wanyu/IteraTeR_v2 huggingface dataset:
Original: Networks provide a powerful approach to study a  variety of ecological systems, but their formulation does not typically account for multiple interaction types, for interactions that vary in space and time, or for interconnected systems such as networks of species networks.
Revised: Networks provide a powerful approach to study a large variety of ecological systems, but their formulation does not typically account for multiple interaction types, for interactions that vary in space and time, or for interconnected systems such as networks of species networks.
Edit intent: clarity
Original: Networks provide a powerful approach to study a  variety of ecological systems, but their formulation does not typically account for multiple interaction types, for interactions that vary in space and time, or for interconnected systems such as networks of species networks.
Revised: Networks provide a powerful approach to study a variety of ecological systems, their formulation does not typically account for multiple interaction types, for interactions that vary in space and time, or for interconnected systems such as networks of species networks.
Edit intent: coherence
Original: Networks provide a powerful approach to study a  variety of ecological systems, but their formulation does not typically account for multiple interaction types, for interactions that vary in space and time, or for interconnected systems such as networks of species networks.
Revised: Networks provide a powerful approach to study a variety of ecological systems, but their formulation does not typically account for multiple interaction types, interactions that vary in space and time, or for interconnected systems such as networks of species networks.
Edit intent: fluency
Original: Networks provide a powerful approach to study a  variety of ecological systems, but their formulation does not typically account for multiple interaction types, for interactions that vary in space and time, or for interconnected systems such as networks of species networks.
Revised: Networks provide a powerful approach to study a variety of ecological systems, but their formulation does not typically account for multiple interaction types, for interactions that vary in space and time, and interconnected systems such as networks of species networks.
Edit intent: fluency
Original: Networks provide a powerful approach to study a  variety of ecological systems, but their formulation does not typically account for multiple interaction types, for interactions that vary in space and time, or for interconnected systems such as networks of species networks.
Revised: Networks provide a powerful approach to study a variety of ecological systems, but their formulation does not typically account for multiple interaction types, for interactions that vary in space and time, or for interconnected systems such as networks of networks.
Edit intent: clarity
Original:  The emergent field of ' multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it  allows one to differentiate between and simultaneously model ' intralayer' and ' interlayer' connectivity.
Revised:  The emergent field of ` multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it allows one to differentiate between and simultaneously model ' intralayer' and ' interlayer' connectivity.
Edit intent: fluency
Original:  The emergent field of ' multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it  allows one to differentiate between and simultaneously model ' intralayer' and ' interlayer' connectivity.
Revised:  The emergent field of ' multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it allows one to differentiate and model ` intralayer' and ' interlayer' connectivity.
Edit intent: clarity
Original:  The emergent field of ' multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it  allows one to differentiate between and simultaneously model ' intralayer' and ' interlayer' connectivity.
Revised:  The emergent field of ' multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it allows one to differentiate between and simultaneously model ' intralayer' and ` interlayer' connectivity.
Edit intent: fluency
Original:  Here, we formally define ecological multilayer networks based on a review of previous  related approaches, illustrate their application and potential insights with analyses of existing data, and discuss limitations, challenges, and future applications.
Revised:  Here, we formally define ecological multilayer networks based on a review of previous and related approaches, illustrate their application and potential insights with analyses of existing data, and discuss limitations, challenges, and future applications.
Edit intent: fluency
Original:  Here, we formally define ecological multilayer networks based on a review of previous  related approaches, illustrate their application and potential insights with analyses of existing data, and discuss limitations, challenges, and future applications.
Revised:  Here, we formally define ecological multilayer networks based on a review of previous related approaches, illustrate their application and potential with analyses of existing data, and discuss limitations, challenges, and future applications.
Edit intent: clarity
Original:  The integration of multilayer network theory into ecology offers an exciting perspective to tackle ecological complexity, with the potential to provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.
Revised:  The integration of multilayer network theory into ecology offers largely untapped potential to further address ecological complexity, with the potential to provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.
Edit intent: clarity
Original:  The integration of multilayer network theory into ecology offers an exciting perspective to tackle ecological complexity, with the potential to provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.
Revised:  The integration of multilayer network theory into ecology offers an exciting perspective to tackle ecological complexity, to ultimately provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.
Edit intent: clarity
Original:  Here, we introduce the Synaptic Sampling Machine (SSM), a stochastic neural network model that uses synaptic unreliability as a means to stochasticity for sampling .
Revised:  Here, we introduce Synaptic Sampling Machines, a class of neural network models that uses synaptic unreliability as a means to stochasticity for sampling .
