wanyu/IteraTeR_v2 huggingface dataset:
Original:  For this bounded environment , we derive an analytical expression for the channel impulse response subject to fluid flow and magnetic drift . Numerical results, obtained by particle-based simulation, validate the accuracy of the derived analytical expressions .
Revised:  For this model , we derive an analytical expression for the channel impulse response subject to fluid flow and magnetic drift . Numerical results, obtained by particle-based simulation, validate the accuracy of the derived analytical expressions .
Edit intent: clarity
Original:  For this bounded environment , we derive an analytical expression for the channel impulse response subject to fluid flow and magnetic drift . Numerical results, obtained by particle-based simulation, validate the accuracy of the derived analytical expressions .
Revised:  For this bounded environment , we derive an analytical expression for the channel impulse response subject to fluid flow and magnetic drift .
Edit intent: clarity
Original:  This model can explain the experimentally found capability of neural circuits to configure themselves for a given task , and to compensate automatically for  changes in the network or task.
Revised:  This model can explain the capability of networks of neurons in the brain to configure themselves for a given task , and to compensate automatically for changes in the network or task.
Edit intent: clarity
Original:  This model can explain the experimentally found capability of neural circuits to configure themselves for a given task , and to compensate automatically for  changes in the network or task.
Revised:  This model can explain the experimentally found capability of neural circuits to configure themselves for a given task , and to compensate automatically for later changes in the network or task.
Edit intent: clarity
Original:  In fact, it was recently shown show that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity.
Revised:  In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity.
Edit intent: fluency
Original:  On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as Bayesian policy sampling .
Revised:  On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations .
Edit intent: clarity
Original: N distinguishable players are randomly fitted with a white or black hat, where the probabilities of getting a white or black hat may be different for each player, but known to all the players.
Revised: Three distinguishable players are randomly fitted with a white or black hat, where the probabilities of getting a white or black hat may be different for each player, but known to all the players.
Edit intent: style
Original:  We find explicit solutions in case of N =3 and N =4.
Edit intent: coherence
Original:  HPTT also offers an optional autotuning framework---guided by a performance mode---that explores a vast search space of implementations at runtime (similar to FFTW).
Revised:  HPTT also offers an optional autotuning framework---guided by a performance model---that explores a vast search space of implementations at runtime (similar to FFTW).
Edit intent: clarity
Original:  On CPU , our algorithm uses much less memory than im2col and in most cases is also faster .
Revised:  On CPU , our algorithm is faster than im2col and in most cases is also faster .
Edit intent: clarity
Original:  On CPU , our algorithm uses much less memory than im2col and in most cases is also faster .
Revised:  On CPU , our algorithm uses much less memory than im2col in most cases is also faster .
Edit intent: coherence
Original:  On CPU , our algorithm uses much less memory than im2col and in most cases is also faster .
Revised:  On CPU , our algorithm uses much less memory than im2col and in most cases .
Edit intent: clarity
Original: This paper proposes a new data-driven approach for modeling detailed splashes for liquid simulations with neural networks.
Revised: This paper proposes a new data-driven approach to model detailed splashes for liquid simulations with neural networks.
Edit intent: fluency
Original:  Our model learns to generate small-scale splash detail for  fluid-implicit-particle methods using training data acquired from physically accurate, high-resolution simulations.
Revised:  Our model learns to generate small-scale splash detail for the fluid-implicit-particle methods using training data acquired from physically accurate, high-resolution simulations.
Edit intent: fluency
Original:  Our model learns to generate small-scale splash detail for  fluid-implicit-particle methods using training data acquired from physically accurate, high-resolution simulations.
Revised:  Our model learns to generate small-scale splash detail for fluid-implicit-particle method using training data acquired from physically accurate, high-resolution simulations.
Edit intent: fluency
Original:  Our model learns to generate small-scale splash detail for  fluid-implicit-particle methods using training data acquired from physically accurate, high-resolution simulations.
