wanyu/IteraTeR_v2 huggingface dataset:
Original:  The network is fully connected, and both excitatory and inhibitory interactions are strong, of magnitude N^{-1/2 inhibitory  feedback strongly reduces the input correlation, and the output correlation is positive and of order N^{-1/2 . The result agrees with recent numerical studies, and also with anatomical and physiological measurements in the cortex: a cortical neuron receives about N \simeq 10^4 connections, and the measured pairwise correlations are of order 10^{-2}.
Revised:  The network is fully connected, and both excitatory and inhibitory interactions are strong, of magnitude N^{-1/2 inhibitory feedback strongly reduces the input correlation, and the output correlation is positive and of magnitude K^{-1/2 . The result agrees with recent numerical studies, and also with anatomical and physiological measurements in the cortex: a cortical neuron receives about N \simeq 10^4 connections, and the measured pairwise correlations are of order 10^{-2}.
Edit intent: clarity
Original:  The network is fully connected, and both excitatory and inhibitory interactions are strong, of magnitude N^{-1/2 inhibitory  feedback strongly reduces the input correlation, and the output correlation is positive and of order N^{-1/2 . The result agrees with recent numerical studies, and also with anatomical and physiological measurements in the cortex: a cortical neuron receives about N \simeq 10^4 connections, and the measured pairwise correlations are of order 10^{-2}.
Revised:  The network is fully connected, and both excitatory and inhibitory interactions are strong, of magnitude N^{-1/2 inhibitory feedback strongly reduces the input correlation, and the output correlation is positive and of order N^{-1/2 . The result agrees with anatomical and physiological measurements in the cortex: a cortical neuron receives about N \simeq 10^4 connections, and the measured pairwise correlations are of order 10^{-2}.
Edit intent: clarity
Original:  The network is fully connected, and both excitatory and inhibitory interactions are strong, of magnitude N^{-1/2 inhibitory  feedback strongly reduces the input correlation, and the output correlation is positive and of order N^{-1/2 . The result agrees with recent numerical studies, and also with anatomical and physiological measurements in the cortex: a cortical neuron receives about N \simeq 10^4 connections, and the measured pairwise correlations are of order 10^{-2}.
Revised:  The network is fully connected, and both excitatory and inhibitory interactions are strong, of magnitude N^{-1/2 inhibitory feedback strongly reduces the input correlation, and the output correlation is positive and of order N^{-1/2 . The result agrees with recent numerical studies, and also with anatomical and physiological measurements in the cortex: a cortical neuron receives about N \simeq 10^4 connections, and the measured correlations are about 10^{-2}.
Edit intent: clarity
Original: The activity of cortical neurons is correlated, and this correlation affects how the brain processes information.
Revised: The activity of neurons is correlated, and this correlation affects how the brain processes information.
Edit intent: clarity
Original:  We consider strong excitatory and inhibitory connections: the excitatory input drives the fluctuations of neural activity, which are counterbalanced by the inhibitory feedback.
Revised:  We consider strong excitatory and inhibitory connections: the excitatory input drives the fluctuations of neural activity, which are counterbalanced by inhibitory feedback.
Edit intent: fluency
Original:  While the strong excitatory input tends to correlate neurons, the inhibitory feedback strongly reduces the correlations.
Revised:  In particular, excitatory input tends to correlate neurons, the inhibitory feedback strongly reduces the correlations.
Edit intent: clarity
Original:  Crucially, the heterogeneity of synaptic connections is necessary for the inhibition of correlations.
Revised:  Crucially, the heterogeneity of synaptic connections is necessary for this inhibition of correlations.
Edit intent: fluency
Original:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0  \ 0 & \pm z^*_j pmatrix } or { pmatrix \pm z^*_j & 0  \ 0 & \pm z_j pmatrix }.
Revised:  Define O_z an M-type COD if and only if O_z does not contain submatrix { \pm z_j & 0 \ 0 & \pm z^*_j pmatrix } or { pmatrix \pm z^*_j & 0 \ 0 & \pm z_j pmatrix }.
