wanyu/IteraTeR_v2 huggingface dataset:
Original:  We show that the statistical information contained in the resampled data allows us to build surrogate versions of the unknown contacts and that simulations of epidemic processes using these surrogate data sets yield good estimates of the outcome of simulations performed using the complete data set.
Revised:  We show that the statistical information contained in the resampled data can be used to build a series of surrogate versions of the unknown contacts and that simulations of epidemic processes using these surrogate data sets yield good estimates of the outcome of simulations performed using the complete data set.
Edit intent: clarity
Original:  The hyperparameters of all LSTM variants for each task were optimized separately using random search  and their importance was assessed using the powerful fANOVA framework.
Revised:  The hyperparameters of all LSTM variants for each task were optimized separately using random search , and their importance was assessed using the powerful fANOVA framework.
Edit intent: fluency
Original:  In total, we summarize the results of 5400 experimental runs ( about 15 years of CPU time), which makes our study the largest of its kind on LSTM networks.
Revised:  In total, we summarize the results of 5400 experimental runs ( \approx 15 years of CPU time), which makes our study the largest of its kind on LSTM networks.
Edit intent: clarity
Original:  It is now possible to sequence these BCRs in high throughput , and analysis of these sequences is bringing new insight into how antibodies develop, in particular for broadly-neutralizing antibodies against HIV and influenza.
Revised:  It is now possible to sequence these BCRs in high throughput ; analysis of these sequences is bringing new insight into how antibodies develop, in particular for broadly-neutralizing antibodies against HIV and influenza.
Edit intent: coherence
Original:  A fundamental step in such sequence analysis is to annotate each base as coming from a specific one of the V, D, or J genes, or from a non-templated insertion  . Previous work has used simple parametric distributions to model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations occur via the same process across sites.
Revised:  A fundamental step in such sequence analysis is to annotate each base as coming from a specific one of the V, D, or J genes, or from an N-addition (a.k.a. non-templated insertion . Previous work has used simple parametric distributions to model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations occur via the same process across sites.
Edit intent: clarity
Original:  A fundamental step in such sequence analysis is to annotate each base as coming from a specific one of the V, D, or J genes, or from a non-templated insertion  . Previous work has used simple parametric distributions to model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations occur via the same process across sites.
Revised:  A fundamental step in such sequence analysis is to annotate each base as coming from a specific one of the V, D, or J genes, or from a non-templated insertion ) . Previous work has used simple parametric distributions to model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations occur via the same process across sites.
Edit intent: fluency
Original:  Our method predicts, with a success rate of over 60%DIFDELCMD < \\%%% %DIF < , how popular a movie will become in the future .
Revised:  Our method predicts, with a success rate of over %DIFDELCMD < \\%%% %DIF < , how popular a movie will become in the future .
Edit intent: clarity
Original:  We show that SPAI may be costly and even impractical to implement, especially when a given coefficient matrix is irregular sparse, i.e., it has at least one relatively dense column.
Revised:  We show that SPAI may be costly , which is especially true when a given coefficient matrix is irregular sparse, i.e., it has at least one relatively dense column.
Edit intent: clarity
Original:  In this paper, we propose a residual based SPAI (RSAI)  algorithm, which , in a different way from the SPAI algorithm , augments the sparsity pattern of an approximate inverse M dynamically .
Revised:  In this paper, we propose a residual based SPAI (RSAI) algorithm, which is different from the SPAI algorithm , augments the sparsity pattern of an approximate inverse M dynamically .
Edit intent: clarity
Original:  In this paper, we propose a residual based SPAI (RSAI)  algorithm, which , in a different way from the SPAI algorithm , augments the sparsity pattern of an approximate inverse M dynamically .
Revised:  In this paper, we propose a residual based SPAI (RSAI) algorithm, which , in a different way from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically .
Edit intent: coherence
Original:  To control the sparsity of M  , we develop a practical RSAI(tol) algorithm which drops small entries in magnitude generated during the setup process of M . Numerical experiments are reported to verify the effectiveness of the RSAI algorithm , and numerical comparisons are made for the RSAI algorithm, the SPAI algorithm and the PSAI(tol) algorithm  .
Revised:  To control the sparsity of M , we develop a practical RSAI(tol) algorithm which drops small entries in magnitude generated during loops . Numerical experiments are reported to verify the effectiveness of the RSAI algorithm , and numerical comparisons are made for the RSAI algorithm, the SPAI algorithm and the PSAI(tol) algorithm .
