wanyu/IteraTeR_v2 huggingface dataset:
Original:  We also propose an explanation on how SACs across different species can exhibit a large variability in their bursting periods, as observed experimentally  , yet based on a simple, unique, mechanism.
Revised:  We also propose an explanation on how SACs can exhibit a large variability in their bursting periods, as observed experimentally , yet based on a simple, unique, mechanism.
Edit intent: clarity
Original:  As we discuss, these observations at the cellular level , have a deep impact on the retinal waves description.
Revised:  As we discuss, these observations at the cellular level have a deep impact on the retinal waves description.
Edit intent: fluency
Original:  In this paper, we have proposed a 32- layer CNN architecture that is based on Dense Convolutional Network (DenseNet) for JPEG steganalysis.
Revised:  In this paper, we propose a 32- layer CNN architecture that is based on Dense Convolutional Network (DenseNet) for JPEG steganalysis.
Edit intent: coherence
Original:  The proposed CNN architecture can reuse features by concatenating features from all of the previous layers with the same feature-map size .
Revised:  The proposed CNN architecture can reuse features by concatenating all features from the previous layers with the same feature-map size .
Edit intent: clarity
Original:  The shared features and bottleneck layers make the CNN improve the feature propagation and reduce the parameters . Experimental results on the BOSSbase, BOWS2 and ImageNet datasets have showed that the proposed CNN architecture can improve the performance and enhance the robustness.
Revised:  The shared features and bottleneck layers further improve the feature propagation and reduce the parameters . Experimental results on the BOSSbase, BOWS2 and ImageNet datasets have showed that the proposed CNN architecture can improve the performance and enhance the robustness.
Edit intent: clarity
Original:  There are very few structures that support agreement tests, essentially either coming from algebraic low degree tests or from direct product tests (and recently also from high dimensional expanders).
Revised:  There are very few structures that support agreement tests, essentially either coming from algebraic low degree tests or from direct product tests (and recently also from high-dimensional expanders).
Edit intent: fluency
Original:  As a corollary of our main theorem, we show that an ensemble of small graphs on overlapping sets of vertices can be glued together to one global graph assuming they agree with each other on average.
Revised:  As a corollary of our main theorem, it follows that an ensemble of small graphs on overlapping sets of vertices can be glued together to one global graph assuming they agree with each other on average.
Edit intent: clarity
Original:  In this paper, we conduct an in-depth experimental study comparing different notions of optimality based on a variety of metrics like cardinality, number of rank-1 edges, time taken to compute the matching , to name a few.
Revised:  In this paper, we conduct an in-depth experimental study comparing different notions of optimality based on a variety of metrics like cardinality, number of rank-1 edges, popularity , to name a few.
Edit intent: clarity
Original:  Observing certain shortcomings in the standard notions of optimality, we propose an algorithm which maximizes an alternative metric called the Area under Profile Curve ratio (AUPCR).
Revised:  Observing certain shortcomings in the standard notions of optimality, we propose an algorithm which maximizes an alternative metric called the Area under Profile Curve ratio (AUPCR).
Edit intent: fluency
Original:  To the best of our knowledge, the AUPCR metric was used earlier but there were no known algorithms to compute an AUPCR maximizing matching.
Revised:  To the best of our knowledge, the AUPCR metric was used earlier but there is no known algorithm to compute an AUPCR maximizing matching.
Edit intent: fluency
Original:  We present a novel algorithm that uses exact learning and abstract interpretation to perform efficient extraction of a minimal automaton describing the state dynamics of a given  RNN. We use Angluin's L* algorithm as a learner and the given RNN as an oracle , employing abstract interpretation of the RNN for answering equivalence queries .
Revised:  We present a novel algorithm that uses exact learning and abstract interpretation to perform efficient extraction of a minimal automaton describing the state dynamics of a given RNN. We do this using Angluin's L* algorithm as a learner and the given RNN as an oracle , employing abstract interpretation of the RNN for answering equivalence queries .
