wanyu/IteraTeR_v2 huggingface dataset:
Original: Ensemble recordings from the humanand monkeyneocortex were used to categorize units into regular-spiking (RS) and fast-spiking (FS) cells based on spike-waveform features and functional interactions.
Revised: Neocortical ensemble recordings (human/monkey) were used to categorize units into regular-spiking (RS) and fast-spiking (FS) cells based on spike-waveform features and functional interactions.
Edit intent: clarity
Original: Ensemble recordings from the humanand monkeyneocortex were used to categorize units into regular-spiking (RS) and fast-spiking (FS) cells based on spike-waveform features and functional interactions.
Revised: Ensemble recordings from the humanand monkeyneocortex were used to categorize units into regular-spiking and fast-spiking (FS) cells based on spike-waveform features and functional interactions.
Edit intent: clarity
Original: Ensemble recordings from the humanand monkeyneocortex were used to categorize units into regular-spiking (RS) and fast-spiking (FS) cells based on spike-waveform features and functional interactions.
Revised: Ensemble recordings from the humanand monkeyneocortex were used to categorize units into regular-spiking (RS) and fast-spiking cells based on spike-waveform features and functional interactions.
Edit intent: fluency
Original:  We adapted renormalization to evaluate the static/dynamic aspects of balance of these two interacting cell groups.
Revised:  We adapted renormalization to evaluate the static/dynamic balance aspects between these cell groups.
Edit intent: clarity
Original:  We found that the ensemble magnitude distribution is similar for both groups at multiple scales, demonstrating an overall balanced activity .
Revised:  We found that the ensemble magnitude distribution is similar for both groups at multiple scales, demonstrating an overall balance .
Edit intent: clarity
Original:  Scaling the ensemble series to a matrix of Excitation-Inhibition (E-I) ensemble pairs, enabled us to quantify the detailed dynamics of E-I in different brain states.
Revised:  Scaling the ensemble series to a matrix of Excitation-Inhibition (E-I) ensemble pairs, enabled us to quantify the detailed dynamics of E-I in different states.
Edit intent: clarity
Original:  Deep SWS showed moderate deviations .
Revised:  Deep SWS showed moderate deviations from balance. These multiscale features were also found in a spiking neurons model of balanced states.
Edit intent: clarity
Original:  In contrast, we observed a multiscale break-down of balance during instances of focal seizure.
Revised:  In contrast, we observed a multiscale break-down of balance during focal seizure.
Edit intent: clarity
Original:  In conclusion, cortex shows a multiscale E-I balance for all brain states, except seizures, showing that balanced E-I is a fundamental feature of normal brain activity.
Revised:  Summarizing, neocortex shows an overall multiscale E-I balance for all brain states, except seizures, showing that balanced E-I is a fundamental feature of normal brain activity.
Edit intent: clarity
Original:  In conclusion, cortex shows a multiscale E-I balance for all brain states, except seizures, showing that balanced E-I is a fundamental feature of normal brain activity.
Revised:  In conclusion, cortex shows a multiscale E-I balance across states, except seizures, showing that balanced E-I is a fundamental feature of normal brain activity.
Edit intent: clarity
Original:  We investigated here the dynamics of excitation and inhibition using dense multielectrode recordings in humans and monkeys.
Revised:  We investigated the dynamics of excitation and inhibition using dense multielectrode recordings in humans and monkeys.
Edit intent: clarity
Original: In this article  we introduce a fast iterative solver for sparse matrices arising from the finite element discretization of elliptic PDEs.
Revised: In this article , we introduce a fast iterative solver for sparse matrices arising from the finite element discretization of elliptic PDEs.
Edit intent: fluency
Original:  The solver uses a fast direct multi-frontal solver as a preconditioner to a simple fixed point iterative scheme .
Revised:  The solver uses a fast direct multi-frontal solver as a preconditioner , and use an iterative solver to achieve a desired accuracy .
Edit intent: clarity
Original:  Using this idea, we replace all large dense matrix operations in the multi-frontal elimination process with  HODLR operations to arrive at a faster and more memory efficient multi-frontal solver.
Revised:  Using this idea, we replace all large dense matrix operations in the multifrontal elimination process with HODLR operations to arrive at a faster and more memory efficient multi-frontal solver.