Edit intent: clarity
Original:  Here, we introduce the Synaptic Sampling Machine (SSM), a stochastic neural network model that uses synaptic unreliability as a means to stochasticity for sampling .
Revised:  Here, we introduce the Synaptic Sampling Machine (SSM), a stochastic neural network model that uses synaptic stochasticity as a means to stochasticity for sampling .
Edit intent: clarity
Original:  Synaptic unreliability plays the dual role of an efficient mechanism for sampling in neuromorphic hardware, and a regularizer during learningakin to DropConnect . Similar to the original formulation of Boltzmann machines, the SSM can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections.
Revised:  Synaptic unreliability plays the dual role of an efficient mechanism for sampling in neuromorphic hardware, and a regularizer during learningakin to DropConnect . Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections.
Edit intent: clarity
Original:  SSMs are remarkably robust to weight  pruning: removal of more than 80 \% of the weakest connections followed by cursory re-learning causes only a negligible performance loss on the MNIST task (4.8\% error rate).
Revised:  SSMs are remarkably robust to weight pruning: removal of more than 80 \% of the weakest connections followed by cursory re-learning causes a negligible performance loss on the MNIST task (4.8\% error rate).
Edit intent: clarity
Original:  These results show that SSMs offer substantial improvements in terms of performance, power and complexity over existing methods for unsupervised learning in spiking neural networks , and are thus promising models for machine learning in neuromorphic execution platforms .
Revised:  These results show that SSMs offer substantial improvements in terms of power and complexity over existing methods for unsupervised learning in spiking neural networks , and are thus promising models for machine learning in neuromorphic execution platforms .
Edit intent: clarity
Original:  These results show that SSMs offer substantial improvements in terms of performance, power and complexity over existing methods for unsupervised learning in spiking neural networks , and are thus promising models for machine learning in neuromorphic execution platforms .
Revised:  These results show that SSMs offer substantial improvements in terms of performance, power and complexity , and are thus promising models for machine learning in neuromorphic execution platforms .
Edit intent: clarity
Original:  These results show that SSMs offer substantial improvements in terms of performance, power and complexity over existing methods for unsupervised learning in spiking neural networks , and are thus promising models for machine learning in neuromorphic execution platforms .
Revised:  These results show that SSMs offer substantial improvements in terms of performance, power and complexity over existing methods for unsupervised learning in spiking neural networks , and are thus promising models for on-line learning in brain-inspired hardware .
Edit intent: clarity
Original: We present a nonparametric framework to model an evolving sequence of probability distributions that vary both due to underlying effects of sequential progression and confounding noise.
Revised: We present a nonparametric framework to model a short sequence of probability distributions that vary both due to underlying effects of sequential progression and confounding noise.
Edit intent: clarity
Original:  We introduce a new regression model for ordinal covariates where responses are univariate distributions and the underlying relationship reflects coherent changes in the distributions over increasing levels of the covariate , a concept we formalize as trends in distributions .
Revised:  We introduce a new regression model for ordinal covariates where responses are univariate distributions and the underlying relationship reflects consistent changes in the distributions over increasing levels of the covariate , a concept we formalize as trends in distributions .
Edit intent: clarity
Original:  Implemented via a fast alternating projections algorithm, our method exhibits numerous strengths in simulations and application to single-cell gene-expression data.
Revised:  Implemented via a fast alternating projections algorithm, our method exhibits numerous strengths in simulations and analyses of single-cell gene-expression data.
Edit intent: clarity
Original:  We also use algebraic techniques, involving computing the Hamming distances of affine semilinear groups and projective semilinear groups, and unions of cosets of AGL(1,q) and PGL(2,q) with Frobenius maps to obtain infinitely many new, improved lower bounds for M(n,d).
Revised:  We also use algebraic techniques, involving computing the Hamming distances of affine semilinear groups and projective semilinear groups, and unions of cosets of AGL(1,q) and PGL(2,q) with Frobenius maps to obtain new, improved lower bounds for M(n,d).
Edit intent: clarity
Original:  Here we develop and test 8-bit approximation algorithms, which provide improved utilization of the available bandwidth by compressing 32-bit gradients and nonlinear activations to 8-bit approximations.
Revised:  Here we develop and test 8-bit approximation algorithmsmake better use of the available bandwidth by compressing 32-bit gradients and nonlinear activations to 8-bit approximations.
Edit intent: clarity
Original:  Here we develop and test 8-bit approximation algorithmsmake better use of the available bandwidth by compressing 32-bit gradients and nonlinear activations to 8-bit approximations.
Revised:  Here we develop and test 8-bit approximation algorithms make better use of the available bandwidth by compressing 32-bit gradients and nonlinear activations to 8-bit approximations.