Edit intent: clarity
Original:  We present a novel algorithm that uses exact learning and abstract interpretation to perform efficient extraction of a minimal automaton describing the state dynamics of a given  RNN. We use Angluin's L* algorithm as a learner and the given RNN as an oracle , employing abstract interpretation of the RNN for answering equivalence queries .
Revised:  We present a novel algorithm that uses exact learning and abstract interpretation to perform efficient extraction of a minimal automaton describing the state dynamics of a given RNN. We use Angluin's L* algorithm as a learner and the trained RNN as an oracle , employing abstract interpretation of the RNN for answering equivalence queries .
Edit intent: clarity
Original:  We present a novel algorithm that uses exact learning and abstract interpretation to perform efficient extraction of a minimal automaton describing the state dynamics of a given  RNN. We use Angluin's L* algorithm as a learner and the given RNN as an oracle , employing abstract interpretation of the RNN for answering equivalence queries .
Revised:  We present a novel algorithm that uses exact learning and abstract interpretation to perform efficient extraction of a minimal automaton describing the state dynamics of a given RNN. We use Angluin's L* algorithm as a learner and the given RNN as an oracle .
Edit intent: coherence
Original:  Our technique allows automaton-extraction from the RNN while avoiding state explosion , even when the state vectors are large and fine differentiation is required between RNN states.
Revised:  Our technique efficiently extracts accurate automata from trained RNNs , even when the state vectors are large and fine differentiation is required between RNN states.
Edit intent: clarity
Original:  For the best of our knowledge, we present in this work the first verifiable delegation scheme where a classical client delegates her quantum computation to two entangled servers that are allowed to communicate , but respecting the plausible assumption that information cannot be propagated faster than speed of light .
Revised:  For the best of our knowledge, we present in this work the client delegates her quantum computation to two entangled servers that are allowed to communicate , but respecting the plausible assumption that information cannot be propagated faster than speed of light .
Edit intent: clarity
Original:  For the best of our knowledge, we present in this work the first verifiable delegation scheme where a classical client delegates her quantum computation to two entangled servers that are allowed to communicate , but respecting the plausible assumption that information cannot be propagated faster than speed of light .
Revised:  For the best of our knowledge, we present in this work the first verifiable delegation scheme where a classical client delegates her quantum computation to two entangled servers that are allowed to communicate but respecting the plausible assumption that information cannot be propagated faster than speed of light .
Edit intent: fluency
Original: Large scale recordings of neural activity in behaving animals have established that the transformation of sensory stimuli into motor outputs relies on low-dimensional dynamics at the population level, while individual neurons generally exhibit complex , mixed selectivity.
Revised: Large scale neural recordings have established that the transformation of sensory stimuli into motor outputs relies on low-dimensional dynamics at the population level, while individual neurons generally exhibit complex , mixed selectivity.
Edit intent: clarity
Original: Large scale recordings of neural activity in behaving animals have established that the transformation of sensory stimuli into motor outputs relies on low-dimensional dynamics at the population level, while individual neurons generally exhibit complex , mixed selectivity.
Revised: Large scale recordings of neural activity in behaving animals have established that the transformation of sensory stimuli into motor outputs relies on low-dimensional dynamics at the population level, while individual neurons exhibit complex selectivity.
Edit intent: clarity
Original:  Starting from this observation, here we study a new class of recurrent network models in which the connectivity consists of a combination of a random part and a minimal, low dimensional structure.
Revised:  Starting from this observation, here we study a class of recurrent network models in which the connectivity consists of a combination of a random part and a minimal, low dimensional structure.
Edit intent: clarity
Original:  Starting from this observation, here we study a new class of recurrent network models in which the connectivity consists of a combination of a random part and a minimal, low dimensional structure.
Revised:  Starting from this observation, here we study a new class of recurrent network models in which the connectivity is a sum of a random part and a minimal, low dimensional structure.