Edit intent: fluency
Original:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0  \ 0 & \pm z^*_j pmatrix } or { pmatrix \pm z^*_j & 0  \ 0 & \pm z_j pmatrix }.
Revised:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0 ; \ 0 & \pm z^*_j pmatrix } or { pmatrix \pm z^*_j & 0 \ 0 & \pm z_j pmatrix }.
Edit intent: fluency
Original:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0  \ 0 & \pm z^*_j pmatrix } or { pmatrix \pm z^*_j & 0  \ 0 & \pm z_j pmatrix }.
Revised:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0 \ 0 & \pm z^*_j } or { pmatrix \pm z^*_j & 0 \ 0 & \pm z_j pmatrix }.
Edit intent: fluency
Original:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0  \ 0 & \pm z^*_j pmatrix } or { pmatrix \pm z^*_j & 0  \ 0 & \pm z_j pmatrix }.
Revised:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0 \ 0 & \pm z^*_j pmatrix } or { \pm z^*_j & 0 \ 0 & \pm z_j pmatrix }.
Edit intent: fluency
Original:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0  \ 0 & \pm z^*_j pmatrix } or { pmatrix \pm z^*_j & 0  \ 0 & \pm z_j pmatrix }.
Revised:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0 \ 0 & \pm z^*_j pmatrix } or { pmatrix \pm z^*_j & 0 ; \ 0 & \pm z_j pmatrix }.
Edit intent: fluency
Original:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0  \ 0 & \pm z^*_j pmatrix } or { pmatrix \pm z^*_j & 0  \ 0 & \pm z_j pmatrix }.
Revised:  Define O_z an M-type COD if and only if O_z does not contain submatrix { pmatrix \pm z_j & 0 \ 0 & \pm z^*_j pmatrix } or { pmatrix \pm z^*_j & 0 \ 0 & \pm z_j }.
Edit intent: fluency
Original:  New M-type CODs with parameters [p,n,k]=[n{w-1}+n{w+1}, n, \binom{n}{w}], for 0 \le w \le n, are constructed, which demonstrate the possibility of sacrificing code rate to reduce decoding delay.
Revised:  New CODs with parameters [p,n,k]=[n{w-1}+n{w+1}, n, \binom{n}{w}], for 0 \le w \le n, are constructed, which demonstrate the possibility of sacrificing code rate to reduce decoding delay.
Edit intent: clarity
Original:  It's worth mentioning that all maximal rate, minimal delay CODs are contained in our constructions, and their uniqueness under equivalence operation of all optimal CODs is proved.
Revised:  It's worth mentioning that all maximal rate, minimal delay CODs are contained in our constructions, and their uniqueness under equivalence operation is proved.
Edit intent: clarity
Original:  It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual interfaces .
Revised:  It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites .
Edit intent: clarity
Original: A single inhibitory neuron with delayed feedback is considered  .
Revised: Activity of inhibitory neuron with delayed feedback is considered .
Edit intent: clarity
Original:  The neuron receives an excitatory input from the Poisson stream, and inhibitory impulses from the feedback line with  delay.
Revised:  The neuron receives excitatory input impulses from a Poisson stream, and inhibitory impulses from the feedback line with delay.
Edit intent: fluency
Original:  The neuron receives an excitatory input from the Poisson stream, and inhibitory impulses from the feedback line with  delay.
Revised:  The neuron receives an excitatory input from the Poisson stream, and inhibitory impulses from the feedback line with a delay.
Edit intent: fluency
Original:  We investigate here, how does the feedback presence affect the output firing statistics of inhibitory neuron .
Revised:  We investigate here, how does the presence of inhibitory feedback affect the output firing statistics of inhibitory neuron .
Edit intent: clarity
Original:  We investigate here, how does the feedback presence affect the output firing statistics of inhibitory neuron .
