wanyu/IteraTeR_v2 huggingface dataset:
Original: Two-player , zero-sum games of infinite duration and their quantitative versions are often used in verification to model the interaction between a controller (Eve) and an antagonistic environment (Adam).
Revised: Two-player , zero-sum games of infinite duration and their quantitative versions are used in verification to model the interaction between a controller (Eve) and an antagonistic environment (Adam).
Edit intent: clarity
Original: Two-player , zero-sum games of infinite duration and their quantitative versions are often used in verification to model the interaction between a controller (Eve) and an antagonistic environment (Adam).
Revised: Two-player , zero-sum games of infinite duration and their quantitative versions are often used in verification to model the interaction between a controller (Eve) and its environment (Adam).
Edit intent: clarity
Original:  In this work  we are interested in strategies of Eve that minimize her regret .
Revised:  In this work , we are interested in strategies of Eve that minimize her regret .
Edit intent: fluency
Original:  Finally, we propose a subclass of copyless CRAand we show that this subclass is closed under regular-lookahead .
Revised:  To find a better class we impose restrictions on copyless CRA, which ends successfully with a new robust computational model that is closed under regular-lookahead .
Edit intent: clarity
Original:  This estimator is drastically more efficient than prior approaches, enables the calculation of deterministic confidence intervals in all model parameters, and can easily enforce important physical constraints on the models such as detailed balance.
Revised:  This estimator is dramatically more efficient than prior approaches, enables the calculation of deterministic confidence intervals in all model parameters, and can easily enforce important physical constraints on the models such as detailed balance.
Edit intent: fluency
Original:  For several common time- and distance-based objectives, we show that the associated path optimization problems for multiple robots are all NP-hard on planargraphs . Establishing the computational intractability of optimal multi-robot path planning problems on planar graphs has important practical implications.
Revised:  For four common time- and distance-based objectives, we show that the associated path optimization problems for multiple robots are all NP-hard on planargraphs . Establishing the computational intractability of optimal multi-robot path planning problems on planar graphs has important practical implications.
Edit intent: clarity
Original:  In particular, from a theoretical perspective, our result implies that the best approach toward solving such problems, when the number of robots is large, is to augment the planar , discrete environment so as to reduce the sharing of paths among robots traveling in opposite directions along those paths.
Revised:  In particular, our result suggests the preferred approach toward solving such problems, when the number of robots is large, is to augment the planar , discrete environment so as to reduce the sharing of paths among robots traveling in opposite directions along those paths.
Edit intent: clarity
Original:  In particular, from a theoretical perspective, our result implies that the best approach toward solving such problems, when the number of robots is large, is to augment the planar , discrete environment so as to reduce the sharing of paths among robots traveling in opposite directions along those paths.
Revised:  In particular, from a theoretical perspective, our result implies that the best approach toward solving such problems, when the number of robots is large, is to augment the planar environment to reduce the sharing of paths among robots traveling in opposite directions along those paths.
Edit intent: clarity
Original:  In particular, from a theoretical perspective, our result implies that the best approach toward solving such problems, when the number of robots is large, is to augment the planar , discrete environment so as to reduce the sharing of paths among robots traveling in opposite directions along those paths.
Revised:  In particular, from a theoretical perspective, our result implies that the best approach toward solving such problems, when the number of robots is large, is to augment the planar , discrete environment so as to reduce the sharing of paths among robots traveling in opposite directions on those paths.
Edit intent: fluency
Original:  Indeed, such efficiency boosting structures, such as two-way roads and elevated intersections, are rather prevalent in the real world .
Revised:  Indeed, such efficiency boosting structures, such as highways and elevated intersections, are rather prevalent in the real world .
Edit intent: clarity
Original:  Indeed, such efficiency boosting structures, such as two-way roads and elevated intersections, are rather prevalent in the real world .
Revised:  Indeed, such efficiency boosting structures, such as two-way roads and elevated intersections, are ubiquitous in robotics and transportation applications .