Edit intent: fluency
Original:  Using this idea, we replace all large dense matrix operations in the multi-frontal elimination process with  HODLR operations to arrive at a faster and more memory efficient multi-frontal solver.
Revised:  Using this idea, we replace all large dense matrix operations in the multi-frontal elimination process with HODLR operations to arrive at a faster and more memory efficient solver.
Edit intent: fluency
Original:  A compressed overlap graph that preserves all necessary information is constructed with the compressed reads to enable the final-stage assembly .
Revised:  A compressed overlap graph that preserves all necessary information is constructed with the compressed reads . Magnitudes of compression lead to magnitudes of reduction in read lengths, enabling magnitudes of savings in computational time and memory space .
Edit intent: clarity
Original:  Experiments with the sequencing data from the third generation technologies show that our method is able to assemble large genomes much more efficiently than existing methods.
Revised:  Experiments with the sequencing data from the third generation technologies show that our method is able to assemble large genomes magnitudes more efficiently than existing methods.
Edit intent: clarity
Original:  On a large PacBio human genome dataset we calculated the pair-wise alignment of 54x erroneous long reads of human genome in 6 hours on a desktop computer compared to the 405,000 CPU hours using a clusters, previously reported by Pacific Biosciences.
Revised:  For example, on a large PacBio human genome dataset we calculated the pair-wise alignment of 54x erroneous long reads of human genome in 6 hours on a desktop computer compared to the 405,000 CPU hours using a clusters, previously reported by Pacific Biosciences.
Edit intent: style
Original:  On a large PacBio human genome dataset we calculated the pair-wise alignment of 54x erroneous long reads of human genome in 6 hours on a desktop computer compared to the 405,000 CPU hours using a clusters, previously reported by Pacific Biosciences.
Revised:  On a large PacBio human genome dataset we calculated the all-pair alignment of 54x erroneous long reads of human genome in 6 hours on a desktop computer compared to the 405,000 CPU hours using a clusters, previously reported by Pacific Biosciences.
Edit intent: clarity
Original:  On a large PacBio human genome dataset we calculated the pair-wise alignment of 54x erroneous long reads of human genome in 6 hours on a desktop computer compared to the 405,000 CPU hours using a clusters, previously reported by Pacific Biosciences.
Revised:  On a large PacBio human genome dataset we calculated the pair-wise alignment of 54x erroneous long reads in 6 hours on a desktop computer compared to the 405,000 CPU hours using a clusters, previously reported by Pacific Biosciences.
Edit intent: clarity
Original:  On a large PacBio human genome dataset we calculated the pair-wise alignment of 54x erroneous long reads of human genome in 6 hours on a desktop computer compared to the 405,000 CPU hours using a clusters, previously reported by Pacific Biosciences.
Revised:  On a large PacBio human genome dataset we calculated the pair-wise alignment of 54x erroneous long reads of human genome in 6 hours compared to the 405,000 CPU hours using a clusters, previously reported by Pacific Biosciences.
Edit intent: clarity
Original:  On a large PacBio human genome dataset we calculated the pair-wise alignment of 54x erroneous long reads of human genome in 6 hours on a desktop computer compared to the 405,000 CPU hours using a clusters, previously reported by Pacific Biosciences.
Revised:  On a large PacBio human genome dataset we calculated the pair-wise alignment of 54x erroneous long reads of human genome in 6 hours on a desktop computer compared to the 405,000 CPU hours previously reported by Pacific Biosciences.
Edit intent: clarity
Original: The highly anticipated transition to the third generation DNA sequencing (3rdGS) technology have reached a stalemate primarily due to the high error rates (15-45\%), which make the assembly of long erroneous reads extremely challenging because existing software solutions for 3rdGS assembly are often overwhelmed by error correction tasks.
Revised: The highly anticipated transition from next generation sequencing (NGS) to third generation sequencing (3GS) has been difficult primarily due to the high error rates (15-45\%), which make the assembly of long erroneous reads extremely challenging because existing software solutions for 3rdGS assembly are often overwhelmed by error correction tasks.