Edit intent: clarity
Original:  Starting from this observation, here we study a new class of recurrent network models in which the connectivity consists of a combination of a random part and a minimal, low dimensional structure.
Revised:  Starting from this observation, here we study a new class of recurrent network models in which the connectivity consists of a combination of a random part and a minimal, low-dimensional structure.
Edit intent: fluency
Original:  We show that in such low-rank recurrent networks, the dynamics are low-dimensional and can be directly inferred from connectivity using a geometrical approach.
Revised:  We show that , in such networks, the dynamics are low-dimensional and can be directly inferred from connectivity using a geometrical approach.
Edit intent: clarity
Original:  We show that in such low-rank recurrent networks, the dynamics are low-dimensional and can be directly inferred from connectivity using a geometrical approach.
Revised:  We show that in such low-rank recurrent networks, the dynamics are low dimensional and can be directly inferred from connectivity using a geometrical approach.
Edit intent: fluency
Original:  We exploit this understanding to determine minimal connectivity structures required to implement specific computations .
Revised:  We exploit this understanding to determine minimal connectivity required to implement specific computations .
Edit intent: clarity
Original:  We find that the dynamical range and computational capacity of a network quickly increases with the dimensionality of the structure in the connectivity, so that a rank-two structure is already sufficient to implement a complex behavioral task such as context-dependent decision-making .
Revised:  We find that the dynamical range and computational capacity quickly increase with the dimensionality of the structure in the connectivity, so that a rank-two structure is already sufficient to implement a complex behavioral task such as context-dependent decision-making .
Edit intent: clarity
Original: This paperinvestigates on the effect on collective opinions of filtering algorithms managed by social network platforms .
Revised: In this paper, the effect on collective opinions of filtering algorithms managed by social network platforms .
Edit intent: clarity
Original:  For this purpose, a stochastic multi-agent model for opinion dynamics that explicitly accounts for a centralized tuning of the strength of interaction between individuals within a social network is proposed .
Revised:  For this purpose, a stochastic multi-agent model for opinion dynamics is proposed, that accounts for a centralized tuning of the strength of interaction between individuals within a social network is proposed .
Edit intent: clarity
Original:  For this purpose, a stochastic multi-agent model for opinion dynamics that explicitly accounts for a centralized tuning of the strength of interaction between individuals within a social network is proposed .
Revised:  For this purpose, a stochastic multi-agent model for opinion dynamics that explicitly accounts for a centralized tuning of the strength of interaction between individuals .
Edit intent: clarity
Original:  The evolution of each individual opinion is described by a Markov chain, whose transition rates are affected through influence parameters, by the opinions of the neighbors  .
Revised:  The evolution of each individual opinion is described by a Markov chain, whose transition rates are affected by the opinions of the neighbors .
Edit intent: clarity
Original:  The evolution of each individual opinion is described by a Markov chain, whose transition rates are affected through influence parameters, by the opinions of the neighbors  .
Revised:  The evolution of each individual opinion is described by a Markov chain, whose transition rates are affected through influence parameters, by the opinions of the neighbors through influence parameters .
Edit intent: clarity
Original:  Under the assumption of identical agents and unbiased influence, the model can be marginalized, thus implying, among other things, that the influence intensity affects the variance, but not the expectation, of the number of individuals sharing a certain opinion.
Revised:  Under the assumption of identical agents and unbiased influence, it is shown that the influence intensity affects the variance, but not the expectation, of the number of individuals sharing a certain opinion.
Edit intent: clarity
Original:  Moreover, a detailed analysis is carried out in the case of the so-called Peer Assembly, which describes the evolution of binary opinions among identical agents whose interaction of graph is completely connected .
Revised:  Moreover, a detailed analysis is carried out for the so-called Peer Assembly, which describes the evolution of binary opinions among identical agents whose interaction of graph is completely connected .
Edit intent: clarity
Original:  Moreover, a detailed analysis is carried out in the case of the so-called Peer Assembly, which describes the evolution of binary opinions among identical agents whose interaction of graph is completely connected .
