wanyu/IteraTeR_v2 huggingface dataset:
Original:  This neural network allows us to model very complex relationship between sequence and contact map as well as long-range interdependency between contacts and thus, obtain high-quality contact prediction .
Revised:  This neural network allows us to model very complex relationship between sequence and contact map as well as long-range interdependency between contacts .
Edit intent: coherence
Original:  Further, our contact-assisted models also have much better quality (especially for membrane proteins ) than template-based models .
Revised:  Further, our contact-assisted models have much better quality (especially for membrane proteins ) than template-based models .
Edit intent: clarity
Original:  Recently exciting progress has been made  , but the predicted contacts for proteins without many sequence homologs is still of low quality and not extremely useful for de novo structure prediction.
Revised:  Recently exciting progress has been made on protein contact prediction , but the predicted contacts for proteins without many sequence homologs is still of low quality and not extremely useful for de novo structure prediction.
Edit intent: clarity
Original:  Recently exciting progress has been made  , but the predicted contacts for proteins without many sequence homologs is still of low quality and not extremely useful for de novo structure prediction.
Revised:  Recently exciting progress has been made , but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction.
Edit intent: clarity
Original:  This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks.
Revised:  This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual networks.
Edit intent: fluency
Original:  This deep neural network allows us to model very complex relationship between sequence and contact map as well as long-range interdependency between contacts .
Revised:  This deep neural network allows us to model very complex sequence-contact relationship as well as long-range interdependency between contacts .
Edit intent: clarity
Original:  This deep neural network allows us to model very complex relationship between sequence and contact map as well as long-range interdependency between contacts .
Revised:  This deep neural network allows us to model very complex relationship between sequence and contact map as well as long-range inter-contact correlation .
Edit intent: clarity
Original:  One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method still works well on membrane protein prediction  .
Revised:  One interesting finding is that even if we do not train our prediction models with any membrane proteins, our method works very well on membrane protein prediction .
Edit intent: clarity
Original: Let S be a set of n points in general poisition in the plane.
Revised: Let S be a set of n points in general position in the plane.
Edit intent: fluency
Original:  In this paper we consider the problem  computing cr(S'), where S' comes from adding, removing or moving a point from S.
Revised:  In this paper we consider the problem of computing cr(S'), where S' comes from adding, removing or moving a point from S.
Edit intent: fluency
Original:  Specifically, we propose a novel induction principle on a well-founded relation of separation logic model , and follow the %DIFDELCMD < {\em %%% explicit induction methods to implement this principle as inference rules, so that it can be easily integrated into a deductive system.
Revised:  Specifically, we propose a novel induction principle on a well-founded relation of separation logic model and follow the %DIFDELCMD < {\em %%% explicit induction methods to implement this principle as inference rules, so that it can be easily integrated into a deductive system.
Edit intent: fluency
Original:  Specifically, we propose a novel induction principle on a well-founded relation of separation logic model , and follow the %DIFDELCMD < {\em %%% explicit induction methods to implement this principle as inference rules, so that it can be easily integrated into a deductive system.
Revised:  Specifically, we propose a novel induction principle on a well-founded relation of separation logic model , and follow the %DIFDELCMD < {\em %%% explicit induction methods to implement this principle as inference rules, so that it can be easily integrated into a deductive system.
Edit intent: fluency
Original:  We also support %DIFDELCMD < {\em %%% mutual induction, a natural feature of implicit induction, where the goal entailment and other entailments derived during the proof search can be used as hypotheses to prove each other.
Revised:  We also support %DIFDELCMD < {\em %%% mutual induction, a natural feature of implicit induction, where the goal entailment and other entailments derived during the proof search can be used as hypotheses to prove each other.
Edit intent: fluency
Original:  We have implemented a prototype prover and evaluated it on benchmarks of handcrafted entailments as well as entailments from a separation logic competition.
Revised:  We have implemented a prototype prover and evaluated it on a benchmark of handcrafted entailments as well as entailments from a separation logic competition.
Edit intent: fluency
Original:  We have implemented a prototype prover and evaluated it on benchmarks of handcrafted entailments as well as entailments from a separation logic competition.
Revised:  We have implemented a prototype prover and evaluated it on benchmarks of handcrafted entailments as well as benchmarks from a separation logic competition.
Edit intent: fluency
Original:  With the final objective of accelerating iterative reconstruction techniques, a non-oversampling Joseph-like raytracing projection algorithm for three dimensions featuring both a branchless sampling loop and a cache friendly memory access pattern is presented.
Revised:  With the final objective of accelerating iterative reconstruction techniques, a Joseph-type interpolating ray-casting algorithm for the computation of X-ray projections is presented.