Edit intent: clarity
Original:  To control the sparsity of M  , we develop a practical RSAI(tol) algorithm which drops small entries in magnitude generated during the setup process of M . Numerical experiments are reported to verify the effectiveness of the RSAI algorithm , and numerical comparisons are made for the RSAI algorithm, the SPAI algorithm and the PSAI(tol) algorithm  .
Revised:  To control the sparsity of M , we develop a practical RSAI(tol) algorithm which drops small entries in magnitude generated during the setup process of M . Numerical experiments are reported to verify the effectiveness of the RSAI algorithm . We also make numerical comparisons of the RSAI(tol) algorithm, the SPAI algorithm and the PSAI(tol) algorithm .
Edit intent: clarity
Original:  The results indicate that the RSAIalgorithm is at least competitive to the SPAI algorithm and comparably as effective as the PSAI(tol) for general problems .
Revised:  The results indicate that RSAI(tol)) is at least competitive to the SPAI algorithm and comparably as effective as the PSAI(tol) for general problems .
Edit intent: clarity
Original:  The results indicate that the RSAIalgorithm is at least competitive to the SPAI algorithm and comparably as effective as the PSAI(tol) for general problems .
Revised:  The results indicate that the RSAIalgorithm is at least competitive to the SPAI algorithm and comparably as effective as the PSAI(tol) .
Edit intent: clarity
Original:  We revisit the SPAI algorithm proposed by Grote and Huckle [SIAM J. Sci. Comput., 18 (1997), pp.~838--853.], which is based on the F-norm minimization and updates approximate sparsity patterns adaptively.
Revised:  We revisit the SPAI algorithm proposed by Grote and Huckle [SIAM J. Sci. Comput., 18 (1997), pp.~838--853.], is based on the F-norm minimization and updates approximate sparsity patterns adaptively.
Edit intent: coherence
Original:  In this paper, we propose a new residual based sparse approximate inverse  (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em   . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture  good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Revised:  In this paper, we propose a residual based sparse approximate inverse (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Edit intent: clarity
Original:  In this paper, we propose a new residual based sparse approximate inverse  (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em   . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture  good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Revised:  In this paper, we propose a new residual based sparse approximate inverse preconditioning procedure (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Edit intent: clarity
Original:  In this paper, we propose a new residual based sparse approximate inverse  (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em   . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture  good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Revised:  In this paper, we propose a new residual based sparse approximate inverse (RSAI) , which, unlike SPAI, is based on only the{\em . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Edit intent: coherence
Original:  In this paper, we propose a new residual based sparse approximate inverse  (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em   . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture  good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Revised:  In this paper, we propose a new residual based sparse approximate inverse (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em dominant . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Edit intent: fluency
Original:  In this paper, we propose a new residual based sparse approximate inverse  (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em   . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture  good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Revised:  In this paper, we propose a new residual based sparse approximate inverse (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em . RSAI is less costly to seek indices and is more effective to capture good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Edit intent: coherence
Original:  In this paper, we propose a new residual based sparse approximate inverse  (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em   . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture  good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Revised:  In this paper, we propose a new residual based sparse approximate inverse (RSAI) preconditioning algorithm, whichis different from the SPAI algorithm and augments the sparsity pattern of an approximate inverse M dynamically in a cheaper way than SPAI{\em . RSAI is less costly to seek and add indices than SPAI during loops, and it is more effective to capture a good approximate sparsity patterns of the inverse of a given sparse matrix than SPAI.
Edit intent: fluency
Original:  To control the sparsity of M and improve efficiency , we develop a practical RSAI(tol) algorithm which drops small entries in magnitude generated during loops.
Revised:  To control the sparsity of M and reduce computational cost , we develop a practical RSAI(tol) algorithm which drops small entries in magnitude generated during loops.
Edit intent: clarity
Original:  To control the sparsity of M and improve efficiency , we develop a practical RSAI(tol) algorithm which drops small entries in magnitude generated during loops.
Revised:  To control the sparsity of M and improve efficiency , we develop a practical RSAI(tol) algorithm which drops small entries in magnitude during the loops.
Edit intent: clarity
Original:  The results indicate that RSAI(tol) ) is at least competitive with SPAI and can be considerably more effective than the latter for difficult problems, and it is also comparably effective as PSAI(tol) .