Revised:  Our model learns to generate small-scale splash detail for fluid-implicit-particle methods using training data acquired from physically parametrized, high resolution simulations.
Edit intent: clarity
Original:  In this study, a deep learning architecture composed of multi-layer convolution neural network was used to develop three types of high-level predictive models: regression model (deepAOT-R), multi-classification  (deepAOT-C) model and multitask model (deepAOT-CR) for AOT evaluation  .
Revised:  In this study, a deep learning architecture composed of multi-layer convolution neural network was used to develop three types of high-level predictive models: regression model (deepAOT-R), multi-classification model (deepAOT-C) model and multitask model (deepAOT-CR) for AOT evaluation .
Edit intent: fluency
Original:  In this study, a deep learning architecture composed of multi-layer convolution neural network was used to develop three types of high-level predictive models: regression model (deepAOT-R), multi-classification  (deepAOT-C) model and multitask model (deepAOT-CR) for AOT evaluation  .
Revised:  In this study, a deep learning architecture composed of multi-layer convolution neural network was used to develop three types of high-level predictive models: regression model (deepAOT-R), multi-classification (deepAOT-C) and multi-task model (deepAOT-CR) for AOT evaluation .
Edit intent: fluency
Original:  In this study, a deep learning architecture composed of multi-layer convolution neural network was used to develop three types of high-level predictive models: regression model (deepAOT-R), multi-classification  (deepAOT-C) model and multitask model (deepAOT-CR) for AOT evaluation  .
Revised:  In this study, a deep learning architecture composed of multi-layer convolution neural network was used to develop three types of high-level predictive models: regression model (deepAOT-R), multi-classification (deepAOT-C) model and multitask model (deepAOT-CR) for AOT evaluation were constructed .
Edit intent: clarity
Original:  For the two external datasets containing 1673 (test set I) and 375 (test set II) compounds, the R2 and mean absolute error (MAE) of deepAOTR on the test set I were 0.864 and 0.195, and the prediction accuracy of deepAOT-C was 95.5\% and 96.3\% on the test set I and II, respectively.
Revised:  For the two external datasets containing 1673 (test set I) and 375 (test set II) compounds, the R2 and mean absolute error (MAE) of deepAOT-R on the test set I were 0.864 and 0.195, and the prediction accuracy of deepAOT-C was 95.5\% and 96.3\% on the test set I and II, respectively.
Edit intent: fluency
Original:  To address this challenge, we developed SILKMOTH , a system capable of rapidly discovering related set pairs in collections of sets.
Revised:  To address this challenge, we developed SilkMoth , a system capable of rapidly discovering related set pairs in collections of sets.
Edit intent: fluency
Original:  Internally, SILKMOTH creates a signature for each set, with the property that any other set which is related must match the signature.
Revised:  Internally, SilkMoth creates a signature for each set, with the property that any other set which is related must match the signature.
Edit intent: fluency
Original:  SILKMOTH then uses these signatures to prune the search space, so only sets which match the signatures are left as candidates.
Revised:  SilkMoth then uses these signatures to prune the search space, so only sets which match the signatures are left as candidates.
Edit intent: fluency
Original:  Finally, SILKMOTH applies the maximum matching metric on remaining candidates to verify which of these candidates are truly related sets.
Revised:  Finally, SilkMoth applies the maximum matching metric on remaining candidates to verify which of these candidates are truly related sets.
Edit intent: fluency
Original:  In this process  vertices receive grains of sand, and once the number of grains exceeds their degree, they topple by sending grains to their neighbors.
Revised:  In this process , vertices receive grains of sand, and once the number of grains exceeds their degree, they topple by sending grains to their neighbors.
Edit intent: fluency
Original:  Through a more refined and global analysis of electrical potentials and random walks, we give an upper bound of O(n^4\log^4{n}) and a lower bound of \Omega(n^4)  for the transience class of the n \times n grid.
Revised:  Through a more refined and global analysis of electrical potentials and random walks, we give an O(n^4\log^4{n}) and a lower bound of \Omega(n^4) for the transience class of the n \times n grid.