Edit intent: clarity
Original: Protein distributions measured under a broad set of conditions in bacteria and yeast exhibit a universal skewed shape, with variances depending quadratically on means.
Revised: Protein distributions measured under a broad set of conditions in bacteria and yeast were shown to exhibit a common skewed shape, with variances depending quadratically on means.
Edit intent: clarity
Original:  For bacteria these properties are reproduced by protein accumulation and division dynamics across generations.
Revised:  For bacteria these properties were reproduced by temporal measurements of protein content, showing accumulation and division dynamics across generations.
Edit intent: clarity
Original:  For bacteria these properties are reproduced by protein accumulation and division dynamics across generations.
Revised:  For bacteria these properties are reproduced by protein accumulation and division across generations.
Edit intent: clarity
Original:  We present a stochastic growth-and-division model with feedback which captures these observed properties.
Revised:  Here we present a stochastic growth-and-division model with feedback which captures these observed properties.
Edit intent: coherence
Original:  Estimating this parameter from bacterial temporal data reproduces the measured universal distribution shape with high accuracy, and leads to predictions for future experiments.
Revised:  Estimating this parameter from bacterial temporal data reproduces the measured distribution shape with high accuracy, and leads to predictions for future experiments.
Edit intent: clarity
Original: Any primitive binary word contains at least two unbordered conjugates.
Edit intent: coherence
Original:  In this context, we show that the cost-optimal network design can be efficiently found ( in polynomial time ) using Geometric Programming .
Revised:  In this context, we show that the cost-optimal network design can be efficiently found ( in polynomial time .
Edit intent: coherence
Original:  At the same time, mounting experimental evidence from studies of microbial communities raises the intriguing question whether this intuition is an accurate description of the microbial world.
Revised:  At the same time, mounting experimental evidence raises the intriguing question whether this intuition is an accurate description of the microbial world.
Edit intent: clarity
Original:  At the same time, mounting experimental evidence from studies of microbial communities raises the intriguing question whether this intuition is an accurate description of the microbial world.
Revised:  At the same time, mounting experimental evidence from studies of microbial communities raises the intriguing question whether this perspective is appropriate for the microbial world.
Edit intent: clarity
Original:  This work builds on Mac Arthur's model of competitive coexistence on multiple resources to construct a framework that does not rely on postulated existence of species as fundamental ecological variables .
Revised:  Here, an alternative formalism is proposed that does not rely on postulated existence of species as fundamental ecological variables .
Edit intent: clarity
Original:  This work builds on Mac Arthur's model of competitive coexistence on multiple resources to construct a framework that does not rely on postulated existence of species as fundamental ecological variables .
Revised:  This work builds on Mac Arthur's model of competitive coexistence on multiple resources to construct a framework that does not require postulating the existence of species as fundamental ecological variables .
Edit intent: clarity
Original:  The relevance of this framework for understanding the complexity of naturally observed microbial communities is discussed.
Revised:  The relevance of this framework for understanding the complexity of natural microbial communities is discussed.
Edit intent: clarity
Original:  Based on dynamical modes of population fluctuations, this approach reduces to the familiar species-based perspective when it applies, but remains well-defined even when the speciesdescription breaks down .
Revised:  This formalism is used to construct a novel regime which, it is argued, fundamentally cannot be conceptualized as a system of interacting species .
Edit intent: coherence
Original:  The relevance of this framework for understanding the complexity of natural microbial communities is discussed.
Revised:  The relevance of this theoretical construct for describing highly diverse natural ecosystems is discussed.
Edit intent: clarity
Original: As in classical runtime analysis theOneMax problem is the most prominent test problem also in black-box  complexity theory.
Revised: OneMax problem is the most prominent test problem also in black-box complexity theory.
Edit intent: coherence
Original: As in classical runtime analysis theOneMax problem is the most prominent test problem also in black-box  complexity theory.