Edit intent: clarity
Original:  Both memory access rates and total run time are benchmarked on a current consumer grade graphics processing unit and explicitly compared to the performance of a classic Digital Differential Analyzer (DDA) algorithm.
Revised:  Both memory access rates and total run time are benchmarked on a current consumer grade graphics processing unit and explicitly compared to the competing and widely adopted digital differential analyzer (DDA) algorithm.
Edit intent: clarity
Original: Today, huge amounts of data are being collected with spatial and temporal components from sources such as metrological , satellite imagery etc.
Revised: Today, huge amounts of data are being collected with spatial and temporal components from sources such as meteorological , satellite imagery etc.
Edit intent: fluency
Original:  Data Mining has emerged as the technology to discover hidden knowledge from very large size of data.
Revised:  Data Mining has emerged as the technology to discover hidden knowledge in very large amounts of data.
Edit intent: clarity
Original:  We provide evidence that neither class of assumptions alone is sufficient: For any member of a class of "nice" simple target functions, there are difficult input distributions , and on the other hand, for any member of a general class of "nice" input distributions, there are simple target functions  which are difficult to learn .
Revised:  We provide evidence that neither class of assumptions alone is sufficient: On the one hand, for any member of a class of "nice" simple target functions, there are difficult input distributions , and on the other hand, for any member of a general class of "nice" input distributions, there are simple target functions which are difficult to learn .
Edit intent: fluency
Original:  We provide evidence that neither class of assumptions alone is sufficient: For any member of a class of "nice" simple target functions, there are difficult input distributions , and on the other hand, for any member of a general class of "nice" input distributions, there are simple target functions  which are difficult to learn .
Revised:  We provide evidence that neither class of assumptions alone is sufficient: For any member of a class of "nice" target functions, there are difficult input distributions , and on the other hand, for any member of a general class of "nice" input distributions, there are simple target functions which are difficult to learn .
Edit intent: coherence
Original:  We provide evidence that neither class of assumptions alone is sufficient: For any member of a class of "nice" simple target functions, there are difficult input distributions , and on the other hand, for any member of a general class of "nice" input distributions, there are simple target functions  which are difficult to learn .
Revised:  We provide evidence that neither class of assumptions alone is sufficient: For any member of a class of "nice" simple target functions, there are difficult input distributions . On the other hand, for any member of a general class of "nice" input distributions, there are simple target functions which are difficult to learn .
Edit intent: coherence
Original:  We provide evidence that neither class of assumptions alone is sufficient: For any member of a class of "nice" simple target functions, there are difficult input distributions , and on the other hand, for any member of a general class of "nice" input distributions, there are simple target functions  which are difficult to learn .
Revised:  We provide evidence that neither class of assumptions alone is sufficient: For any member of a class of "nice" simple target functions, there are difficult input distributions , and on the other hand, for any member of a general class of "nice" input distributions, there are simple target functions , which are difficult to learn .
Edit intent: fluency
Original:  To prove our results, we develop some tools which may be of independent interest, such as extension of Fourier-based techniques for proving hardness in the statistical queries framework blum1994weakly, from the Boolean cube to Euclidean space  .
Revised:  To prove our results, we develop some tools which may be of independent interest, such as extending Fourier-based techniques for proving hardness in the statistical queries framework blum1994weakly, from the Boolean cube to Euclidean space .
Edit intent: clarity
Original:  To prove our results, we develop some tools which may be of independent interest, such as extension of Fourier-based techniques for proving hardness in the statistical queries framework blum1994weakly, from the Boolean cube to Euclidean space  .
Revised:  To prove our results, we develop some tools which may be of independent interest, such as extension of Fourier-based hardness techniques developed in the context of statistical queries blum1994weakly, from the Boolean cube to Euclidean space .
Edit intent: clarity
Original: The field of cryo-electron microscopy has made astounding advancements in the past few years, mainly due to improvements in the hardware of the microscopes .
Revised: The field of cryo-electron microscopy has made astounding advancements in the past few years, mainly due to advancements in electron detectors' technology .
Edit intent: clarity
Original:  We prove accuracy and stability bounds on the algorithm, and demonstrate it on simulated as well as experimental datasets .
Revised:  We demonstrate it on simulated as well as experimental datasets .
Edit intent: clarity
Original:  A PCA model \cal U \cal  is a (c, \ell , d, d_s )-CA model when C has circumference c, all the arcs in \cal A have length \ell,  all the extremes of the arcs in \cal A are at a distance at least d, and all the beginning points of the arcs in%DIFDELCMD < \cal %%% A are at a distance at least d + d_s.