Revised:  Moreover, a detailed analysis is carried out in the case of the so-called Peer Assembly, which describes the evolution of binary opinions in a completely connected graph of identical agents .
Edit intent: clarity
Original:  Both analytical results and simulation experiments are used to highlight the emergence of particular collective behaviours,  depending on the interaction strength parameters.
Revised:  Both analytical results and simulation experiments are used to highlight the emergence of particular collective behaviours, depending on the centralized tuning of the influence parameters.
Edit intent: clarity
Original:  This leads to failures of end to end paths between nodes , and thus makes the routing decisions a challenge for the community . Named Data Networking (NDN) is a network paradigm that deals with such problems, since information is forwarded based on content and not on the location of the hosts.
Revised:  This leads to failures of end-to-end paths between nodes , and thus makes the routing decisions a challenge for the community . Named Data Networking (NDN) is a network paradigm that deals with such problems, since information is forwarded based on content and not on the location of the hosts.
Edit intent: fluency
Original:  This leads to failures of end to end paths between nodes , and thus makes the routing decisions a challenge for the community . Named Data Networking (NDN) is a network paradigm that deals with such problems, since information is forwarded based on content and not on the location of the hosts.
Revised:  This leads to failures of end to end paths between nodes . Named Data Networking (NDN) is a network paradigm that deals with such problems, since information is forwarded based on content and not on the location of the hosts.
Edit intent: coherence
Original:  We propose a topology-oblivious multihop, multipath and multichannel VANET routing strategy that exploits several paths to achieve faster content retrieval.
Revised:  We propose a topology-oblivious multihop, multipath and multichannel VANET routing strategy that exploits several paths to achieve more efficient content retrieval.
Edit intent: clarity
Original:  We minimize the broadcasting nature of Wi-Fi by adding new fields to Interest and Data messages ( MMM-VNDN ) and also, we deployed a second algorithm that performs the forwarding decisions in the strategy layer of NDN (iMMM-VNDN) . Simulation results show that our approach does not only guarantee faster data delivery, but also saves network resources, due to reduced message retransmissions .
Revised:  We minimize the broadcasting nature of Wi-Fi by adding new fields to Interest and Data messages ( MMM-VNDN ) and also, we deployed a second algorithm that performs the forwarding decisions in the strategy layer of an NDN node . Simulation results show that our approach does not only guarantee faster data delivery, but also saves network resources, due to reduced message retransmissions .
Edit intent: clarity
Original:  We minimize the broadcasting nature of Wi-Fi by adding new fields to Interest and Data messages ( MMM-VNDN ) and also, we deployed a second algorithm that performs the forwarding decisions in the strategy layer of NDN (iMMM-VNDN) . Simulation results show that our approach does not only guarantee faster data delivery, but also saves network resources, due to reduced message retransmissions .
Revised:  We minimize the broadcasting nature of Wi-Fi by adding new fields to Interest and Data messages ( MMM-VNDN ) and also, we deployed a second algorithm that performs the forwarding decisions in the strategy layer of NDN (iMMM-VNDN) . Simulation results show that our routing strategy performs better than other state of the art strategies in terms of Interest Satisfaction Rate, while keeping the latency and jitter of messages low .
Edit intent: clarity
Original:  We used both resting state and task-based fMRI data, altogether 10 datasets containing 780 scans corresponding to different scanning sequences and different subject populations.
Revised:  We used both resting state and task-based fMRI data, altogether ten datasets containing 780 scans corresponding to different scanning sequences and different subject populations.
Edit intent: fluency
Original:  We used both resting state and task-based fMRI data, altogether 10 datasets containing 780 scans corresponding to different scanning sequences and different subject populations.
Revised:  We used both resting state and task-based fMRI data, altogether 10 datasets containing 780 scans corresponding to different scanning sequences and subject populations.
Edit intent: fluency
Original:  In analyses of each fMRI scan we considered different assumed experimental designs , as well as different spatial smoothing levelsand different detrending options .
