wanyu/IteraTeR_v2 huggingface dataset:
Original: An proof is presented that gene regulatory networks (GRNs) based solely on protein transcription factors (TFs) cannot control the development of complex multicellular life.
Revised: An proof is presented that gene regulatory networks (GRNs) based solely on transcription factors cannot control the development of complex multicellular life.
Edit intent: clarity
Original:  Hence, GRNs cannot explain the evolution of multicellular life in the Cambrian Explosion.
Revised:  GRNs alone cannot explain the evolution of multicellular life in the Cambrian Explosion.
Edit intent: clarity
Original:  Networks are based on an addressing system which is used to construct network links.
Revised:  Networks are based on addressing systems which are used to construct network links.
Edit intent: fluency
Original:  It has been assumed that combinations of protein transcription factors (TFs) generate a large enough address space to form gene regulatory networks that are complex enough to control the development of complex multicellular life.
Revised:  It has been assumed that combinations of transcription factors generate a large enough address space to form gene regulatory networks that are complex enough to control the development of complex multicellular life.
Edit intent: clarity
Original:  It has been assumed that combinations of protein transcription factors (TFs) generate a large enough address space to form gene regulatory networks that are complex enough to control the development of complex multicellular life.
Revised:  It has been assumed that combinations of protein transcription factors (TFs) generate a large enough address space to form GRNs that are complex enough to control the development of complex multicellular life.
Edit intent: clarity
Original:  We prove that TFs do not have sufficient combinatorial power to serve as the basis of an addressing system for regulatory control of genomes in the development of complex organisms.
Revised:  However, it is shown in this article that transcription factors do not have sufficient combinatorial power to serve as the basis of an addressing system for regulatory control of genomes in the development of complex organisms.
Edit intent: coherence
Original:  I show that given n transcription factor genes in a genome and address combinations of length k then there are at most n/k k-length TF-addresses in the address space.
Revised:  I show that given n transcription factor genes in a genome and address combinations of length k then there are at most n/k k-length transcription factor addresses in the address space.
Edit intent: clarity
Original:  Therefore,  different addressing system must exist to form the complex control networks required for complex control systems.
Revised:  Therefore, a different addressing system must exist to form the complex control networks required for complex control systems.
Edit intent: fluency
Original: Computational discovery of microRNAs (miRNA) is based on a pre-determined set of features from miRNA precursors (pre-miRNA).
Revised: Computational discovery of microRNAs (miRNA) is based on pre-determined set of features from miRNA precursors (pre-miRNA).
Edit intent: fluency
Original: Computational discovery of microRNAs (miRNA) is based on a pre-determined set of features from miRNA precursors (pre-miRNA).
Revised: Computational discovery of microRNAs (miRNA) is based on a pre-determined sets of features from miRNA precursors (pre-miRNA).
Edit intent: fluency
Original:  These sets of features sets used in current tools for pre-miRNA recognition differ in construction and dimension.
Revised:  These feature sets used by current tools for pre-miRNA recognition differ in construction and dimension.
Edit intent: clarity
Original:  Some  sets are composed of sequence-structure patterns commonly occurring in pre-miRNAs  while others are a miscellany of more sophisticated RNA features.
Revised:  Some feature sets are composed of sequence-structure patterns commonly occurring in pre-miRNAs while others are a miscellany of more sophisticated RNA features.
Edit intent: fluency
Original:  Some  sets are composed of sequence-structure patterns commonly occurring in pre-miRNAs  while others are a miscellany of more sophisticated RNA features.
Revised:  Some sets are composed of sequence-structure patterns commonly found in pre-miRNAs while others are a miscellany of more sophisticated RNA features.
Edit intent: clarity
Original:  Some  sets are composed of sequence-structure patterns commonly occurring in pre-miRNAs  while others are a miscellany of more sophisticated RNA features.
Revised:  Some sets are composed of sequence-structure patterns commonly occurring in pre-miRNAs , while others are a miscellany of more sophisticated RNA features.
Edit intent: fluency
Original:  Some  sets are composed of sequence-structure patterns commonly occurring in pre-miRNAs  while others are a miscellany of more sophisticated RNA features.