Revised:  A PCA model \cal U \cal A) is a (c, \ell , d, d_s )-CA model when C has circumference c, all the arcs in \cal A have length \ell, all the extremes of the arcs in \cal A are at a distance at least d, and all the beginning points of the arcs in%DIFDELCMD < \cal %%% A are at a distance at least d + d_s.
Edit intent: fluency
Original:  A PCA model \cal U \cal  is a (c, \ell , d, d_s )-CA model when C has circumference c, all the arcs in \cal A have length \ell,  all the extremes of the arcs in \cal A are at a distance at least d, and all the beginning points of the arcs in%DIFDELCMD < \cal %%% A are at a distance at least d + d_s.
Revised:  A PCA model \cal U \cal is a (c, \ell )-CA model when C has circumference c, all the arcs in \cal A have length \ell, all the extremes of the arcs in \cal A are at a distance at least d, and all the beginning points of the arcs in%DIFDELCMD < \cal %%% A are at a distance at least d + d_s.
Edit intent: coherence
Original:  A PCA model \cal U \cal  is a (c, \ell , d, d_s )-CA model when C has circumference c, all the arcs in \cal A have length \ell,  all the extremes of the arcs in \cal A are at a distance at least d, and all the beginning points of the arcs in%DIFDELCMD < \cal %%% A are at a distance at least d + d_s.
Revised:  A PCA model \cal U \cal is a (c, \ell , d, d_s )-CA model when C has circumference c, all the arcs in \cal A have length \ell, and all the extremes of the arcs in \cal A are at a distance at least d, and all the beginning points of the arcs in%DIFDELCMD < \cal %%% A are at a distance at least d + d_s.
Edit intent: fluency
Original:  A PCA model \cal U \cal  is a (c, \ell , d, d_s )-CA model when C has circumference c, all the arcs in \cal A have length \ell,  all the extremes of the arcs in \cal A are at a distance at least d, and all the beginning points of the arcs in%DIFDELCMD < \cal %%% A are at a distance at least d + d_s.
Revised:  A PCA model \cal U \cal is a (c, \ell , d, d_s )-CA model when C has circumference c, all the arcs in \cal A have length \ell, all the extremes of the arcs in \cal A are at a distance at least %DIFDELCMD < \cal %%% A are at a distance at least d + d_s.
Edit intent: clarity
Original:  If c \leq c' and \ell \leq \ell' for every (c', \ell' , d, d_s )-CA model \cal  , then \cal U is (d, d_s)-minimal .
Revised:  If c \leq c' and \ell \leq \ell' for every (c', \ell' )-CA model \cal , then \cal U is (d, d_s)-minimal .
Edit intent: fluency
Original:  If c \leq c' and \ell \leq \ell' for every (c', \ell' , d, d_s )-CA model \cal  , then \cal U is (d, d_s)-minimal .
Revised:  If c \leq c' and \ell \leq \ell' for every (c', \ell' , d, d_s )-CA model \cal U , then \cal U is (d, d_s)-minimal .
Edit intent: fluency
Original:  In this article we prove that c and \ell are integer combinations of d and d_s when \cal U is (d, d_s)-minimal .
Revised:  In this article we prove that c and \ell are integer when \cal U is (d, d_s)-minimal .
Edit intent: clarity
Original:  In this article we prove that c and \ell are integer combinations of d and d_s when \cal U is (d, d_s)-minimal .
Revised:  In this article we prove that c and \ell are integer combinations of d and d_s when \cal U is minimal .
Edit intent: coherence
Original:  As a consequence, we obtain an algorithm to compute a (d, d_s)-minimal model equivalent to an input PCA model%DIFDELCMD < \cal %%% M that runs in O(n^3) time and O(n^2) space  .
Revised:  As a consequence, we obtain an %DIFDELCMD < \cal %%% M that runs in O(n^3) time and O(n^2) space .
Edit intent: coherence
Original:  As a consequence, we obtain an algorithm to compute a (d, d_s)-minimal model equivalent to an input PCA model%DIFDELCMD < \cal %%% M that runs in O(n^3) time and O(n^2) space  .
Revised:  As a consequence, we obtain an algorithm to compute a (d, d_s)-minimal model equivalent to an input PCA model%DIFDELCMD < \cal %%% O(n^3) time and O(n^2) space .
Edit intent: clarity
Original:  A particular promising paradigm for achieving such deep neural networks is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over successive generations that retain high modeling capabilities .
Revised:  A particular promising paradigm for achieving this is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over successive generations that retain high modeling capabilities .