Edit intent: coherence
Original:  Our methods naturally extend to n^d-sized d-dimensional grids to give an upper bound of O(n^{3d - 2}\log^{d+2}{n}) and a lower bound of \Omega(n^{3d -2})  .
Revised:  Our methods naturally extend to n^d-sized d-dimensional grids to give O(n^{3d - 2}\log^{d+2}{n}) and a lower bound of \Omega(n^{3d -2}) .
Edit intent: clarity
Original:  Our model is constrained by the parameters of a mixed signal analogue-digital neuromorphic device and is driven by the output of a neuromorphic vision sensor DVS.
Edit intent: coherence
Original: We present the optimisation of a neuromorphic adaptation of a spiking neural network model of the locust Lobula Giant Movement Detector (LGMD) , which detects looming objects and can be used to facilitate obstacle avoidance  in robotic applications.
Revised: The Lobula Giant Movement Detector (LGMD) , which detects looming objects and can be used to facilitate obstacle avoidance in robotic applications.
Edit intent: coherence
Original: We present the optimisation of a neuromorphic adaptation of a spiking neural network model of the locust Lobula Giant Movement Detector (LGMD) , which detects looming objects and can be used to facilitate obstacle avoidance  in robotic applications.
Revised: We present the optimisation of a neuromorphic adaptation of a spiking neural network model of the locust Lobula Giant Movement Detector (LGMD) , which detects looming objects and can be used to facilitate obstacle avoidance strategies in robotic applications.
Edit intent: clarity
Original:  Therefore, we investigate the use of Differential Evolution and self-adaptive DE (SADE) to find optimal values.
Revised:  Therefore, we investigate the use of Self-Adaptive Differential Evolution (SADE) to find optimal values.
Edit intent: clarity
Original:  (3) We study their congruence properties , for fixed and arbitrary `parts' , many of which are analogous to those of ordinary binomial coefficients or related quantities.
Revised:  (3) We study their congruence properties , for fixed and arbitrary number of parts , many of which are analogous to those of ordinary binomial coefficients or related quantities.
Edit intent: clarity
Original:  (3) We study their congruence properties , for fixed and arbitrary `parts' , many of which are analogous to those of ordinary binomial coefficients or related quantities.
Revised:  (3) We study their congruence properties , for fixed and arbitrary `parts' , many of which are analogous to those of ordinary binomial coefficients and related quantities.
Edit intent: fluency
Original:  Via the Central Limit Theorem and their multivariate generating functions, (4) we also investigate the asymptotic behavior of several special cases  of weighted vector compositions.
Revised:  Via the Central Limit Theorem and their multivariate generating functions, (4) we also investigate the asymptotic behavior of several special cases of numbers of weighted vector compositions.
Edit intent: clarity
Original:  We introduce two different mixing mechanisms: spatial relocation , and non-local interaction ("telephoning").
Revised:  We introduce two different mixing mechanisms: spatial relocation and non-local interaction ("telephoning").
Edit intent: fluency
Original:  We find that a relatively small amount of mixing by telephoningbreaks up polarization and hastens consensus, and the effects of mixing by relocation are even more pronounced.
Revised:  Interestingly, the mechanisms that create deadlock in the spatial model have the opposite affect when there is a sufficient amount of telephoning: not only is polarization and deadlock broken up, but consensus is hastened. The effects of mixing by relocation are even more pronounced.
Edit intent: clarity
Original:  We introduce two different mixing mechanisms : spatial relocation and non-local interaction ("telephoning")  .
Revised:  We introduce two different mixing mechanisms , spatial relocation and non-local interaction ("telephoning") .
Edit intent: fluency
Original:  Interestingly, the mechanisms that create deadlock in the  spatial model have the opposite affect when there is a sufficient amount of telephoning: not only is polarization and deadlock broken up, but consensus is hastened.