Revised:  We investigate here, how does the feedback presence affect the output firing statistics .
Edit intent: clarity
Original:  Using binding neuron (BN) as a model, we derive exact analytical expressions for the output interspike intervals (ISI) probability density, mean output ISI and coefficient of variation as functions of model's parameters for the case of threshold 2. Using  leaky integrate-and-fire (LIF) model, as well as  BN model with higher thresholds, these statistical quantities are found numerically.
Revised:  Using binding neuron (BN) as a model, we derive analytically the exact expressions for the output interspike intervals (ISI) probability density, mean output ISI and coefficient of variation as functions of model's parameters for the case of threshold 2. Using leaky integrate-and-fire (LIF) model, as well as BN model with higher thresholds, these statistical quantities are found numerically.
Edit intent: fluency
Original:  Using binding neuron (BN) as a model, we derive exact analytical expressions for the output interspike intervals (ISI) probability density, mean output ISI and coefficient of variation as functions of model's parameters for the case of threshold 2. Using  leaky integrate-and-fire (LIF) model, as well as  BN model with higher thresholds, these statistical quantities are found numerically.
Revised:  Using binding neuron (BN) as a model, we derive exact analytical expressions for the output interspike intervals (ISI) probability density, mean output ISI and coefficient of variation as functions of model's parameters for the case of threshold 2. Using the leaky integrate-and-fire (LIF) model, as well as BN model with higher thresholds, these statistical quantities are found numerically.
Edit intent: fluency
Original:  Using binding neuron (BN) as a model, we derive exact analytical expressions for the output interspike intervals (ISI) probability density, mean output ISI and coefficient of variation as functions of model's parameters for the case of threshold 2. Using  leaky integrate-and-fire (LIF) model, as well as  BN model with higher thresholds, these statistical quantities are found numerically.
Revised:  Using binding neuron (BN) as a model, we derive exact analytical expressions for the output interspike intervals (ISI) probability density, mean output ISI and coefficient of variation as functions of model's parameters for the case of threshold 2. Using leaky integrate-and-fire (LIF) model, as well as the BN model with higher thresholds, these statistical quantities are found numerically.
Edit intent: fluency
Original:  Nevertheless, the delayed feedback presence was not found to affect substantially the output ISI coefficient of variation.
Revised:  Nevertheless, the presence of inhibitory delayed feedback was not found to affect substantially the output ISI coefficient of variation.
Edit intent: clarity
Original:  It is concluded that introduction of delayed  feedback can radically change neuronal output firing statistics of inhibitory neuron .
Revised:  It is concluded that introduction of delayed inhibitory feedback can radically change neuronal output firing statistics of inhibitory neuron .
Edit intent: fluency
Original:  It is concluded that introduction of delayed  feedback can radically change neuronal output firing statistics of inhibitory neuron .
Revised:  It is concluded that introduction of delayed feedback can radically change neuronal output firing statistics .
Edit intent: clarity
Original:  This statistics is as well distinct of what was found previously   for excitatory neuron with delayed feedback.
Revised:  This statistics is as well distinct from what was found previously for excitatory neuron with delayed feedback.
Edit intent: fluency
Original:  In this note we answer some open questions from that paper by showing that the dependence of the ratio function upon the parameter takes the form of a Devil's staircase.
Revised:  In this paper we answer some open questions from that paper by showing that the dependence of the ratio function upon the parameter takes the form of a Devil's staircase.
Edit intent: fluency
Original: We study the problem  of index deployment ordering .
Revised: Query workloads and database schemas in OLAP applications are becoming increasingly complex. Moreover, the queries and the schemas have to continually of index deployment ordering .
Edit intent: coherence
Original: We study the problem  of index deployment ordering .
Revised: We study the problemevolve of index deployment ordering .
Edit intent: clarity
Original: We study the problem  of index deployment ordering .
Revised: We study the problem of index deployment .