Revised:  Some sets are composed of sequence-structure patterns commonly occurring in pre-miRNAs while others are a combination of more sophisticated RNA features.
Edit intent: clarity
Original:  Our analysis is based on  classification performance achieved with these feature sets in using the training algorithms used with those tools.
Revised:  The analysis is based on classification performance achieved with these feature sets in using the training algorithms used with those tools.
Edit intent: fluency
Original:  Our analysis is based on  classification performance achieved with these feature sets in using the training algorithms used with those tools.
Revised:  Our analysis is based on the classification performance achieved with these feature sets in using the training algorithms used with those tools.
Edit intent: fluency
Original:  Our analysis is based on  classification performance achieved with these feature sets in using the training algorithms used with those tools.
Revised:  Our analysis is based on classification performance achieved with these feature sets for the training algorithms used with those tools.
Edit intent: clarity
Original:  Our analysis is based on  classification performance achieved with these feature sets in using the training algorithms used with those tools.
Revised:  Our analysis is based on classification performance achieved with these feature sets in using the training algorithms used in these tools.
Edit intent: clarity
Original:  We show that more diverse feature sets produced classifiers with significantly higher classification performances than feature sets composed  of sequence-structure patterns.
Revised:  We show that more diverse feature sets produce classifiers with significantly higher classification performances than feature sets composed of sequence-structure patterns.
Edit intent: fluency
Original:  We show that more diverse feature sets produced classifiers with significantly higher classification performances than feature sets composed  of sequence-structure patterns.
Revised:  We show that more diverse feature sets produced classifiers with significantly higher classification performance compared to feature sets composed of sequence-structure patterns.
Edit intent: clarity
Original:  We show that more diverse feature sets produced classifiers with significantly higher classification performances than feature sets composed  of sequence-structure patterns.
Revised:  We show that more diverse feature sets produced classifiers with significantly higher classification performances than feature sets composed only of sequence-structure patterns.
Edit intent: clarity
Original:  Based on these results, we proposed a subset of features which produced sensitivity of 90\% and  specificity of 95\% , which are 0.1\% lower than the highest sensitivities and specificities produced by other feature sets.
Revised:  Based on these results, we proposed a subset of features which produced sensitivity of 90\% and a specificity of 95\% , which are 0.1\% lower than the highest sensitivities and specificities produced by other feature sets.
Edit intent: fluency
Original:  Based on these results, we proposed a subset of features which produced sensitivity of 90\% and  specificity of 95\% , which are 0.1\% lower than the highest sensitivities and specificities produced by other feature sets.
Revised:  Based on these results, we proposed a subset of features which produced sensitivity of 90\% and specificity of 95\% . Our feature set achieves a sensitivity and specificity within 0.1\% lower than the highest sensitivities and specificities produced by other feature sets.
Edit intent: clarity
Original:  We show that the method can be implemented without the need to perform full-dimensional vector operations, which is considered to be the major bottleneck of  accelerated coordinate descent  .
Revised:  We show that the method can be implemented without the need to perform full-dimensional vector operations, which is the major bottleneck of accelerated coordinate descent .
Edit intent: clarity
Original:  We show that the method can be implemented without the need to perform full-dimensional vector operations, which is considered to be the major bottleneck of  accelerated coordinate descent  .
Revised:  We show that the method can be implemented without the need to perform full-dimensional vector operations, which is considered to be the major bottleneck of accelerated coordinate descent methods .
Edit intent: clarity
Original: It is well known that direct training of deep multi-layer neural networks (DNNs) will generally lead to poor results.
Revised: It is well known that direct training of deep neural networks will generally lead to poor results.
Edit intent: clarity
Original:  A major progress in recent years is the invention of various unsupervised pretraining methods to initialize network parameters and it was shown that such methods lead to good prediction performance.
Revised:  A major progress in recent years is the invention of various pretraining methods to initialize network parameters and it was shown that such methods lead to good prediction performance.
Edit intent: clarity
Original:  However, the reason for the success of the pretraining has not been fully understood, although it was argued that regularization and better optimization play certain roles.