Revised:  Interestingly, the mechanisms that create deadlock in the spatial model have the opposite effect when there is a sufficient amount of telephoning: not only is polarization and deadlock broken up, but consensus is hastened.
Edit intent: coherence
Original:  Interestingly, the mechanisms that create deadlock in the  spatial model have the opposite affect when there is a sufficient amount of telephoning: not only is polarization and deadlock broken up, but consensus is hastened.
Revised:  Interestingly, the mechanisms that create deadlock in the spatial model have the opposite affect when there is a sufficient amount of mixing. With telephoning, not only is polarization and deadlock broken up, but consensus is hastened.
Edit intent: clarity
Original:  Some of the existing regression-based methods do not guarantee that estimators of breed composition will lie in the appropriate parameter space and none of them accounts for uncertainty about allele frequencies in the pure breeds, that is, uncertainty about the design matrix.
Revised:  Some of the existing regression-based methods do not guarantee that estimators of breed composition will lie in the appropriate parameter space and none of them account for uncertainty about allele frequencies in the pure breeds, that is, uncertainty about the design matrix.
Edit intent: fluency
Original:  We discover that rather than the frequent myopic- and self-referencing that was common in the early 20^{th century, modern scientists instead tend to look for literature further back and farther around.
Revised:  We discover that rather than the frequent myopic- and self-referencing that was common in the early 20th century, modern scientists instead tend to look for literature further back and farther around.
Edit intent: fluency
Original:  Finally, we also observe the globalization of scientific development from 1900 to 2015, including 25-fold and 7-fold increases in international collaborations and citations, respectively, as well as a dramatic decline in the dominant accumulation of citations by the US, the UK, and Germany, from  95 %DIFDELCMD < \\%%% %DIF < to 50\\% over the same period.
Revised:  Finally, we also observe the globalization of scientific development from 1900 to 2015, including 25-fold and 7-fold increases in international collaborations and citations, respectively, as well as a dramatic decline in the dominant accumulation of citations by the US, the UK, and Germany, from ~ 95 %DIFDELCMD < \\%%% %DIF < to 50\\% over the same period.
Edit intent: fluency
Original:  We evaluate the agent in two classical reinforcement learning scenarios: the Mountain Car benchmark and the Acrobot double pendulum, finding that in both cases the neural controller reaches a point of criticality, which coincides with a transition point between two regimes of the agent's behaviour, maximizing the synergistic information between hidden neurons and sensorimotor patterns.
Revised:  We evaluate the agent in two classical reinforcement learning scenarios: the Mountain Car benchmark and the Acrobot double pendulum, finding that in both cases the neural controller reaches a point of criticality, which coincides with a transition point between two regimes of the agent's behaviour, maximizing the mutual information between neurons and sensorimotor patterns.
Edit intent: clarity
Original: This paper outlines a methodological approach to generate adaptive agents driving themselves near points of criticality.
Revised: This paper outlines a methodological approach for designing adaptive agents driving themselves near points of criticality.
Edit intent: clarity
Original:  Our approach captures the well-known principle of universality that classifies critical phenomena inside a few universality classes of systems without relying on specific mechanisms or topologies.
Revised:  Our approach exploits the well-known principle of universality that classifies critical phenomena inside a few universality classes of systems without relying on specific mechanisms or topologies.
Edit intent: clarity
Original:  Our approach captures the well-known principle of universality that classifies critical phenomena inside a few universality classes of systems without relying on specific mechanisms or topologies.
Revised:  Our approach captures the well-known principle of universality , which classifies critical phenomena inside a few universality classes of systems without relying on specific mechanisms or topologies.
Edit intent: fluency
Original:  Our approach captures the well-known principle of universality that classifies critical phenomena inside a few universality classes of systems without relying on specific mechanisms or topologies.
Revised:  Our approach captures the well-known principle of universality that classifies critical phenomena inside a few universality classes of systems independently of their specific mechanisms or topologies.
Edit intent: clarity
Original:  In particular, we implement an artificial embodied agent controlled by a neural network maintaining a correlation structure randomly sampled form a lattice Ising model at a critical point.