Edit intent: fluency
Original:  We  compare our data types with other methods and show that 8-bit approximations achieve state-of-the-art speedups for model parallelism in general and data parallelism with up to 200k parameters per layer .
Revised:  We compare our data types with other methods and show that 8-bit approximations achieve state-of-the-art speedups for model parallelism .
Edit intent: coherence
Original:  Thus 8-bit approximation is the single best method for parameter compression in the parallelization of convolutional networks .
Revised:  Thus 8-bit approximation is an efficient method to parallelize convolutional networks on very large systems of GPUs .
Edit intent: clarity
Original: The creation of practical deep learning data-products often requires the parallelization across processors and computers to make deep learning feasible on large data sets, but bottlenecks in communication bandwidth make it difficult to attain good speedups through parallelism.
Revised: The creation of practical deep learning data-products often requires parallelization across processors and computers to make deep learning feasible on large data sets, but bottlenecks in communication bandwidth make it difficult to attain good speedups through parallelism.
Edit intent: fluency
Original:  Here we develop and test 8-bit approximation algorithms  make better use of the available bandwidth by compressing 32-bit gradients and nonlinear activations to 8-bit approximations.
Revised:  Here we develop and test 8-bit approximation algorithms which make better use of the available bandwidth by compressing 32-bit gradients and nonlinear activations to 8-bit approximations.
Edit intent: fluency
Original:  It transforms demand into relations, based on the same basic idea for transforming objects and sets into relations in  prior work.
Revised:  It transforms demand into relations, based on the same basic idea for transforming objects and sets into relations in a prior work.
Edit intent: fluency
Original: Object queries are significantly easier to write, understand, and maintain than efficient low-level programs .
Revised: Object queries are essential in information seeking and decision making in vast areas of applications .
Edit intent: clarity
Original:  However, a query may involve any number and combination of objects and sets, which can be arbitrarily nested and aliased.
Revised:  However, a query may involve complex conditions on objects and sets, which can be arbitrarily nested and aliased.
Edit intent: clarity
Original:  The objects and sets involved , starting from the given demand---the given parameter values of interest---can change arbitrarily.
Revised:  The objects and sets involved as well as the demand---the given parameter values of interest---can change arbitrarily.
Edit intent: clarity
Original:  This paper describes such an automatic method.
Revised:  This paper describes an automatic method.
Edit intent: clarity
Original:  The method allows the queries to be written completely declaratively.
Revised:  The method allows powerful queries to be written completely declaratively.
Edit intent: clarity
Original:  It transforms demand into relations, based on the same basic idea for transforming objects and sets into relations in a prior work .
Revised:  It transforms demand as well as all objects and sets into relations in a prior work .
Edit intent: clarity
Original:  It transforms demand into relations, based on the same basic idea for transforming objects and sets into relations in a prior work .
Revised:  It transforms demand into relations, based on the same basic idea for transforming objects and sets into relations .
Edit intent: clarity
Original:  Most importantly, it defines and incrementally maintains invariants for not only the query results, but also all auxiliary values about the objects and sets involved, starting from the demand .
Revised:  Most importantly, it defines invariants for not only the query results, but also all auxiliary values about the objects and sets involved, starting from the demand .
Edit intent: clarity
Original:  Implementation and experiments with problems from a variety of application areas, including distributed algorithms  , confirm the analyzed complexities, trade-offs, and significant improvements over prior works .
Revised:  Implementation and experiments with problems from a variety of application areas, including distributed algorithms , confirm the analyzed complexities, trade-offs, and significant improvements over prior work .
Edit intent: fluency
Original:  Linking peripheral sensor properties to behavior requires computational models of nervous processes transforming sensory representation into action, which are constrained by quantitative data based on physiological and behavioral studies.
Revised:  Linking peripheral sensor properties to behavior requires computational models of nervous processes transforming sensory representations into actions, based on quantitative data from physiological and behavioral studies.
Edit intent: coherence
Original:  Recently, studies based on machine vision approaches are shedding light on the quantitative relationships between sensory polymorphism and the resulting behavioral variability.
Revised:  Recently, studies based on machine vision approaches are shedding light on the quantitative relationships between sensory polymorphisms and the resulting behavioral variability.
Edit intent: fluency
Original:  To gain a convergent understanding of the functional impact of sensory polymorphism in realistic environments, a close coordination among physiological, behavioral, and computational approaches is required.
Revised:  To reach a convergent understanding of the functional impact of sensory polymorphism in realistic environments, a close coordination among physiological, behavioral, and computational approaches is required.