Edit intent: clarity
Original: Low-latency anonymous networks like Tor are subject to selective denial-of-service (DoS) attacks .
Revised: Low-latency anonymity networks like Tor are subject to selective denial-of-service (DoS) attacks .
Edit intent: fluency
Original: Low-latency anonymous networks like Tor are subject to selective denial-of-service (DoS) attacks .
Revised: Low-latency anonymous networks like Tor are subject to selective denial-of-service (DoS) attack .
Edit intent: fluency
Original:  Selective-DoS attacks lowers anonymity as it forces paths to be rebuilt multiple times to ensure delivery which increases the opportunity for more attack.
Revised:  Selective-DoS attack lowers anonymity as it forces paths to be rebuilt multiple times to ensure delivery which increases the opportunity for more attack.
Edit intent: fluency
Original:  Our detection algorithm uses two levels of probing to filter out potential compromised tunnels.
Revised:  Our detection algorithm uses two levels of probing to filter out potentially compromised tunnels.
Edit intent: fluency
Original:  Real world experiments reveal that our detection algorithm provides good defence against selective DoS attack.
Revised:  Real world experiments reveal that our detection algorithm provides good defense against selective-DoS attack.
Edit intent: fluency
Original: Low-latency anonymity networks like Tor are subject to selective denial-of-service (DoS) attack.
Revised: Anonymous communication systems are subject to selective denial-of-service (DoS) attack.
Edit intent: clarity
Original:  In this paper we present a detection algorithm which filters out compromised tunnels from a set of Tor tunnels to ensure better anonymity .
Revised:  In this paper we present a detection algorithm that filters out compromised tunnels from a set of Tor tunnels to ensure better anonymity .
Edit intent: fluency
Original:  In this paper we present a detection algorithm which filters out compromised tunnels from a set of Tor tunnels to ensure better anonymity .
Revised:  In this paper we present a detection algorithm which filters out compromised communication channels for one of the most widely used anonymity networks, Tor .
Edit intent: clarity
Original:  We also analyze the cost of our algorithm and show a tradeoff between security and communication overhead . Real world experiments reveal that our detection algorithm provides good defense against selective-DoS attack.
Revised:  We also analyze the overhead of our detection algorithm and show a tradeoff between security and communication overhead . Real world experiments reveal that our detection algorithm provides good defense against selective-DoS attack.
Edit intent: clarity
Original:  We also analyze the cost of our algorithm and show a tradeoff between security and communication overhead . Real world experiments reveal that our detection algorithm provides good defense against selective-DoS attack.
Revised:  We also analyze the cost of our algorithm and show a tradeoff between security and communication overhead . Real world experiments reveal that our detection algorithm provides good defense against selective DoS attack.
Edit intent: fluency
Original:  Furthermore, it is proved here that only a finite number of terms in the asymptotic expansion is needed to recover (via the method of Pad\'e approximants) the exact two-locus sampling distribution as an analytic function of \rho;
Revised:  Furthermore, it is proved here that only a finite number of terms in the asymptotic expansion is needed to recover (via the method of Pad\'{e approximants) the exact two-locus sampling distribution as an analytic function of \rho;
Edit intent: fluency
Original:  We also discuss the fact that the permanent is lower bounded by the Bethe permanent, and we comment on potential upper bounds on the permanent based on the Bethe permanent.
Revised:  We then discuss the fact that the permanent is lower bounded by the Bethe permanent, and we comment on potential upper bounds on the permanent based on the Bethe permanent.
Edit intent: clarity
Original:  Moreover, we comment on possibilities to modify the Bethe permanent so that it approximates the permanent even better, and we conclude the paper with some  conjectures about permanent-based pseudo-codewords and permanent-based kernels.
Revised:  Moreover, we comment on possibilities to modify the Bethe permanent so that it approximates the permanent even better, and we conclude the paper with some observations and conjectures about permanent-based pseudo-codewords and permanent-based kernels.