Revised:  In analyses of each fMRI scan we considered different assumed experimental designs and smoothing levels .
Edit intent: clarity
Original:  For data used as null data the use of FSL and SPM resulted in much higher false positive rates than the use of AFNI.
Revised:  For data with no expected experimentally-induced activation, FSL and SPM resulted in much higher false positive rates than the use of AFNI.
Edit intent: clarity
Original:  For one dataset we observed a big loss of sensitivity when SPM was used.
Revised:  As a result, for one task-based dataset we observed a big loss of sensitivity when SPM was used.
Edit intent: clarity
Original:  For one dataset we observed a big loss of sensitivity when SPM was used.
Revised:  For one dataset we observed a large loss of sensitivity when SPM was used.
Edit intent: clarity
Original:  We used both resting state and task-based fMRI data, altogether ten datasets containing 780 scans corresponding to different scanning sequences and subject populations.
Revised:  We used both resting state and task-based fMRI data, altogether ten datasets containing 780 scans corresponding to different fMRI protocols and subject populations.
Edit intent: clarity
Original:  We employed eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations.
Revised:  We employ eleven datasets containing 980 scans corresponding to different fMRI protocols and subject populations.
Edit intent: fluency
Original:  Though autocorrelation modeling in AFNI was not perfect, its performance was much higher than the performance of autocorrelation modeling in FSL and SPM.
Revised:  Though autocorrelation modeling in AFNI is not perfect, its performance was much higher than the performance of autocorrelation modeling in FSL and SPM.
Edit intent: fluency
Original:  Though autocorrelation modeling in AFNI was not perfect, its performance was much higher than the performance of autocorrelation modeling in FSL and SPM.
Revised:  Though autocorrelation modeling in AFNI was not perfect, its performance is much higher than the performance of autocorrelation modeling in FSL and SPM.
Edit intent: fluency
Original:  The residual autocorrelated noise in FSL and SPM led to heavily confounded first level results, particularly for low-frequency experimental designs.
Revised:  The residual autocorrelated noise in FSL and SPM leads to heavily confounded first level results, particularly for low-frequency experimental designs.
Edit intent: fluency
Original:  Our results show superior performance of SPM's alternative pre-whitening: FAST, over the default SPM's method .
Revised:  Our results show superior performance of SPM's alternative pre-whitening: FAST, over SPM's method .
Edit intent: clarity
Original:  Our results show superior performance of SPM's alternative pre-whitening: FAST, over the default SPM's method .
Revised:  Our results show superior performance of SPM's alternative pre-whitening: FAST, over the default SPM's default .
Edit intent: clarity
Original:  Furthermore, reliability could increase if the analysis packages provided diagnostic plots.
Revised:  Furthermore, reliability could increase if the packages provided diagnostic plots.
Edit intent: clarity
Original:  We provide a MATLAB script for the fMRI researchers to check if their analyses might be affected by imperfect pre-whitening  .
Revised:  We provide a MATLAB script for the fMRI researchers to check if their analyses might be affected by imperfect pre-whitening problems .
Edit intent: clarity
Original:  Assuming only that the growth dynamics is , to first order, captured by proportional growth, we describe the system with fitness-adjusted preferential attachment, where the idiosyncratic system dynamics is conveniently absorbed into the fitness term.
Revised:  Assuming that the growth dynamics is , to first order, captured by proportional growth, we describe the system with fitness-adjusted preferential attachment, where the idiosyncratic system dynamics is conveniently absorbed into the fitness term.
Edit intent: clarity
Original:  Assuming only that the growth dynamics is , to first order, captured by proportional growth, we describe the system with fitness-adjusted preferential attachment, where the idiosyncratic system dynamics is conveniently absorbed into the fitness term.
Revised:  Assuming only that the growth dynamics is captured by proportional growth, we describe the system with fitness-adjusted preferential attachment, where the idiosyncratic system dynamics is conveniently absorbed into the fitness term.
Edit intent: coherence