wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we perform a large-scale benchmarking study on these  choices, with two key experiments evaluated on a real-world object pose estimation task , which is also a proxy for end-to-end visual control .
Revised:  In this paper, we perform a comprehensive benchmarking study on these choices, with two key experiments evaluated on a real-world object pose estimation task , which is also a proxy for end-to-end visual control .
Edit intent: clarity
Original:  In this paper, we perform a large-scale benchmarking study on these  choices, with two key experiments evaluated on a real-world object pose estimation task , which is also a proxy for end-to-end visual control .
Revised:  In this paper, we perform a large-scale benchmarking study on these different choices, with two key experiments evaluated on a real-world object pose estimation task , which is also a proxy for end-to-end visual control .
Edit intent: meaning-changed
Original:  We draw on the social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous .
Revised:  We draw on social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous .
Edit intent: fluency
Original:  We draw on the social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous .
Revised:  We draw on the social science corpora to suggest ways for improving the rigor of studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous .
Edit intent: clarity
Original:  We draw on the social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous .
Revised:  We draw on the social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers use observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous .
Edit intent: fluency
Original:  We draw on the social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data more rigorous .
Revised:  We draw on the social science corpora of qualitative research to illustrate opportunities for making the human studies where XAI researchers used observations, interviews, focus groups, and/or questionnaires to capture qualitative data .
Edit intent: clarity
Original:  We contextualize the presentation of the XAI contributions included in our analysis according to the components of rigor described in the qualitative research literature: 1) underlying theories or frameworks, 2) methodological approaches, 3) data collection methods, and 4) data analysis processes.
Revised:  We contextualize the presentation of the XAI papers included in our analysis according to the components of rigor described in the qualitative research literature: 1) underlying theories or frameworks, 2) methodological approaches, 3) data collection methods, and 4) data analysis processes.
Edit intent: clarity
Original:  The oracle can answer queries of the form " Is s a substring of S? " . The Substring Query Complexity of a string S, denoted \chi(S), is the minimum number of adaptive substring queries that are needed to exactly reconstruct (or learn) S. It has been introduced in 1995 by Skiena and Sundaram, who showed that \chi(S) \geq \sigma n/4 -
Revised:  The oracle can answer queries of the form `` Is s a substring of S? " . The Substring Query Complexity of a string S, denoted \chi(S), is the minimum number of adaptive substring queries that are needed to exactly reconstruct (or learn) S. It has been introduced in 1995 by Skiena and Sundaram, who showed that \chi(S) \geq \sigma n/4 -
Edit intent: fluency
Original:  The oracle can answer queries of the form " Is s a substring of S? " . The Substring Query Complexity of a string S, denoted \chi(S), is the minimum number of adaptive substring queries that are needed to exactly reconstruct (or learn) S. It has been introduced in 1995 by Skiena and Sundaram, who showed that \chi(S) \geq \sigma n/4 -
Revised:  The oracle can answer queries of the form " Is s a substring of S? '' . The Substring Query Complexity of a string S, denoted \chi(S), is the minimum number of adaptive substring queries that are needed to exactly reconstruct (or learn) S. It has been introduced in 1995 by Skiena and Sundaram, who showed that \chi(S) \geq \sigma n/4 -
Edit intent: fluency
Original:  However, since this result does not yield an efficient algorithm for the reconstruction, we  present new algorithms to compute a set of substring queries whose size grows as a function of other known measures of complexity, e.g., the number %DIFDELCMD < {\sf %%% rle of runs in S, the size g of the smallest grammar producing (only) S  or the size z_{no} of the non-overlapping LZ77 factorization of S.
Revised:  However, since this result does not yield an efficient algorithm for the reconstruction, we also present new algorithms to compute a set of substring queries whose size grows as a function of other known measures of complexity, e.g., the number %DIFDELCMD < {\sf %%% rle of runs in S, the size g of the smallest grammar producing (only) S or the size z_{no} of the non-overlapping LZ77 factorization of S.