Revised: As in classical runtime analysis the problem is the most prominent test problem also in black-box complexity theory.
Edit intent: clarity
Original:  It is known that the unrestricted, the memory-restricted , and the ranking-based black-box complexities of this problem are all of order n/ \! \log n, where n denotes the length of the bit strings.
Revised:  It is known that the unrestricted, the memory-restricted ranking-based black-box complexities of this problem are all of order n/ \! \log n, where n denotes the length of the bit strings.
Edit intent: fluency
Original:  It is known that the unrestricted, the memory-restricted , and the ranking-based black-box complexities of this problem are all of order n/ \! \log n, where n denotes the length of the bit strings.
Revised:  It is known that the unrestricted, the memory-restricted , and the ranking-based black-box complexities of this problem are all of order n/ \log n, where n denotes the length of the bit strings.
Edit intent: clarity
Original:  We extend this result by showing that even if elitist selection is enforced, there exists a linear time algorithm optimizingOneMax with failure probability o(1) . This is quite surprising given that all previously regarded algorithms with o(n\log n) runtime onOneMax , in particular the quite natural (1+(\lambda, \lambda))~GA, heavily exploit information encoded in search points of fitness much smaller than the current best-so-far solution  .
Revised:  We extend this result by showing that even if elitist selection is enforced, there exists a linear time algorithm optimizing with failure probability o(1) . This is quite surprising given that all previously regarded algorithms with o(n\log n) runtime onOneMax , in particular the quite natural (1+(\lambda, \lambda))~GA, heavily exploit information encoded in search points of fitness much smaller than the current best-so-far solution .
Edit intent: fluency
Original:  We extend this result by showing that even if elitist selection is enforced, there exists a linear time algorithm optimizingOneMax with failure probability o(1) . This is quite surprising given that all previously regarded algorithms with o(n\log n) runtime onOneMax , in particular the quite natural (1+(\lambda, \lambda))~GA, heavily exploit information encoded in search points of fitness much smaller than the current best-so-far solution  .
Revised:  We extend this result by showing that even if elitist selection is enforced, there exists a linear time algorithm optimizingOneMax OneMax , in particular the quite natural (1+(\lambda, \lambda))~GA, heavily exploit information encoded in search points of fitness much smaller than the current best-so-far solution .
Edit intent: clarity
Original:  We extend this result by showing that even if elitist selection is enforced, there exists a linear time algorithm optimizingOneMax with failure probability o(1) . This is quite surprising given that all previously regarded algorithms with o(n\log n) runtime onOneMax , in particular the quite natural (1+(\lambda, \lambda))~GA, heavily exploit information encoded in search points of fitness much smaller than the current best-so-far solution  .
Revised:  We extend this result by showing that even if elitist selection is enforced, there exists a linear time algorithm optimizingOneMax with failure probability o(1) . This is quite surprising given that all previously regarded algorithms with o(n\log n) runtime on , in particular the quite natural (1+(\lambda, \lambda))~GA, heavily exploit information encoded in search points of fitness much smaller than the current best-so-far solution .
Edit intent: fluency
Original:  Also for other settings of \mu and \lambda we show that the (\mu+\lambda)\emph{elitist memory-restricted ranking-based black-box complexity ofOneMax is as small as (an advanced version of) the information-theoretic lower bound.
Revised:  \emph{elitist memory-restricted ranking-based black-box complexity ofOneMax is as small as (an advanced version of) the information-theoretic lower bound.
Edit intent: coherence
Original:  Also for other settings of \mu and \lambda we show that the (\mu+\lambda)\emph{elitist memory-restricted ranking-based black-box complexity ofOneMax is as small as (an advanced version of) the information-theoretic lower bound.
Revised:  Also for other settings of \mu and \lambda we show that the (\mu+\lambda)\emph{ ofOneMax is as small as (an advanced version of) the information-theoretic lower bound.