Edit intent: clarity
Original:  To gain a convergent understanding of the functional impact of sensory polymorphism in realistic environments, a close coordination among physiological, behavioral, and computational approaches is required.
Revised:  To gain a convergent understanding of the functional impacts of sensory polymorphisms in realistic environments, a close coordination among physiological, behavioral, and computational approaches is required.
Edit intent: fluency
Original:  At the same time, such an interdisciplinary approach yields broad insights into the universal mechanisms in our cognitive processes and effective strategies to compensate for individual differences in daily life .
Revised:  Aiming at enhancing such integrative researches, this paper provides an overview for the recent progresses in those interdisciplinary approaches, and suggests effective strategies for such integrative paradigms .
Edit intent: clarity
Original:  The architecture introduces an ICN publish/subscribe communication approach to the wavelength layer ;
Revised:  The architecture introduces an ICN publish/subscribe communication approach to the wavelength layer ,
Edit intent: fluency
Original:  In this paper, we show that by constructing the gadgets using a powerful lemma of Bringmann and K\"unnemann (FOCS'15) and surrounding them with some carefully designed sequences, the framework of Abboud et al. can be improved upon to work for the case where the alphabet size is 4, yielding a conditional lower bound for the RNA folding problem  .
Revised:  In this paper, we show that by constructing the gadgets using a lemma of Bringmann and K\"unnemann (FOCS'15) and surrounding them with some carefully designed sequences, the framework of Abboud et al. can be improved upon to work for the case where the alphabet size is 4, yielding a conditional lower bound for the RNA folding problem .
Edit intent: clarity
Original: An RNA sequence is a string composed of four types of nucleotides, A, C, G, and U. Given an RNA sequence, the goal of the RNA folding problem is to find a maximum cardinality set of crossing-free pairs of the form \{A,U\} or \{C,G\}  . The problem is central in bioinformatics and has received much attention over the years.
Revised: An RNA sequence is a string composed of four types of nucleotides, A, C, G, and U. The goal of the RNA folding problem is to find a maximum cardinality set of crossing-free pairs of the form \{A,U\} or \{C,G\} . The problem is central in bioinformatics and has received much attention over the years.
Edit intent: clarity
Original: An RNA sequence is a string composed of four types of nucleotides, A, C, G, and U. Given an RNA sequence, the goal of the RNA folding problem is to find a maximum cardinality set of crossing-free pairs of the form \{A,U\} or \{C,G\}  . The problem is central in bioinformatics and has received much attention over the years.
Revised: An RNA sequence is a string composed of four types of nucleotides, A, C, G, and U. Given an RNA sequence, the goal of the RNA folding problem is to find a maximum cardinality set of crossing-free pairs of the form \{A,U\} or \{C,G\} in a given RNA sequence . The problem is central in bioinformatics and has received much attention over the years.
Edit intent: clarity
Original:  However, the current best algorithm for the problem still takes O%DIFDELCMD < \left(%%% \frac{n^3{\log^2 (n)}}%DIFDELCMD < \right) %%% time, which is only a slight improvement over the classic O(n^3) dynamic programming algorithm.
Revised:  %DIFDELCMD < \left(%%% \frac{n^3{\log^2 (n)}}%DIFDELCMD < \right) %%% time, which is only a slight improvement over the classic O(n^3) dynamic programming algorithm.
Edit intent: coherence
Original:  However, the current best algorithm for the problem still takes O%DIFDELCMD < \left(%%% \frac{n^3{\log^2 (n)}}%DIFDELCMD < \right) %%% time, which is only a slight improvement over the classic O(n^3) dynamic programming algorithm.
Revised:  However, the current best algorithm for the problem still takes O%DIFDELCMD < \left(%%% {\log^2 (n)}}%DIFDELCMD < \right) %%% time, which is only a slight improvement over the classic O(n^3) dynamic programming algorithm.
Edit intent: coherence
Original:  Whether the RNA folding problem can be solved in O(n^{3-\epsilon Abboud, Backurs, and Williams (FOCS '15) made the first progress by showing a conditional lower bound for a generalized version of the RNA folding problem based on a conjectured hardness of the k-clique problem.
Revised:  Whether the RNA folding problem can be solved in O(n^{3-\epsilon Abboud, Backurs, and Williams (FOCS 2015) demonstrated a conditional lower bound for a generalized version of the RNA folding problem based on a conjectured hardness of the k-clique problem.
Edit intent: clarity
Original:  A drawback of their work is that they require the RNA sequence to have at least 36 types of letters, making their result biologically irrelevant.
Revised:  Their lower bound requires the RNA sequence to have at least 36 types of letters, making their result biologically irrelevant.
Edit intent: clarity