Revised:  However, the reason for the success of pretraining has not been fully understood, although it was argued that regularization and better optimization play certain roles.
Edit intent: fluency
Original:  This paper provides another explanation for the effectiveness of the pretraining, where we empirically show the pretraining leads to a higher level of sparseness of hidden unit activation in the resulting neural networks , and the higher sparseness is positively correlated to faster training speed and better prediction accuracy.
Revised:  This paper provides another explanation for the effectiveness of pretraining, where we empirically show the pretraining leads to a higher level of sparseness of hidden unit activation in the resulting neural networks , and the higher sparseness is positively correlated to faster training speed and better prediction accuracy.
Edit intent: fluency
Original:  This paper provides another explanation for the effectiveness of the pretraining, where we empirically show the pretraining leads to a higher level of sparseness of hidden unit activation in the resulting neural networks , and the higher sparseness is positively correlated to faster training speed and better prediction accuracy.
Revised:  This paper provides another explanation for the effectiveness of the pretraining, where we show pretraining leads to a higher level of sparseness of hidden unit activation in the resulting neural networks , and the higher sparseness is positively correlated to faster training speed and better prediction accuracy.
Edit intent: clarity
Original:  This paper provides another explanation for the effectiveness of the pretraining, where we empirically show the pretraining leads to a higher level of sparseness of hidden unit activation in the resulting neural networks , and the higher sparseness is positively correlated to faster training speed and better prediction accuracy.
Revised:  This paper provides another explanation for the effectiveness of the pretraining, where we empirically show the pretraining leads to a sparseness of hidden unit activation in the resulting neural networks , and the higher sparseness is positively correlated to faster training speed and better prediction accuracy.
Edit intent: clarity
Original: A graph G is 3-colorable if and only if it is homomorphic to the complete 3-vertex graph K_3.
Revised: A graph G is 3-colorable if and only if it maps homomorphically to the complete 3-vertex graph K_3.
Edit intent: clarity
Original:  We prove}\ne that W(n) =\Omega(\log n), improving on a lower bound W(n)=\Omega(\log\log n/\log\log\log n) that follows from the work of Ne\v{s and Zhu on bounded treewidth duality.
Revised:  We prove}\ne that W(n) =\Omega(\log n), improving on a lower bound W(n)=\Omega(\log\log n/\log\log\log n) that follows from the work of Nesetril and Zhu on bounded treewidth duality.
Edit intent: fluency
Original:   On the other hand, we make a first improvement\le \le \le{4}\,n+1, improving } on the trivial upper bound W(n)\le n by proving that W( n)%DIFDELCMD < \le%%% \frac{3{16}\, g(G) for every non-3-colorable graph G, where g(G) denotes the girth of G. Finally, we consider the function } W(n) remains a challenging problem: We should have W(n)= \Omega(n/\log n) if the Exponential Time Hypothesis is true .
Revised:  On the other hand, we observe that W(G)\le \le \le{4}\,n+1, improving } on the trivial upper bound W(n)\le n by proving that W( n)%DIFDELCMD < \le%%% \frac{3{16}\, g(G) for every non-3-colorable graph G, where g(G) denotes the girth of G. Finally, we consider the function } W(n) remains a challenging problem: We should have W(n)= \Omega(n/\log n) if the Exponential Time Hypothesis is true .
Edit intent: clarity
Original:   On the other hand, we make a first improvement\le \le \le{4}\,n+1, improving } on the trivial upper bound W(n)\le n by proving that W( n)%DIFDELCMD < \le%%% \frac{3{16}\, g(G) for every non-3-colorable graph G, where g(G) denotes the girth of G. Finally, we consider the function } W(n) remains a challenging problem: We should have W(n)= \Omega(n/\log n) if the Exponential Time Hypothesis is true .
Revised:  On the other hand, we make a first improvement\le \le \le\frac{3{4}\,n+1, improving } on the trivial upper bound W(n)\le n by proving that W( n)%DIFDELCMD < \le%%% \frac{3{16}\, g(G) for every non-3-colorable graph G, where g(G) denotes the girth of G. Finally, we consider the function } W(n) remains a challenging problem: We should have W(n)= \Omega(n/\log n) if the Exponential Time Hypothesis is true .