Edit intent: clarity
Original: The highly anticipated transition to the third generation DNA sequencing (3rdGS) technology have reached a stalemate primarily due to the high error rates (15-45\%), which make the assembly of long erroneous reads extremely challenging because existing software solutions for 3rdGS assembly are often overwhelmed by error correction tasks.
Revised: The highly anticipated transition to the third generation DNA sequencing (3rdGS) technology have reached a stalemate primarily due to high error rates (15-45\%), which make the assembly of long erroneous reads extremely challenging because existing software solutions for 3rdGS assembly are often overwhelmed by error correction tasks.
Edit intent: fluency
Original: The highly anticipated transition to the third generation DNA sequencing (3rdGS) technology have reached a stalemate primarily due to the high error rates (15-45\%), which make the assembly of long erroneous reads extremely challenging because existing software solutions for 3rdGS assembly are often overwhelmed by error correction tasks.
Revised: The highly anticipated transition to the third generation DNA sequencing (3rdGS) technology have reached a stalemate primarily due to the high error rates (15-45\%), which make the assembly of long erroneous reads of large genomes challenging because existing software solutions for 3rdGS assembly are often overwhelmed by error correction tasks.
Edit intent: clarity
Original: The highly anticipated transition to the third generation DNA sequencing (3rdGS) technology have reached a stalemate primarily due to the high error rates (15-45\%), which make the assembly of long erroneous reads extremely challenging because existing software solutions for 3rdGS assembly are often overwhelmed by error correction tasks.
Revised: The highly anticipated transition to the third generation DNA sequencing (3rdGS) technology have reached a stalemate primarily due to the high error rates (15-45\%), which make the assembly of long erroneous reads extremely challenging because existing software solutions are often overwhelmed by error correction tasks.
Edit intent: clarity
Original:  In addition, we present two anti-entropy algorithms: a basic one that provides eventual convergence, and another one that ensures both convergence and causal consistency.
Revised:  In addition, we present an anti-entropy algorithms: a basic one that provides eventual convergence, and another one that ensures both convergence and causal consistency.
Edit intent: clarity
Original:  We analyze the data sets from many different perspectives, focusing on (a) productivity, patterns and trends, (b) centrality , and (c) community structures, and present an array of interesting findings .
Revised:  We analyze the data sets from many different perspectives, focusing on (a) centrality, (b) centrality , and (c) community structures, and present an array of interesting findings .
Edit intent: coherence
Original:  We analyze the data sets from many different perspectives, focusing on (a) productivity, patterns and trends, (b) centrality , and (c) community structures, and present an array of interesting findings .
Revised:  We analyze the data sets from many different perspectives, focusing on (a) productivity, patterns and trends, (b) community structures , and (c) community structures, and present an array of interesting findings .
Edit intent: clarity
Original:  We analyze the data sets from many different perspectives, focusing on (a) productivity, patterns and trends, (b) centrality , and (c) community structures, and present an array of interesting findings .
Revised:  We analyze the data sets from many different perspectives, focusing on (a) productivity, patterns and trends, (b) centrality , and (c) productivity, patterns and trends .
Edit intent: clarity
Original:  We also identify a handful of "hot" papers and suggest "Variable Selection" and several other areas as the "hot" areas in statistics .
Revised:  We have also identified a handful of "hot" papers and suggest "Variable Selection" and several other areas as the "hot" areas in statistics .
Edit intent: style
Original:  We also identify a handful of "hot" papers and suggest "Variable Selection" and several other areas as the "hot" areas in statistics .
Revised:  We also identify a handful of "hot" papers , suggesting "Variable Selection" and several other areas as the "hot" areas in statistics .
Edit intent: fluency
Original:  We also identify a handful of "hot" papers and suggest "Variable Selection" and several other areas as the "hot" areas in statistics .
Revised:  We also identify a handful of "hot" papers and suggest "Variable Selection" as one of the "hot" areas in statistics .
Edit intent: clarity
Original:  We also identify a handful of "hot" papers and suggest "Variable Selection" and several other areas as the "hot" areas in statistics .
Revised:  We also identify a handful of "hot" papers and suggest "Variable Selection" and several other areas as the "hot" areas .
Edit intent: clarity
Original:  We have identified about 15 meaningful communities  , including large-size communities such as " Biostatistics ", "Large-Scale Multiple Testing", "Variable Selection" as well as small-size communities such as "Dimensional Reduction", "Objective Bayes", "Quantile Regression", and "Theoretical Machine Learning".