Revised:  The results indicate that RSAI(tol) is at least competitive with SPAI and can be considerably more effective than the latter for difficult problems, and it is also comparably effective as PSAI(tol) .
Edit intent: fluency
Original:  The results indicate that RSAI(tol) ) is at least competitive with SPAI and can be considerably more effective than the latter for difficult problems, and it is also comparably effective as PSAI(tol) .
Revised:  The results indicate that RSAI(tol) ) is at least competitive with SPAI and can be considerably more efficient and effective than SPAI. They also indicate that RSAI(tol) is comparable to the PSAI(tol) .
Edit intent: clarity
Original:  To control the sparsity of M and reduce computational cost, we develop a practical RSAI(tol) algorithm that drops small nonzero entries in magnitude during the loops . Numerical experiments are reported to demonstrate that RSAI(tol) is at least competitive with SPAI and can be considerably more efficient and effective than SPAI.
Revised:  To control the sparsity of M and reduce computational cost, we develop a practical RSAI(tol) algorithm that drops small nonzero entries adaptively during the process . Numerical experiments are reported to demonstrate that RSAI(tol) is at least competitive with SPAI and can be considerably more efficient and effective than SPAI.
Edit intent: clarity
Original: The paradigm of phenotypic plasticity indicates reversible relations of different cancer cell phenotypes, which challenges the cellular hierarchy proposed by the conventional cancer stem cell (CSC) theory.
Revised: The paradigm of phenotypic plasticity indicates reversible relations of different cancer cell phenotypes, which extends the cellular hierarchy proposed by the conventional cancer stem cell (CSC) theory.
Edit intent: clarity
Original: The paradigm of phenotypic plasticity indicates reversible relations of different cancer cell phenotypes, which challenges the cellular hierarchy proposed by the conventional cancer stem cell (CSC) theory.
Revised: The paradigm of phenotypic plasticity indicates reversible relations of different cancer cell phenotypes, which challenges the cellular hierarchy proposed by the classical cancer stem cell (CSC) theory.
Edit intent: clarity
Original:  Since the validity of the reversible model versus the hierarchical model of cancer cells is still experimentally debated , it is worthwhile to theoretically explore the dynamic behavior characterizing the reversible model in comparison of the hierarchical model .
Revised:  Since the validity of the reversible model versus the hierarchical model of cancer cells is still experimentally debated , it is worthwhile to explore the dynamic behavior characterizing the reversible model in comparison of the hierarchical model .
Edit intent: clarity
Original:  By comparing the two models in predicting the cell-state dynamics observed in biological experiments , our results imply that the reversible model has advantages over the hierarchical model in predicting both long-term stable and short-term transient dynamics  of cancer cells.
Revised:  By comparing the two models in predicting the cell-state dynamics observed in biological experiments . Our results show that the hierarchical model shows significant disadvantages over the reversible model in describing both long-term stable and short-term transient dynamics of cancer cells.
Edit intent: clarity
Original:  Due to its training speed and very few tunable parameters, the method has strong potential for embedded hardware applications requiring frequent retraining or online training.
Revised:  Due to its training speed and very few tunable parameters, the method has strong potential for applications requiring frequent retraining or online training.
Edit intent: clarity
Original:  We demonstrate the efficacy of the method as an image classifier, obtaining state-of-the-art results on the MNIST (0.37\% error) and NORB-small (2.2\%  ) image classification databases,  with very fast training times compared to standard deep network approaches.
Revised:  We demonstrate the efficacy of the method by applying it to image classification. Our results match existing state-of-the-art results on the MNIST (0.37\% error) and NORB-small (2.2\% ) image classification databases, with very fast training times compared to standard deep network approaches.
Edit intent: clarity
Original:  We demonstrate the efficacy of the method as an image classifier, obtaining state-of-the-art results on the MNIST (0.37\% error) and NORB-small (2.2\%  ) image classification databases,  with very fast training times compared to standard deep network approaches.
Revised:  We demonstrate the efficacy of the method as an image classifier, obtaining state-of-the-art results on the MNIST (0.37\% error) and NORB-small (2.2\% error ) image classification databases, with very fast training times compared to standard deep network approaches.
Edit intent: fluency
Original:  We demonstrate the efficacy of the method as an image classifier, obtaining state-of-the-art results on the MNIST (0.37\% error) and NORB-small (2.2\%  ) image classification databases,  with very fast training times compared to standard deep network approaches.