Edit intent: coherence
Original:  However, since this result does not yield an efficient algorithm for the reconstruction, we  present new algorithms to compute a set of substring queries whose size grows as a function of other known measures of complexity, e.g., the number %DIFDELCMD < {\sf %%% rle of runs in S, the size g of the smallest grammar producing (only) S  or the size z_{no} of the non-overlapping LZ77 factorization of S.
Revised:  However, since this result does not yield an efficient algorithm for the reconstruction, we present new algorithms which require a number of substring queries whose size grows as a function of other known measures of complexity, e.g., the number %DIFDELCMD < {\sf %%% rle of runs in S, the size g of the smallest grammar producing (only) S or the size z_{no} of the non-overlapping LZ77 factorization of S.
Edit intent: clarity
Original:  However, since this result does not yield an efficient algorithm for the reconstruction, we  present new algorithms to compute a set of substring queries whose size grows as a function of other known measures of complexity, e.g., the number %DIFDELCMD < {\sf %%% rle of runs in S, the size g of the smallest grammar producing (only) S  or the size z_{no} of the non-overlapping LZ77 factorization of S.
Revised:  However, since this result does not yield an efficient algorithm for the reconstruction, we present new algorithms to compute a set of substring queries bounded by other known measures of complexity, e.g., the number %DIFDELCMD < {\sf %%% rle of runs in S, the size g of the smallest grammar producing (only) S or the size z_{no} of the non-overlapping LZ77 factorization of S.
Edit intent: clarity
Original:  However, since this result does not yield an efficient algorithm for the reconstruction, we  present new algorithms to compute a set of substring queries whose size grows as a function of other known measures of complexity, e.g., the number %DIFDELCMD < {\sf %%% rle of runs in S, the size g of the smallest grammar producing (only) S  or the size z_{no} of the non-overlapping LZ77 factorization of S.
Revised:  However, since this result does not yield an efficient algorithm for the reconstruction, we present new algorithms to compute a set of substring queries whose size grows as a function of other known measures of complexity, e.g., the number %DIFDELCMD < {\sf %%% rle of runs in S, the size g of the smallest grammar producing (only) S or the size z_{no} of the non-overlapping LZ77 factorization of S.
Edit intent: fluency
Original:  However, since this result does not yield an efficient algorithm for the reconstruction, we  present new algorithms to compute a set of substring queries whose size grows as a function of other known measures of complexity, e.g., the number %DIFDELCMD < {\sf %%% rle of runs in S, the size g of the smallest grammar producing (only) S  or the size z_{no} of the non-overlapping LZ77 factorization of S.
Revised:  However, since this result does not yield an efficient algorithm for the reconstruction, we present new algorithms to compute a set of substring queries whose size grows as a function of other known measures of complexity, e.g., the number %DIFDELCMD < {\sf %%% rle of runs in S, the size g of the smallest grammar producing (only) S , or the size z_{no} of the non-overlapping LZ77 factorization of S.
Edit intent: fluency
Original:  We first show that any string of length n over an integer alphabet of size \sigma with %DIFDELCMD < {\sf %%% rle runs can be reconstructed with q=O( %DIFDELCMD < {\sf %%% rle (\sigma + \log \frac{n{{\sf rle}}}  )) substring queries in linear time and space.
Revised:  We first show that any string of length n over an integer alphabet of size \sigma with %DIFDELCMD < {\sf %%% rle runs can be reconstructed with q=O( %DIFDELCMD < {\sf %%% rle (\sigma + \log \frac{n{{\sf rle}}} )) substring queries in linear time and space.
Edit intent: fluency
Original:  We first show that any string of length n over an integer alphabet of size \sigma with %DIFDELCMD < {\sf %%% rle runs can be reconstructed with q=O( %DIFDELCMD < {\sf %%% rle (\sigma + \log \frac{n{{\sf rle}}}  )) substring queries in linear time and space.
Revised:  We first show that any string of length n over an integer alphabet of size \sigma with %DIFDELCMD < {\sf %%% rle runs can be reconstructed with q=O( %DIFDELCMD < {\sf %%% rle (\sigma + \log {{\sf rle}}} )) substring queries in linear time and space.
Edit intent: clarity
Original:  We first show that any string of length n over an integer alphabet of size \sigma with %DIFDELCMD < {\sf %%% rle runs can be reconstructed with q=O( %DIFDELCMD < {\sf %%% rle (\sigma + \log \frac{n{{\sf rle}}}  )) substring queries in linear time and space.