Edit intent: clarity
Original:  A particular promising paradigm for achieving such deep neural networks is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over successive generations that retain high modeling capabilities .
Revised:  A particular promising paradigm for achieving such deep neural networks is the concept of evolutionary deep intelligence, which attempts to mimic biological evolution processes to synthesize highly-efficient deep neural networks over successive generations .
Edit intent: clarity
Original:  An important aspect of evolutionary deep intelligence that is particular interesting and worth deeper investigation is the genetic encoding scheme used to mimic heredity, which can have a significant impact on the way architectural traits are passed down from generation to generation and thus impact the quality of descendant deep neural networks.
Revised:  An important aspect of evolutionary deep intelligence is the genetic encoding scheme used to mimic heredity, which can have a significant impact on the way architectural traits are passed down from generation to generation and thus impact the quality of descendant deep neural networks.
Edit intent: clarity
Original:  An important aspect of evolutionary deep intelligence that is particular interesting and worth deeper investigation is the genetic encoding scheme used to mimic heredity, which can have a significant impact on the way architectural traits are passed down from generation to generation and thus impact the quality of descendant deep neural networks.
Revised:  An important aspect of evolutionary deep intelligence that is particular interesting and worth deeper investigation is the genetic encoding scheme used to mimic heredity, which can have a significant impact on the quality of offspring deep neural networks.
Edit intent: coherence
Original:  Motivated by the neurobiological phenomenon of synaptic clustering, where the probability of synaptic co-activation increases for correlated synapses encoding similar information that are close together on the same dendrite, we introduce a new genetic encoding scheme where synaptic probability within a deep neural network is driven towards the formation of highly sparse synaptic clusters.
Revised:  Motivated by the neurobiological phenomenon of synaptic clustering, we introduce a new genetic encoding scheme where synaptic probability within a deep neural network is driven towards the formation of highly sparse synaptic clusters.
Edit intent: clarity
Original:  Motivated by the neurobiological phenomenon of synaptic clustering, where the probability of synaptic co-activation increases for correlated synapses encoding similar information that are close together on the same dendrite, we introduce a new genetic encoding scheme where synaptic probability within a deep neural network is driven towards the formation of highly sparse synaptic clusters.
Revised:  Motivated by the neurobiological phenomenon of synaptic clustering, where the probability of synaptic co-activation increases for correlated synapses encoding similar information that are close together on the same dendrite, we introduce a new genetic encoding scheme where synaptic probability is driven towards the formation of highly sparse synaptic clusters.
Edit intent: clarity
Original:  Motivated by the neurobiological phenomenon of synaptic clustering, where the probability of synaptic co-activation increases for correlated synapses encoding similar information that are close together on the same dendrite, we introduce a new genetic encoding scheme where synaptic probability within a deep neural network is driven towards the formation of highly sparse synaptic clusters.
Revised:  Motivated by the neurobiological phenomenon of synaptic clustering, where the probability of synaptic co-activation increases for correlated synapses encoding similar information that are close together on the same dendrite, we introduce a new genetic encoding scheme where synaptic probability within a deep neural network is driven towards the formation of a highly sparse set of synaptic clusters.
Edit intent: clarity
Original:  Experimental results for the task of image classification demonstrated that the synthesized `evolved' offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses at a comparable accuracy for MNIST) compared to the original ancestor network, but also highly tailored for GPU-accelerated machine learning applications.
Revised:  Experimental results for the task of image classification demonstrated that the synthesized offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses at a comparable accuracy for MNIST) compared to the original ancestor network, but also highly tailored for GPU-accelerated machine learning applications.
Edit intent: clarity
Original:  Experimental results for the task of image classification demonstrated that the synthesized `evolved' offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses at a comparable accuracy for MNIST) compared to the original ancestor network, but also highly tailored for GPU-accelerated machine learning applications.
Revised:  Experimental results for the task of image classification demonstrated that the synthesized `evolved' offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses for MNIST) compared to the original ancestor network, but also highly tailored for GPU-accelerated machine learning applications.
Edit intent: clarity
Original:  Experimental results for the task of image classification demonstrated that the synthesized `evolved' offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses at a comparable accuracy for MNIST) compared to the original ancestor network, but also highly tailored for GPU-accelerated machine learning applications.
Revised:  Experimental results for the task of image classification demonstrated that the synthesized `evolved' offspring networks using this synaptic cluster-driven genetic encoding scheme can achieve state-of-the-art performance while having network architectures that are not only significantly more efficient (with a ~125-fold decrease in synapses at a comparable accuracy for MNIST) compared to the original ancestor network, but also tailored for GPU-accelerated machine learning applications.
Edit intent: clarity