Revised:  We have identified about 15 meaningful communities , including large-size ones such as " Biostatistics ", "Large-Scale Multiple Testing", "Variable Selection" as well as small-size communities such as "Dimensional Reduction", "Objective Bayes", "Quantile Regression", and "Theoretical Machine Learning".
Edit intent: clarity
Original:  We have identified about 15 meaningful communities  , including large-size communities such as " Biostatistics ", "Large-Scale Multiple Testing", "Variable Selection" as well as small-size communities such as "Dimensional Reduction", "Objective Bayes", "Quantile Regression", and "Theoretical Machine Learning".
Revised:  We have identified about 15 meaningful communities , including large-size communities such as " Biostatistics ", "Large-Scale Multiple Testing", "Variable Selection" as well as small-size ones such as "Dimensional Reduction", "Objective Bayes", "Quantile Regression", and "Theoretical Machine Learning".
Edit intent: clarity
Original:  Our findings shed light on research habits, trends, and topological patterns of statisticians , and our data sets provide a fertile ground for future researches on or related to social networks of statisticians.
Revised:  Our findings shed light on research habits, trends, and topological patterns of statisticians . The data sets provide a fertile ground for future researches on or related to social networks of statisticians.
Edit intent: coherence
Original:  Our controllers can rebalance supply and demand after disturbances, restore the frequency to its nominal value and preserve inter-area power flows.
Revised:  In particular, our controllers can rebalance supply and demand after disturbances, restore the frequency to its nominal value and preserve inter-area power flows.
Edit intent: coherence
Original:  Simulations are used to illustrate the properties of our solution .
Revised:  We illustrate its effectiveness through simulations .
Edit intent: clarity
Original: The weak variant of Hanani--Tutte theorem says that a graph is planar, if it can be drawn in the plane so that every pair of edges cross an even number of times.
Revised: The weak variant of Hanani-Tutte theorem says that a graph is planar, if it can be drawn in the plane so that every pair of edges cross an even number of times.
Edit intent: fluency
Original:  We prove a generalization of the weak Hanani--Tutte theorem that also easily implies the monotone variant of the weak Hanani--Tutte theorem by Pach and T\'oth.
Revised:  We prove a generalization of the weak Hanani-Tutte theorem that also easily implies the monotone variant of the weak Hanani--Tutte theorem by Pach and T\'oth.
Edit intent: fluency
Original:  We prove a generalization of the weak Hanani--Tutte theorem that also easily implies the monotone variant of the weak Hanani--Tutte theorem by Pach and T\'oth.
Revised:  We prove a generalization of the weak Hanani--Tutte theorem that also easily implies the monotone variant of the weak Hanani-Tutte theorem by Pach and T\'oth.
Edit intent: fluency
Original:  The Hanani--Tutte theorem says that a graph is planar, if it can be drawn in the plane so that every pair of edges not sharing a vertex cross an even number of times.
Revised:  The Hanani-Tutte theorem says that a graph is planar, if it can be drawn in the plane so that every pair of edges not sharing a vertex cross an even number of times.
Edit intent: fluency
Original:  We prove the variant of Hanani--Tutte theorem for strip clustered graphs if the underlying abstract graph is three connected or a tree.
Revised:  We prove the variant of Hanani-Tutte theorem for strip clustered graphs if the underlying abstract graph is three connected or a tree.
Edit intent: fluency
Original:  The proof of the latter result combines our forbidden substructure characterization of embedded strip clustered planar graphs with Tucker's characterization of 0--1 matrices with consecutive ones property.
Revised:  The proof of the latter result combines our forbidden substructure characterization of embedded strip clustered planar graphs with Tucker's characterization of 0-1 matrices with consecutive ones property.
Edit intent: fluency
Original:  Algorithms 8 (2) (2012) 14:1-14:23], we design a polynomial time approximation algorithm with ratio \Delta \slash log(log( %DIFDELCMD < \Delta)%%% \Delta \slash k) for the maximum clique and also show that the decision version of this problem is fixed parameter tractable for this particular family of graphs with complexity O(1.2127^{(p+k-1)^{k}}n).