Edit intent: fluency
Original:   On the other hand, we make a first improvement\le \le \le{4}\,n+1, improving } on the trivial upper bound W(n)\le n by proving that W( n)%DIFDELCMD < \le%%% \frac{3{16}\, g(G) for every non-3-colorable graph G, where g(G) denotes the girth of G. Finally, we consider the function } W(n) remains a challenging problem: We should have W(n)= \Omega(n/\log n) if the Exponential Time Hypothesis is true .
Revised:  On the other hand, we make a first improvement\le \le \le{4}\,n+1, improving } on the trivial upper bound W(n)\le n . We also show that W( n)%DIFDELCMD < \le%%% \frac{3{16}\, g(G) for every non-3-colorable graph G, where g(G) denotes the girth of G. Finally, we consider the function } W(n) remains a challenging problem: We should have W(n)= \Omega(n/\log n) if the Exponential Time Hypothesis is true .
Edit intent: clarity
Original:   On the other hand, we make a first improvement\le \le \le{4}\,n+1, improving } on the trivial upper bound W(n)\le n by proving that W( n)%DIFDELCMD < \le%%% \frac{3{16}\, g(G) for every non-3-colorable graph G, where g(G) denotes the girth of G. Finally, we consider the function } W(n) remains a challenging problem: We should have W(n)= \Omega(n/\log n) if the Exponential Time Hypothesis is true .
Revised:  On the other hand, we make a first improvement\le \le \le{4}\,n+1, improving } on the trivial upper bound W(n)\le n by proving that W( %DIFDELCMD < \le%%% \frac{3{16}\, g(G) for every non-3-colorable graph G, where g(G) denotes the girth of G. Finally, we consider the function } W(n) remains a challenging problem: We should have W(n)= \Omega(n/\log n) if the Exponential Time Hypothesis is true .
Edit intent: fluency
Original:   On the other hand, we make a first improvement\le \le \le{4}\,n+1, improving } on the trivial upper bound W(n)\le n by proving that W( n)%DIFDELCMD < \le%%% \frac{3{16}\, g(G) for every non-3-colorable graph G, where g(G) denotes the girth of G. Finally, we consider the function } W(n) remains a challenging problem: We should have W(n)= \Omega(n/\log n) if the Exponential Time Hypothesis is true .
Revised:  On the other hand, we make a first improvement\le \le \le{4}\,n+1, improving } on the trivial upper bound W(n)\le n by proving that W( n)%DIFDELCMD < \le%%% G)>\frac{1{16}\, g(G) for every non-3-colorable graph G, where g(G) denotes the girth of G. Finally, we consider the function } W(n) remains a challenging problem: We should have W(n)= \Omega(n/\log n) if the Exponential Time Hypothesis is true .
Edit intent: fluency
Original:  We introduce many new variants of Sherali-Adams and Bienstock-Zuckerberg operators.
Revised:  We introduce many new variants of Sherali--Adams and Bienstock--Zuckerberg operators.
Edit intent: fluency
Original:  In particular, using the new techniques and a recent result of Mathieu and Sinclair  , we prove that the polyhedral Bienstock-Zuckerberg operator requires at least 2n- 3{2} iterations to compute the matching polytope of the (2n+1)-clique.
Revised:  In particular, using the new techniques and a result of Mathieu and Sinclair , we prove that the polyhedral Bienstock-Zuckerberg operator requires at least 2n- 3{2} iterations to compute the matching polytope of the (2n+1)-clique.
Edit intent: clarity
Original:  In particular, using the new techniques and a recent result of Mathieu and Sinclair  , we prove that the polyhedral Bienstock-Zuckerberg operator requires at least 2n- 3{2} iterations to compute the matching polytope of the (2n+1)-clique.
Revised:  In particular, using the new techniques and a recent result of Mathieu and Sinclair , we prove that the polyhedral Bienstock--Zuckerberg operator requires at least 2n- 3{2} iterations to compute the matching polytope of the (2n+1)-clique.
