wanyu/IteraTeR_v2 huggingface dataset:
Original:  Based on the platform of realtime.springer.com, we have monitored and recorded the dynamic usage data of Scientometrics articles round the clock.
Revised:  Based on the platform of realtime.springer.com, we have been monitoring and recording the dynamic usage data of Scientometrics articles round the clock.
Edit intent: fluency
Original:  Our analysis find that papers published in recent 4 years have much more download than papers published before .
Revised:  Our analysis find that papers published in recent four years have many more downloads than papers published before .
Edit intent: fluency
Original:  Our analysis find that papers published in recent 4 years have much more download than papers published before .
Revised:  Our analysis find that papers published in recent 4 years have much more download than papers published four years ago .
Edit intent: clarity
Original:  Usually, classic papers are still being downloaded frequently even many years after their publication.
Revised:  Usually, classic papers are still being downloaded frequently even long after their publication.
Edit intent: clarity
Original:  Social media may reboot the attention of old scientific literature in a short time.
Revised:  Additionally, we find that social media may reboot the attention of old scientific literature in a short time.
Edit intent: coherence
Original: We explore the effect of an attractive interaction between parallel aligned polymers, which are perpendicularly grafted on a substrate.
Revised: We explore the effect of an attractive interaction between parallel-aligned polymers, which are perpendicularly grafted on a substrate.
Edit intent: fluency
Original:  Such an attractive interaction could  , e.g., be due to reversible cross-links.
Revised:  Such an attractive interaction could be due to , e.g., be due to reversible cross-links.
Edit intent: fluency
Original:  Such an attractive interaction could  , e.g., be due to reversible cross-links.
Revised:  Such an attractive interaction could , e.g., reversible cross-links.
Edit intent: clarity
Original:  We analyse the instability for two models of aligned polymers: directed polymers with a line tension and weakly bending chains with a bending stiffness.
Revised:  We analyze the instability for two models of aligned polymers: directed polymers with a line tension and weakly bending chains with a bending stiffness.
Edit intent: fluency
Original:  However, structural similarity strategy is not efficient enough in many real-world applications because the data are often noisy and the combinatorial nature of graphs makes it computationally very costly.
Revised:  However, current structural similarity strategies are not efficient enough in many real-world applications because the data are often noisy and the combinatorial nature of graphs makes it computationally very costly.
Edit intent: clarity
Original:  However, structural similarity strategy is not efficient enough in many real-world applications because the data are often noisy and the combinatorial nature of graphs makes it computationally very costly.
Revised:  However, structural similarity strategy is not efficient enough in many real-world applications , besides, the combinatorial nature of graphs makes it computationally very costly.
Edit intent: coherence
Original: Holographic algorithms were first introduced by Valiant as a new methodology to derive polynomial time algorithms.
Revised: Holographic algorithms introduced by Valiant as a new methodology to derive polynomial time algorithms.
Edit intent: coherence
Original:  Instead of matchgates , the two underlying families of constraint functions are of the affine type and of the product type .
Revised:  Instead of matchgates , the two underlying families of constraint functions are of the affine type and the product type .
Edit intent: fluency
Original:  We present a polynomial time algorithm to  decide if a given counting problem has a holographic algorithm using the affine or product type constraint functions.
Revised:  We present a polynomial time algorithm to decide if a given counting problem has a holographic algorithm using the affine or product-type functions.
Edit intent: clarity
Original:  Our algorithm also finds a holographic transformation when one exists.
Revised:  Our algorithms also find a holographic transformation when one exists.
Edit intent: clarity
Original:  When the constraint functions are symmetric, we further present a polynomial time algorithm for the same decision and search problems  , where the complexity is measured in terms of the (exponentially more) succinct presentation of symmetric constraint functions .
Revised:  When the constraint functions are symmetric, we further present a polynomial time algorithm for the same decision and search problems for symmetric functions , where the complexity is measured in terms of the (exponentially more) succinct presentation of symmetric constraint functions .
Edit intent: coherence
Original:  When the constraint functions are symmetric, we further present a polynomial time algorithm for the same decision and search problems  , where the complexity is measured in terms of the (exponentially more) succinct presentation of symmetric constraint functions .
Revised:  When the constraint functions are symmetric, we further present a polynomial time algorithm for the same decision and search problems , where the complexity is measured in terms of the (exponentially more) succinct representations .