Revised:  Algorithms 8 (2) (2012) 14:1-14:23], we design a polynomial time approximation algorithm with ratio \Delta \slash log(log( %DIFDELCMD < \Delta)%%% \Delta) \slash k) for the maximum clique and also show that the decision version of this problem is fixed parameter tractable for this particular family of graphs with complexity O(1.2127^{(p+k-1)^{k}}n).
Edit intent: fluency
Original:  Then we study a sublcass of inductive k-independent graphs, namely k-degenerate graphs.
Revised:  Then we study a subclass of inductive k-independent graphs, namely k-degenerate graphs.
Edit intent: fluency
Original: The fundamental properties of macroscopic gene-mating dynamic evolutionary systems are investigated.
Revised: Fundamental properties of macroscopic gene-mating dynamic evolutionary systems are investigated.
Edit intent: fluency
Original:  The full parameter space consists of all allowed parameters of these genotype frequencies.
Revised:  The full parameter space consists of all allowed genotype frequencies.
Edit intent: clarity
Original:  Our equations are uniquely derived from four fundamental assumptions within a population: (1) a closed system;
Revised:  Our equations are uniquely derived from four fundamental assumptions within any population: (1) a closed system;
Edit intent: clarity
Original:  Under time evolution, we show (1) the monotonic behavior of genotype frequencies, (2) a genotype or an allele that ever presents in the population will never become extinct, (3) the Hardy-Weinberg law, and (4) the global stability without chaos in the parameter space.
Revised:  Under time evolution, we show (1) the monotonic behavior of genotype frequencies, (2) any genotype or allele that appears in the population will never become extinct, (3) the Hardy-Weinberg law, and (4) the global stability without chaos in the parameter space.
Edit intent: clarity
Original:  To demonstrate the experimental evidence, as an example, we show an mapping from the blood type genotype frequencies of world ethnic groups to our stable fixed-point solutions.
Revised:  To demonstrate the experimental evidence, as an example, we show a mapping from the blood type genotype frequencies of world ethnic groups to our stable fixed-point solutions.
Edit intent: fluency
Original:  Second, from the mathematical viewpoint, the equilibrium solutions consist of a base manifold as a global stable attractor, attracting any initial point in a Euclidean fiber  to the fixed point where this fiber is attached.
Revised:  From the mathematical viewpoint, the equilibrium solutions consist of a base manifold as a global stable attractor, attracting any initial point in a Euclidean fiber to the fixed point where this fiber is attached.
Edit intent: coherence
Original:  Second, from the mathematical viewpoint, the equilibrium solutions consist of a base manifold as a global stable attractor, attracting any initial point in a Euclidean fiber  to the fixed point where this fiber is attached.
Revised:  Second, from the mathematical viewpoint, the equilibrium solutions consist of a base manifold as a global stable attractor, attracting any initial point in a Euclidean fiber to the fixed point where the fiber is attached.
Edit intent: clarity
Original:  In addition, the modification of our theory under the process of natural selection and mutation are addressed.
Revised:  In addition, the modification of our theory under the process of natural selection and mutation is addressed.
Edit intent: fluency
Original:  A model is proposed to describe a large class of systems within population genetics.
Edit intent: coherence
Original:  We focus on a single locus, arbitrary number alleles in a two-gender dioecious population  .
Revised:  We focus on a single locus, any number of alleles in a two-gender dioecious population .
Edit intent: clarity
Original:  Our governing equations are time-dependent continuous differential equations labeled by a set of genotype frequencies.
Revised:  Our governing equations are time-dependent differential equations labeled by a set of genotype frequencies.
Edit intent: clarity
Original:  The full parameter space consists of all allowed genotype frequencies.
Edit intent: coherence
Original:  Our equations are uniquely derived from four fundamental assumptions within any population: (1) a closed system;
Revised:  Our equations are uniquely derived from 4 assumptions within any population: (1) a closed system;
Edit intent: clarity
Original:  Even though our equations are non-linear with time evolutionary dynamics, we have an exactly solvable model.
Revised:  Even though our equations are nonlinear with time-evolutionary dynamics, we have an exactly solvable model.
Edit intent: fluency
Original:  Our findings are summarized from phenomenological and mathematical viewpoints.
Edit intent: coherence