Edit intent: fluency
Original:  Lastly, we show that some of the worst-case instances for the positive semidefinite Lov\'{a lift-and-project operator are also bad instances for the strongest variants of the Sherali-Adams operator with positive semidefinite strengthenings, and discuss some consequences for integrality gaps of convex relaxations.
Revised:  Lastly, we show that some of the worst-case instances for the positive semidefinite Lov\'{a lift-and-project operator are also bad instances for the strongest variants of the Sherali--Adams operator with positive semidefinite strengthenings, and discuss some consequences for integrality gaps of convex relaxations.
Edit intent: fluency
Original:  As a side effect, these approximations yield significantly different variances for stochastic gradient estimates of individual samples .
Revised:  As a side effect, these approximations yield significantly different variances for stochastic gradient estimates of individual data points .
Edit intent: clarity
Original:   In this paper we  show empirically that CD has a lower stochastic gradient estimate variance than exact sampling, while the sum of subsequent PCD estimates has a higher variance than exact sampling.
Revised:  In this paper we however show empirically that CD has a lower stochastic gradient estimate variance than exact sampling, while the sum of subsequent PCD estimates has a higher variance than exact sampling.
Edit intent: coherence
Original:   In this paper we  show empirically that CD has a lower stochastic gradient estimate variance than exact sampling, while the sum of subsequent PCD estimates has a higher variance than exact sampling.
Revised:  In this paper we show empirically that CD has a lower stochastic gradient estimate variance than exact sampling, while the mean of subsequent PCD estimates has a higher variance than exact sampling.
Edit intent: clarity
Original:  Based on this observation, we propose two novel architectures of a deep RNN and provide an alternative interpretation of these deep RNN's using a novel framework based on neural operators.
Revised:  Based on this observation, we propose two novel architectures of a deep RNN and provide an alternative interpretation of these deep RNNs using a novel framework based on neural operators.
Edit intent: fluency
Original:  The proposed deep RNN's are empirically evaluated on the tasks of polyphonic music prediction and language modeling.
Revised:  The proposed deep RNNs are empirically evaluated on the tasks of polyphonic music prediction and language modeling.
Edit intent: fluency
Original:  The experimental result supports our claim that the proposed deep RNN's benefit from the depth and outperform the conventional, shallow RNN.
Revised:  The experimental result supports our claim that the proposed deep RNNs benefit from the depth and outperform the conventional, shallow RNN.
Edit intent: fluency
Original: In this paper, we propose a novel way to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of the depth in an RNN is not as clear as it is in feedforward neural networks.
Revised: In this paper, we propose a novel way to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of the depth in an RNN is not as clear as it is in feedforward neural networks.
Edit intent: fluency
Original: In this paper, we propose a novel way to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of the depth in an RNN is not as clear as it is in feedforward neural networks.
Revised: In this paper, we propose a novel way to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks.
Edit intent: fluency
Original:  By carefully analyzing and understanding the architecture of an RNN, we define three points which may be made deeper;
Revised:  By carefully analyzing and understanding the architecture of an RNN, however, we find three points of an RNN which may be made deeper;
Edit intent: coherence
Original:  This can be considered in addition to stacking multiple recurrent layers proposed earlier by Schmidhuber (1992).
Edit intent: coherence
Original:  The experimental result supports our claim that the proposed deep RNNs benefit from the depth and outperform the conventional, shallow RNN .
Revised:  The experimental result supports our claim that the proposed deep RNNs benefit from the depth and outperform the conventional, shallow RNNs .
Edit intent: fluency
Original: In this paper, we propose a novel way to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks.
Revised: In this paper, we explore different ways to extend a recurrent neural network (RNN) to a deep RNN. We start by arguing that the concept of depth in an RNN is not as clear as it is in feedforward neural networks.
Edit intent: clarity
Original: We introduce a model for capturing the effects of beam misdirection on coverage and throughput in a wireless directional network using stochastic geometry.
Revised: We introduce a model for capturing the effects of beam misdirection on coverage and throughput in a directional wireless network using stochastic geometry.
Edit intent: fluency