Revised:  We first show that any string of length n over an integer alphabet of size \sigma with %DIFDELCMD < {\sf %%% rle runs can be reconstructed with q=O( %DIFDELCMD < {\sf %%% rle (\sigma + \log \frac{n{{\sf rle}}} \frac{n )) substring queries in linear time and space.
Edit intent: meaning-changed
Original:  We then present an algorithm that spends q \in O(\sigma g\log n) \subseteq O(\sigma z_{no}\log (n/z_{no})\log n) substring queries and runs in O(n(\log n + \log \sigma)+ q) time using linear space . This algorithm actually reconstructs the suffix tree of the string using a dynamic approach based on the centroid decomposition .
Revised:  We then present an algorithm that spends q \in O(\sigma g\log n) \subseteq O(\sigma z_{no}\log (n/z_{no})\log n) substring queries and runs in O(n(\log n + \log \sigma)+ q) time using linear space .
Edit intent: meaning-changed
Original:  To the best of our knowledge, this is the first time that a hybrid model consisting of a full physics engine along with a statistical function approximator has been used to control a complex physical system in real-time using nonlinear model-predictive control (NMPC) . Codes for the simulation environment can be downloaded here URL . A video describing our method could be found here URL .
Revised:  To the best of our knowledge, this is the first time that a hybrid model consisting of a full physics engine along with a statistical function approximator has been used to control a complex physical system in real-time using nonlinear model-predictive control (NMPC) .
Edit intent: coherence
Original:  For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using \mathcal{O}\left(\log n{\epsilon^2}\right) bits of space for p\in[0,2] , and using \mathcal{O}\left(1{\epsilon^2}n^{1-2/p}\right) bits of space for integers p>2.
Revised:  For adversarially robust streams, we obtain a (1+\epsilon)-approximation to F_p using \mathcal{O}\left(\log n{\epsilon^2}\right) bits of space for p\in[0,2] and using \mathcal{O}\left(1{\epsilon^2}n^{1-2/p}\right) bits of space for integers p>2.
Edit intent: fluency
Original:  We also obtain an adversarially robust algorithm for the L_2-heavy hitters problem using \mathcal{O \mathcal{O} \left(\log n{\epsilon^2}\right) bits of space.
Revised:  We also obtain an adversarially robust algorithm for the L_2-heavy hitters problem using \mathcal{O} \left(\log n{\epsilon^2}\right) bits of space.
Edit intent: fluency
Original:  Our bounds are optimal up to poly(\log\log n+\log(1/\epsilon)) factors, and improve the 1{\epsilon^3} dependence of Ben-Eliezer , et al.
Revised:  Our bounds are optimal up to poly(\log\log n+\log(1/\epsilon)) factors, and improve the 1{\epsilon^3} dependence of Ben-Eliezer et al.
Edit intent: fluency
Original:  For sliding windows, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log^2 n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in(0,2], resolving a longstanding question of Braverman and Ostrovsky (FOCS 2007).
Revised:  For sliding windows, we obtain a (1+\epsilon)-approximation to F_p %DIFDELCMD < \mathcal{O}\left(%%% \frac{\log^2 n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in(0,2], resolving a longstanding question of Braverman and Ostrovsky (FOCS 2007).
Edit intent: fluency
Original:  For sliding windows, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log^2 n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in(0,2], resolving a longstanding question of Braverman and Ostrovsky (FOCS 2007).
Revised:  For sliding windows, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% {\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in(0,2], resolving a longstanding question of Braverman and Ostrovsky (FOCS 2007).
Edit intent: meaning-changed
Original:  For sliding windows, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log^2 n{\epsilon^2}}%DIFDELCMD < \right) %%% bits of space for p\in(0,2], resolving a longstanding question of Braverman and Ostrovsky (FOCS 2007).
Revised:  For sliding windows, we obtain a (1+\epsilon)-approximation to F_p using%DIFDELCMD < \mathcal{O}\left(%%% \frac{\log^2 n{\epsilon^2}}%DIFDELCMD < \right) %%% for p\in(0,2], resolving a longstanding question of Braverman and Ostrovsky (FOCS 2007).