Revised:  We demonstrate the efficacy of the method as an image classifier, obtaining state-of-the-art results on the MNIST (0.37\% error) and NORB-small (2.2\% ) image classification databases, but with very fast training times compared to standard deep network approaches.
Edit intent: coherence
Original:  The second equation contains an integral term describing the random onset of the disease as a jump process localized in particularly sensitive areas of the brain.
Revised:  The second equation contains an integral term describing the random onset of the disease as a jump process localised in particularly sensitive areas of the brain.
Edit intent: fluency
Original:  Even though we deliberately neglect many aspects of the complexity of the brain and the disease, numerical simulations are in good qualitative agreement with clinical images of the disease distribution in the brain which vary from early to advanced stages.
Revised:  Our numerical simulations are in good qualitative agreement with clinical images of the disease distribution in the brain which vary from early to advanced stages.
Edit intent: coherence
Original:  More precisely, we prove that for every signed digraph G there exists a system f:A^n\to A^n with G as interaction graph such that f^{\lfloor\log_2 n\rfloor+2\lfloor\rfloor .
Revised:  More precisely, we prove that for every signed digraph G there exists a system f:A^n\to A^n with G as interaction graph such that f^{\lfloor\log_2 n\rfloor+2\lfloor\log_2 n\rfloor .
Edit intent: fluency
Original:  More precisely, we prove that for every signed digraph G there exists a system f:A^n\to A^n with G as interaction graph such that f^{\lfloor\log_2 n\rfloor+2\lfloor\rfloor .
Revised:  More precisely, we prove that for every signed digraph G there exists a system f:A^n\to A^n with G as interaction graph such that f^{\lfloor\log_2 n\rfloor+2\lfloor\rfloor+2 steps .
Edit intent: clarity
Original:  We analyze how isometric cycles in partial cubes behave and derive that every partial cube of girth more than six must have vertices of degree less than three.
Revised:  We analyze how isometric cycles in partial cubes behave and derive that every partial cube of girth more than 6 must have vertices of degree less than three.
Edit intent: fluency
Original:  We analyze how isometric cycles in partial cubes behave and derive that every partial cube of girth more than six must have vertices of degree less than three.
Revised:  We analyze how isometric cycles in partial cubes behave and derive that every partial cube of girth more than six must have vertices of degree less than 3.
Edit intent: fluency
Original:  As a direct corollary we get that every regular partial cube of girth more than six is an even cycle.
Revised:  As a direct corollary we get that every regular partial cube of girth more than 6 is an even cycle.
Edit intent: fluency
Original: Continuous interpolation of real-valued data is characterized by piecewise monotone functions on a compact metric space.
Revised: Continuous interpolation of real-valued data is characterized by piecewise monotone functions on a compact metric space.
Edit intent: fluency
Original:  A varlet basis is a collection of piecewise monotone functions { g_i |i = 1...n}, called varlets , such that every linear combination \sum a_ig_i has topological total variation \sum |a_i|.
Revised:  A varilet basis is a collection of piecewise monotone functions { g_i |i = 1...n}, called varlets , such that every linear combination \sum a_ig_i has topological total variation \sum |a_i|.
Edit intent: fluency
Original:  A varlet basis is a collection of piecewise monotone functions { g_i |i = 1...n}, called varlets , such that every linear combination \sum a_ig_i has topological total variation \sum |a_i|.
Revised:  A varlet basis is a collection of piecewise monotone functions { g_i |i = 1...n}, called varilets , such that every linear combination \sum a_ig_i has topological total variation \sum |a_i|.
Edit intent: clarity
Original:  A varlet transform for f is a varlet basis for which f =\sum \alpha_ig_i.
Revised:  A varilet transform for f is a varlet basis for which f =\sum \alpha_ig_i.
Edit intent: fluency
Original:  A varlet transform for f is a varlet basis for which f =\sum \alpha_ig_i.
Revised:  A varlet transform for f is a varilet basis for which f =\sum \alpha_ig_i.
Edit intent: fluency
Original:  Thus the single format can support a SpMV algorithm that is efficient both for regular matrices and for irregular matrices.
Revised:  Thus the single format can support an SpMV algorithm that is efficient both for regular matrices and for irregular matrices.
Edit intent: fluency
Original:  Furthermore, we show that the overhead of the format conversion from the CSR to the CSR5 can be as low as  cost of a few SpMV operations.
Revised:  Furthermore, we show that the overhead of the format conversion from the CSR to the CSR5 can be as low as the cost of a few SpMV operations.
Edit intent: fluency