Edit intent: clarity
Original:  Our proof techniques are mainly algebraic, e.g.,  stabilizers and orbits of group actions.
Revised:  Our proof techniques are mainly algebraic, e.g., using stabilizers and orbits of group actions.
Edit intent: coherence
Original: To date, most algorithms aiming to find community structures in networks mainly focus on unipartite or bipartite networks.
Revised: To date, most algorithms aiming to find community structures focus on unipartite or bipartite networks.
Edit intent: clarity
Original:  Interestingly, unipartite and bipartite networks can be viewed as limiting cases of a mixture network , suggesting that the mixture network can be considered as a general condition . Based on this observation, we  propose a probabilistic model  based on the link community model for a unipartite, undirected network [B. Ball, B. Karrer, and M. E.  Newman, Phys.
Revised:  Interestingly, unipartite and bipartite networks can be viewed as limiting cases of a mixture network , suggesting that the mixture network can be considered as a general condition . Based on this observation, we first propose a probabilistic model based on the link community model for a unipartite, undirected network [B. Ball, B. Karrer, and M. E. Newman, Phys.
Edit intent: coherence
Original:  Interestingly, unipartite and bipartite networks can be viewed as limiting cases of a mixture network , suggesting that the mixture network can be considered as a general condition . Based on this observation, we  propose a probabilistic model  based on the link community model for a unipartite, undirected network [B. Ball, B. Karrer, and M. E.  Newman, Phys.
Revised:  Interestingly, unipartite and bipartite networks can be viewed as limiting cases of a mixture network , suggesting that the mixture network can be considered as a general condition . Based on this observation, we propose a probabilistic model based on the link community model for a unipartite, undirected network [B. Ball, B. Karrer, and M. E. J. Newman, Phys.
Edit intent: clarity
Original:  We test our algorithm on synthetic networks (both overlapping and non-overlapping community ) and apply it to two real-world networks  .
Revised:  We test our algorithm on synthetic networks (both overlapping and non-overlapping communities ) and apply it to two real-world networks .
Edit intent: fluency
Original:  The results suggest that our model performs well on all three types of networks, and is competitive with other algorithms for unipartite and bipartite networks .
Revised:  The results suggest that our model performs well on all three types of networks, is competitive with other algorithms for unipartite and bipartite networks .
Edit intent: coherence
Original:  As is known, a unipartite network consists of one set of nodes, and a bipartite network consists of two sets of nonoverlapping nodes with links only join nodes in different sets.
Revised:  As is known, a unipartite network consists of one set of nodes, and a bipartite network consists of two non-overlapping sets of nodes with links only join nodes in different sets.
Edit intent: fluency
Original:  As is known, a unipartite network consists of one set of nodes, and a bipartite network consists of two sets of nonoverlapping nodes with links only join nodes in different sets.
Revised:  As is known, a unipartite network consists of one set of nodes, and a bipartite network consists of two sets of nonoverlapping nodes with links only joining nodes in different sets.
Edit intent: fluency
Original:  Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network.
Revised:  Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the non-overlapping restriction of a bipartite network.
Edit intent: fluency
Original: Finding community structures in networks is of importance in network science, technology  and applications.
Revised: Finding community structures in networks is important in network science, technology and applications.
Edit intent: clarity
Original: Finding community structures in networks is of importance in network science, technology  and applications.
Revised: Finding community structures in networks is of importance in network science, technology , and applications.
Edit intent: fluency
Original:  To date, most algorithms aiming to find community structures focus on unipartite or bipartite networks.
Revised:  To date, most algorithms that aim to find community structures focus on unipartite or bipartite networks.
Edit intent: clarity
Original:  To date, most algorithms aiming to find community structures focus on unipartite or bipartite networks.
Revised:  To date, most algorithms aiming to find community structures only focus either on unipartite or bipartite networks.
Edit intent: clarity
Original:  As is known, a unipartite network consists of one set of nodes , and a bipartite network consists of two non-overlapping sets of nodes with links only joining nodes in different sets.
Revised:  A unipartite network consists of one set of nodes , and a bipartite network consists of two non-overlapping sets of nodes with links only joining nodes in different sets.
Edit intent: coherence
Original:  As is known, a unipartite network consists of one set of nodes , and a bipartite network consists of two non-overlapping sets of nodes with links only joining nodes in different sets.
Revised:  As is known, a unipartite network consists of one set of nodes and a bipartite network consists of two non-overlapping sets of nodes with links only joining nodes in different sets.