Revised:  In particular, we implement an artificial embodied agent controlled by a neural network maintaining a correlation structure randomly sampled from a lattice Ising model at a critical point.
Edit intent: fluency
Original:  The proposed lower bound provides a simple approximation that is increasingly accurate in the high reliability region .
Revised:  The proposed bounds provide simple approximations that are increasingly accurate in the high reliability region .
Edit intent: clarity
Original:  Here we investigate the nonequilibrium energetics of a single molecule kinesin by measuring the motion of an attached probe particle and its response to external forces with optical tweezers.
Revised:  Here we investigate the nonequilibrium energetics of a single molecule kinesin by measuring the fluctuation-response relation of a probe attached to the motor using optical tweezers.
Edit intent: clarity
Original:  The sum of the heat dissipation estimated from the violation of the fluctuation-response relation and the output power was inconsistent with the input free energy rate, implying that internal dissipation is dominant.
Revised:  The sum of the dissipation and work did not amount to the input free energy rate, implying that internal dissipation is dominant.
Edit intent: clarity
Original:  The robot routing problem is modeled as a graph whose nodes are stochastic processes generating potential rewards over discrete time.
Revised:  The *robot routing problem* is modeled as a graph whose nodes are stochastic processes generating potential rewards over discrete time.
Edit intent: fluency
Original:  The rewards are generated according to the stochastic process, but at each step, an existing reward may disappear with a given probability.
Revised:  The rewards are generated according to the stochastic process, but at each step, an existing reward disappears with a given probability.
Edit intent: clarity
Original:  The optimization question asks how to select a path in the graph to maximize the expected collected rewards.
Revised:  The optimization question asks to compute an optimal (or epsilon-optimal) path that maximizes the expected collected rewards.
Edit intent: clarity
Original: The activity of a neuronal network, characterized by action potentials (spikes)  is constrained by the intrinsic properties of neurons and their interactions.
Revised: The activity of a neuronal network, characterized by action potentials (spikes) , is constrained by the intrinsic properties of neurons and their interactions.
Edit intent: fluency
Original:  When a neuronal network is submitted to external stimuli, the statistics of spikes changes, and becomes difficult to disentangle the influence of the stimuli from the intrinsic dynamics.
Revised:  When a neuronal network is submitted to external stimuli, the statistics of spikes changes, and it is difficult to disentangle the influence of the stimuli from the intrinsic dynamics.
Edit intent: coherence
Original:  Upon some approximations, it reduces to a convolution, allowing to recover a standard computation in computational neuroscience.
Revised:  Upon some approximations, it reduces to a convolution, allowing to recover a standard formulation in computational neuroscience.
Edit intent: clarity
Original:  This relation allows quantifying the influence of a weak amplitude  external stimuli on spatio-temporal spike correlations,  in a general context where the memory in spike dynamics can go arbitrarily far in the past.
Revised:  This relation allows us to predict the influence of a weak amplitude external stimuli on spatio-temporal spike correlations, in a general context where the memory in spike dynamics can go arbitrarily far in the past.
Edit intent: clarity
Original:  This relation allows quantifying the influence of a weak amplitude  external stimuli on spatio-temporal spike correlations,  in a general context where the memory in spike dynamics can go arbitrarily far in the past.
Revised:  This relation allows quantifying the influence of a weak amplitude external stimuli on spatio-temporal spike correlations, in a general context where the memory in spike dynamics can extend arbitrarily far in the past.
Edit intent: clarity
Original:  With this approach, we show how linear response is explicitly related to neuron dynamics with an example, the gIF model, introduced by M. Rudolph and A. Destexhe 91 . This illustrates the effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike statistics  .
Revised:  Using this approach, we show how linear response is explicitly related to neuron dynamics with an example, the gIF model, introduced by M. Rudolph and A. Destexhe 91 . This illustrates the effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike statistics .
Edit intent: fluency