Edit intent: clarity
Original:  Also for other settings of \mu and \lambda we show that the (\mu+\lambda)\emph{elitist memory-restricted ranking-based black-box complexity ofOneMax is as small as (an advanced version of) the information-theoretic lower bound.
Revised:  Also for other settings of \mu and \lambda we show that the (\mu+\lambda)\emph{elitist memory-restricted ranking-based black-box complexity OneMax is as small as (an advanced version of) the information-theoretic lower bound.
Edit intent: fluency
Original:  Also for other settings of \mu and \lambda we show that the (\mu+\lambda)\emph{elitist memory-restricted ranking-based black-box complexity ofOneMax is as small as (an advanced version of) the information-theoretic lower bound.
Revised:  Also for other settings of \mu and \lambda we show that the (\mu+\lambda)\emph{elitist memory-restricted ranking-based black-box complexity of is as small as (an advanced version of) the information-theoretic lower bound.
Edit intent: clarity
Original:  Our result enlivens the quest for natural evolutionary algorithms optimizing OneMax in o(n \log n) iterations.
Revised:  Our result enlivens the quest for natural evolutionary algorithms optimizing OneMax in o(n \log n) iterations.
Edit intent: fluency
Original: We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, obtained by a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain.
Revised: We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, based on a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain.
Edit intent: clarity
Original:  ~ parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision  .
Revised:  parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision .
Edit intent: fluency
Original:  ~ parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision  .
Revised:  ~ parameterizing the intermediate temporal scale levels, (ii) analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision .
Edit intent: fluency
Original:  ~ parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision  .
Revised:  ~ parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision .
Edit intent: fluency
Original:  ~ parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision  .
Revised:  ~ parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation , (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision .
Edit intent: clarity
Original:  ~ parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision  .
Revised:  ~ parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision .
Edit intent: fluency
Original:  ~ parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) ~ computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision  .
Revised:  ~ parameterizing the intermediate temporal scale levels, (ii) ~ analysing the resulting temporal dynamics, (iii) ~ transferring the theory to a discrete implementation in terms of recursive filters over time and (iv) ~ computing scale-normalized spatio-temporal derivative expressions for spatio-temporal feature detection and (v) computational modelling of receptive fields in the lateral geniculate nucleus (LGN) and the primary visual cortex (V1) in biological vision .
Edit intent: fluency
Original:  The method consists in moving the parameters through change of measure, which we call a weak perturbation, in particular decoupling the usual wealth equation from the varying parameters.
Revised:  The method consists in moving the parameters through change of measure, which we call a weak perturbation, decoupling the usual wealth equation from the varying parameters.
Edit intent: coherence
Original:  Reservoir computing is an approach that takes advantage of collective dynamics of systems for real-time computing.
Revised:  Reservoir computing is an approach that takes advantage of collective system dynamics for real-time computing.
Edit intent: clarity
Original:  In this approach, a dynamical system, called a reservoir, is excited with a time varying signal and observations of its states are used to reconstruct a desired output signal.
Revised:  A dynamical system, called a reservoir, is excited with a time varying signal and observations of its states are used to reconstruct a desired output signal.
Edit intent: clarity
Original:  In this approach, a dynamical system, called a reservoir, is excited with a time varying signal and observations of its states are used to reconstruct a desired output signal.
Revised:  In this approach, a dynamical system, called a reservoir, is excited with a time-varying signal and observations of its states are used to reconstruct a desired output signal.
Edit intent: fluency
Original:  Here, we introduce an approach that  composes a set of interconnected memristive networks into a larger reservoir utilizing signal amplification and restoration to reduce reservoir state correlation which improves feature extraction from the input signals.
Revised:  Here, we introduce an approach that composes a set of interconnected memristive networks into a larger reservoir . We use signal amplification and restoration to reduce reservoir state correlation which improves feature extraction from the input signals.
Edit intent: coherence