Edit intent: fluency
Original:  As is known, a unipartite network consists of one set of nodes , and a bipartite network consists of two non-overlapping sets of nodes with links only joining nodes in different sets.
Revised:  As is known, a unipartite network consists of one set of nodes , and a bipartite network consists of two nonoverlapping sets of nodes with links only joining nodes in different sets.
Edit intent: fluency
Original:  As is known, a unipartite network consists of one set of nodes , and a bipartite network consists of two non-overlapping sets of nodes with links only joining nodes in different sets.
Revised:  As is known, a unipartite network consists of one set of nodes , and a bipartite network consists of two non-overlapping sets of nodes with only links joining the nodes in different sets.
Edit intent: fluency
Original:  However, a third type  exists, defined  as the mixture network in our paper . Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the non-overlapping restriction of a bipartite network.
Revised:  However, a third type of network exists, defined as the mixture network in our paper . Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the non-overlapping restriction of a bipartite network.
Edit intent: fluency
Original:  However, a third type  exists, defined  as the mixture network in our paper . Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the non-overlapping restriction of a bipartite network.
Revised:  However, a third type exists, defined here as the mixture network in our paper . Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the non-overlapping restriction of a bipartite network.
Edit intent: coherence
Original:  However, a third type  exists, defined  as the mixture network in our paper . Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the non-overlapping restriction of a bipartite network.
Revised:  However, a third type exists, defined as the mixture network . Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the non-overlapping restriction of a bipartite network.
Edit intent: clarity
Original:  However, a third type  exists, defined  as the mixture network in our paper . Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the non-overlapping restriction of a bipartite network.
Revised:  However, a third type exists, defined as the mixture network in our paper . Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network.
Edit intent: fluency
Original:  A mixture network can represent not only all the unipartite and bipartite networks  but also a wide range of real-world networks that cannot be properly represented as  unipartite or bipartite networks in fields including biology and social science.
Revised:  A mixture network can represent not only all the unipartite and bipartite networks , but also a wide range of real-world networks that cannot be properly represented as unipartite or bipartite networks in fields including biology and social science.
Edit intent: fluency
Original:  A mixture network can represent not only all the unipartite and bipartite networks  but also a wide range of real-world networks that cannot be properly represented as  unipartite or bipartite networks in fields including biology and social science.
Revised:  A mixture network can represent not only all the unipartite and bipartite networks but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields including biology and social science.
Edit intent: coherence
Original:  A mixture network can represent not only all the unipartite and bipartite networks  but also a wide range of real-world networks that cannot be properly represented as  unipartite or bipartite networks in fields including biology and social science.
Revised:  A mixture network can represent not only all the unipartite and bipartite networks but also a wide range of real-world networks that cannot be properly represented as unipartite or bipartite networks in fields such as biology and social science.
Edit intent: clarity
Original:  Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite , undirected network [B . Ball , B. Karrer, and M. E. J. Newman, Phys.
Revised:  Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B . Ball , B. Karrer, and M. E. J. Newman, Phys.
Edit intent: fluency
Original:  Rev. E 84 , 036103 (2011 )].
Revised:  Rev. E 84 036103 (2011 )].
Edit intent: fluency
Original:  Rev. E 84 , 036103 (2011 )].
Revised:  Rev. E 84 , 036103 )].
Edit intent: coherence
Original:  We test our algorithm on synthetic networks (both overlapping and non-overlapping communities) and apply it to two real-world networks , southern women bipartite network and a human transcriptional regulatory mixture network.
Revised:  We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks , southern women bipartite network and a human transcriptional regulatory mixture network.
Edit intent: fluency
Original:  We test our algorithm on synthetic networks (both overlapping and non-overlapping communities) and apply it to two real-world networks , southern women bipartite network and a human transcriptional regulatory mixture network.
Revised:  We test our algorithm on synthetic networks (both overlapping and non-overlapping communities) and apply it to two real-world networks : a southern women bipartite network and a human transcriptional regulatory mixture network.
Edit intent: fluency
Original:  The results suggest that our model performs well on all three types of networks, is competitive with other algorithms for unipartite and bipartite networks, and is applicable to real-world networks.
Revised:  The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite and bipartite networks, and is applicable to real-world networks.
Edit intent: fluency
Original:  The results suggest that our model performs well on all three types of networks, is competitive with other algorithms for unipartite and bipartite networks, and is applicable to real-world networks.