Edit intent: clarity
Original: Having suitable (approximate) explicit analytic solutions of the well known epidemic model SIR( D) would help in studying the data through easy fit. This paper provides an effective way .
Revised: Having suitable (approximate) explicit analytic solutions of the well known epidemic model SIR( D) epidemiological model is defined through transcendental equations not solvable by elementary functions. In the present paper, those equations are successfully replaced by approximate ones, whose solutions are given explicitly in terms of elementary functions, namely, piece-wisely, generalized logistic functions: they unveil a useful feature, that in fact is also owned by the (numerical) solutions of the exact equations .
Edit intent: meaning-changed
Original:  The machine learning (ML) community has mostly considered explainabilityas a proxy for transparency. With this work, we seek to encourage researchers to study uncertainty as a form of transparency and practitioners to communicate uncertainty estimates to stakeholders . First, we discuss methods for assessing uncertainty.
Revised:  Until now, most research into algorithmic transparency has predominantly focused on explainability. Explainability attempts to provide reasons for a machine learning model's behavior to stakeholders. However, understanding a model's specific behavior alone might not be enough for stakeholders to gauge whether the model is wrong or lacks sufficient knowledge to solve the task at hand. In this paper, we argue for considering a complementary form of transparency and practitioners to communicate uncertainty estimates to stakeholders . First, we discuss methods for assessing uncertainty.
Edit intent: coherence
Original: We design well-balanced numerical algorithms with first-, second-, and third-order of accuracy for the spherically symmetry evolution of a compressible fluid  on a Schwarzschild black hole .
Revised: We design well-balanced numerical algorithms with first-, second-, and third-order of accuracy for the spherically symmetry evolution of a compressible fluid on a Schwarzschild curved background, we design a class of well-balanced numerical algorithms with first-order or second-order of accuracy .
Edit intent: meaning-changed
Original:  Our schemes follow the finite volume methodology and preserve the stationary solutions and allow us to investigate the global asymptotic behavior of such flows and reach definite conclusions about the behavior of the mass density and velocity field  .
Revised:  Our schemes follow the finite volume methodology and preserve the stationary solutions . Importantly, they allow us to investigate the global asymptotic behavior of such flows and reach definite conclusions about the behavior of the mass density and velocity field .
Edit intent: coherence
Original:  Our schemes follow the finite volume methodology and preserve the stationary solutions and allow us to investigate the global asymptotic behavior of such flows and reach definite conclusions about the behavior of the mass density and velocity field  .
Revised:  Our schemes follow the finite volume methodology and preserve the stationary solutions and allow us to investigate the global asymptotic behavior of such flows and reach definite conclusions about the behavior of the mass density and velocity field of the fluid .
Edit intent: clarity
Original:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Revised:  In this work, we observed wide variation in Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Edit intent: clarity
Original:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Revised:  In this work, we extracted a wide variation of Ca2+ binding loop conformations of the Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Edit intent: clarity
Original:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Revised:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ -binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Edit intent: fluency
Original:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Revised:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) , which adopts the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Edit intent: coherence
Original:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Revised:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structures determined from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Edit intent: clarity
Original:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Revised:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on all twelve amino acids in the loop that coordinate the Ca2+ in aqueous solution.
Edit intent: clarity
Original:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate the Ca2+ in aqueous solution.
Revised:  In this work, we extracted a wide variation of Ca2+ binding loop conformations from Ca2+ binding protein calmodulin (CaM) that adopt the most populated ternary structure types from the MD simulations, followed by ab initio quantum mechanical (QM) calculations on the 12 amino acids from entire loop that coordinate Ca2+ in aqueous solution.
Edit intent: fluency
Original:  We discover that the atom radius of Ca2+ in conventional force fields is too large for the QM calculation to capture the variation in  coordination geometry of Ca2+ in its ionic form and lead to unphysical charges.
Revised:  We discovered that the atomic radius of Ca2+ in conventional force fields is too large for the QM calculation to capture the variation in coordination geometry of Ca2+ in its ionic form and lead to unphysical charges.
Edit intent: fluency