binary splitting technique.
Revised:  A new efficient method is proposed for calculating the minimal annihilating polynomials for all the unit vectors via pseudo annihilating polynomials with the key idea of binary splitting technique.
Edit intent: clarity
Original:  Efficiency of the proposed algorithm is shown by arithmetic time complexity analysis.
Revised:  Efficiency of the resulting algorithms is shown by arithmetic time complexity analysis.
Edit intent: clarity
Original:  A target  without line-of-sight to enough number of anchors cannot be unambiguously localized and is, therefore, said to be in a blind-spot .
Revised:  A target without line-of-sight ) to a sufficient number of anchors cannot be unambiguously localized and is, therefore, said to be in a blind-spot .
Edit intent: fluency
Original:  A target  without line-of-sight to enough number of anchors cannot be unambiguously localized and is, therefore, said to be in a blind-spot .
Revised:  A target without line-of-sight to enough number of anchors cannot be unambiguously localized and is, therefore, said to be in a blind spot .
Edit intent: fluency
Original:  In this paper, we analyze the blind-spot probability of a typical target by using stochastic geometry to model the randomness in the obstacle and anchor locations.
Revised:  In this paper, we analyze the blind spot probability of a typical target by using stochastic geometry to model the randomness in the obstacle and anchor locations.
Edit intent: fluency
Original:  In doing so, we handle correlated anchor blocking induced by  obstacles, unlike previous works that assume independent anchor blocking.
Revised:  In doing so, we handle correlated anchor blocking induced by the obstacles, unlike previous works that assume independent anchor blocking.
Edit intent: fluency
Original:  We first characterize the regime over which the independent blocking assumption underestimates the blind-spot probability of the typical target, which in turn, is characterized as a function of the distribution of the unshadowed area, as seen from the target location.
Revised:  We first characterize the regime over which the independent blocking assumption underestimates the blind spot probability of the typical target, which in turn, is characterized as a function of the distribution of the unshadowed area, as seen from the target location.
Edit intent: fluency
Original:  We first characterize the regime over which the independent blocking assumption underestimates the blind-spot probability of the typical target, which in turn, is characterized as a function of the distribution of the unshadowed area, as seen from the target location.
Revised:  We first characterize the regime over which the independent blocking assumption underestimates the blind-spot probability of the typical target, which in turn, is characterized as a function of the distribution of the visible area, surrounding the target location.
Edit intent: clarity
Original:  Based on this, we derive a closed-form (approximate ) expression for the blind-spot probability, which helps determine the anchor deployment intensity needed for the blind-spot probability of a typical target to be at most a threshold, \mu.
Revised:  Based on this, we derive an approximate expression for the blind-spot probability, which helps determine the anchor deployment intensity needed for the blind-spot probability of a typical target to be at most a threshold, \mu.
Edit intent: clarity
Original:  Based on this, we derive a closed-form (approximate ) expression for the blind-spot probability, which helps determine the anchor deployment intensity needed for the blind-spot probability of a typical target to be at most a threshold, \mu.
Revised:  Based on this, we derive a closed-form (approximate ) expression for the blind spot probability, which helps determine the anchor deployment intensity needed for the blind-spot probability of a typical target to be at most a threshold, \mu.
Edit intent: fluency
Original:  Based on this, we derive a closed-form (approximate ) expression for the blind-spot probability, which helps determine the anchor deployment intensity needed for the blind-spot probability of a typical target to be at most a threshold, \mu.
Revised:  Based on this, we derive a closed-form (approximate ) expression for the blind-spot probability, which helps determine the anchor deployment intensity needed for the blind spot probability of a typical target to be at most a threshold, \mu.
Edit intent: fluency
Original: In this paper, we prove that a degree d Boolean function depends on at most C\cdot 2^d variables for some C<22 , i.e. it is a C\cdot 2^d -junta .
Revised: In this paper, we prove that a degree d Boolean function depends on at most C\cdot 2^d -junta , i.e. it is a C\cdot 2^d -junta .
Edit intent: clarity
Original: In this paper, we prove that a degree d Boolean function depends on at most C\cdot 2^d variables for some C<22 , i.e. it is a C\cdot 2^d -junta .
Revised: In this paper, we prove that a degree d Boolean function depends on at most C\cdot 2^d variables for some C<22 , i.e. it depends on at most C\cdot 2^d -junta .
Edit intent: coherence
Original: In this paper, we prove that a degree d Boolean function depends on at most C\cdot 2^d variables for some C<22 , i.e. it is a C\cdot 2^d -junta .
Revised: In this paper, we prove that a degree d Boolean function depends on at most C\cdot 2^d variables for some C<22 , i.e. it is a C\cdot 2^d variables .
Edit intent: clarity
Original:  We note that a bound of C\cdot 2^d is essentially tight .
Revised:  The bound of C\cdot 2^d is essentially tight .
Edit intent: coherence
Original:  A lower bound of 2^d-1 can easily be achieved by a read-once decision tree of depth d (see O'Donnell 14%DIFDELCMD < ]%%% , Exercise 3.24) .
Revised:  A lower bound of 2^d-1 is achieved by a read-once decision tree of depth d (see O'Donnell 14%DIFDELCMD < ]%%% , Exercise 3.24) .
Edit intent: clarity
Original:  A lower bound of 2^d-1 can easily be achieved by a read-once decision tree of depth d (see O'Donnell 14%DIFDELCMD < ]%%% , Exercise 3.24) .
Revised:  A lower bound of 2^d-1 can easily be achieved by a read-once decision tree of depth d O'Donnell 14%DIFDELCMD < ]%%% , Exercise 3.24) .
Edit intent: coherence
Original:  A lower bound of 2^d-1 can easily be achieved by a read-once decision tree of depth d (see O'Donnell 14%DIFDELCMD < ]%%% , Exercise 3.24) .
Revised:  A lower bound of 2^d-1 can easily be achieved by a read-once decision tree of depth d (see %DIFDELCMD < ]%%% , Exercise 3.24) .
Edit intent: coherence