Revised:  The results suggest that our model performs well on all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.
Edit intent: fluency
Original:  We numerically and analytically investigate critical dynamics in a network model and discover a previously unappreciated consequence of inhibition: the greater the number of inhibitory nodes , the more likely network dynamics are to be intrinsically self-sustaining.
Revised:  We numerically and analytically investigate critical dynamics in a network model and discover a previously unappreciated consequence of inhibition: the greater the number of inhibitory nodes is to decrease activity, the collective dynamics becomes self-sustaining.
Edit intent: clarity
Original: Low intensity ultrasonic waves can remotely and non-destructively excite central nervous system (CNS) neurons.
Revised: Low-intensity ultrasonic waves can remotely and non-destructively excite central nervous system (CNS) neurons.
Edit intent: fluency
Original: Low intensity ultrasonic waves can remotely and non-destructively excite central nervous system (CNS) neurons.
Revised: Low intensity ultrasonic waves can remotely and nondestructively excite central nervous system (CNS) neurons.
Edit intent: fluency
Original:  Here , we show that  in CNS neurons, ultrasound-induced cavitation of these nanometric bilayer sonophores can induce a complex mechano-electrical interplay leading to excitation, primarily through the effect of currents induced by membrane capacitance changes.
Revised:  Here , we show that , in CNS neurons, ultrasound-induced cavitation of these nanometric bilayer sonophores can induce a complex mechano-electrical interplay leading to excitation, primarily through the effect of currents induced by membrane capacitance changes.
Edit intent: fluency
Original:  Here , we show that  in CNS neurons, ultrasound-induced cavitation of these nanometric bilayer sonophores can induce a complex mechano-electrical interplay leading to excitation, primarily through the effect of currents induced by membrane capacitance changes.
Revised:  Here , we show that in CNS neurons, ultrasound-induced cavitation of these nanometric bilayer sonophores can induce a complex mechanoelectrical interplay leading to excitation, primarily through the effect of currents induced by membrane capacitance changes.
Edit intent: clarity
Original:  Our model explains the basic features of CNS acousto-stimulation and predicts how the experimentally observed efficacy of mouse motor cortical ultrasonic stimulation depends on stimulation parameters.
Revised:  Our model explains the basic features of CNS acoustostimulation and predicts how the experimentally observed efficacy of mouse motor cortical ultrasonic stimulation depends on stimulation parameters.
Edit intent: fluency
Original: Adaptation in response to selection on polygenic phenotypes occurs via subtle allele frequencies shifts at many loci.
Revised: Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci.
Edit intent: clarity
Original:  Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that have undergone local adaptation.
Revised:  Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation.
Edit intent: clarity
Original:  Using GWAS data, we estimate the mean additive genetic value for a give phenotype across many populations as simple weighted sums of allele frequencies.
Revised:  Using GWAS data, we estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies.
Edit intent: fluency
Original:  We model the expected differentiation of GWAS loci among populations under neutrality to develop simple tests of selection across an arbitrary number of populations with arbitrary population structure.
Revised:  We first describe a general model of neutral genetic value drift for an arbitrary number of populations with arbitrary population structure.
Edit intent: clarity
Original:  This test is a natural generalization of QST /FST comparisons based on GWAS predictions.
Edit intent: coherence
Original: We show that geometric inference of a point cloud can be calculated by examining a kernel density estimate of the point could .
Revised: We show that geometric inference of a point cloud can be calculated by examining its kernel density estimate of the point could .
Edit intent: clarity
Original: We show that geometric inference of a point cloud can be calculated by examining a kernel density estimate of the point could .
Revised: We show that geometric inference of a point cloud can be calculated by examining a kernel density estimate .
Edit intent: fluency
Original:  To achieve this result  we study how to replace distance to a measure, as studied by Chazal, Cohen-Steiner, and Merigot, with the kernel distance to a measure .
Revised:  To achieve this result , we study how to replace distance to a measure, as studied by Chazal, Cohen-Steiner, and Merigot, with the kernel distance to a measure .
Edit intent: fluency
Original:  To achieve this result  we study how to replace distance to a measure, as studied by Chazal, Cohen-Steiner, and Merigot, with the kernel distance to a measure .
Revised:  To achieve this result we study how to replace distance to a measure, as studied by Chazal, Cohen-Steiner, and Merigot, with the kernel distance .
Edit intent: fluency