Edit intent: clarity
Original:  Competitive interactions between the individuals are considered in three different situations: a ) no interaction, b) global interaction in which birth and death rates are influenced by all individuals in the system, and c) reproduction and death rates of an individual depend on the number of individuals in a neighborhood ( finite-range nonlocal interaction).
Revised:  Competitive interactions are considered in three different situations: a ) no interaction, b) global interaction in which birth and death rates are influenced by all individuals in the system, and c) reproduction and death rates of an individual depend on the number of individuals in a neighborhood ( finite-range nonlocal interaction).
Edit intent: clarity
Original:  Competitive interactions between the individuals are considered in three different situations: a ) no interaction, b) global interaction in which birth and death rates are influenced by all individuals in the system, and c) reproduction and death rates of an individual depend on the number of individuals in a neighborhood ( finite-range nonlocal interaction).
Revised:  Competitive interactions between the individuals are considered in a global case, in which birth and death rates are influenced by all individuals in the system, and c) reproduction and death rates of an individual depend on the number of individuals in a neighborhood ( finite-range nonlocal interaction).
Edit intent: clarity
Original:  Competitive interactions between the individuals are considered in three different situations: a ) no interaction, b) global interaction in which birth and death rates are influenced by all individuals in the system, and c) reproduction and death rates of an individual depend on the number of individuals in a neighborhood ( finite-range nonlocal interaction).
Revised:  Competitive interactions between the individuals are considered in three different situations: a ) no interaction, b) global interaction in which birth and death rates are influenced by all individuals in the system, and in a nonlocal but finite-range case in which interaction affects individuals in a neighborhood ( finite-range nonlocal interaction).
Edit intent: clarity
Original:  In the former one single or few-cluster configurations are achieved with the spatial distribution of the particles tied to the type of diffusion.
Revised:  In the former one single or few-cluster configurations are achieved with the spatial distribution of the bugs tied to the type of diffusion.
Edit intent: clarity
Original:  In the Levy case long tails arise in some properties characterizing the shape and dynamics of clusters , which turn to be much short-ranged under Brownian diffusion .
Revised:  In the L\'{e case long tails arise in some properties characterizing the shape and dynamics of clusters , which turn to be much short-ranged under Brownian diffusion .
Edit intent: fluency
Original:  In the Levy case long tails arise in some properties characterizing the shape and dynamics of clusters , which turn to be much short-ranged under Brownian diffusion .
Revised:  In the Levy case long tails appear for some properties characterizing the shape and dynamics of clusters , which turn to be much short-ranged under Brownian diffusion .
Edit intent: clarity
Original:  In the Levy case long tails arise in some properties characterizing the shape and dynamics of clusters , which turn to be much short-ranged under Brownian diffusion .
Revised:  In the Levy case long tails arise in some properties characterizing the shape and dynamics of clusters .
Edit intent: clarity
Original:  This length acts as a cut-off limiting the influence of the long Levy jumps, so that spatial configurations under both types of diffusion are now more similar.
Revised:  This length acts as a cut-off limiting the influence of the long Levy jumps, so that spatial configurations under the two types of diffusion are now more similar.
Edit intent: fluency
Original:  This length acts as a cut-off limiting the influence of the long Levy jumps, so that spatial configurations under both types of diffusion are now more similar.
Revised:  This length acts as a cut-off limiting the influence of the long Levy jumps, so that spatial configurations under both types of diffusion become more similar.
Edit intent: clarity
Original:  The process of family mixing is also considered .
Revised:  By dividing initially everyone into different families and following their descent it is possible to show that mixing of families and their competition is greatly influenced by the spatial dynamics .
Edit intent: coherence
Original: n the given paper we consider finite difference approximations to systems of polynomially-nonlinear partial differential equations whose coefficients are rational functions over rationals in the independent variables.
Revised: In the given paper we consider finite difference approximations to systems of polynomially-nonlinear partial differential equations whose coefficients are rational functions over rationals in the independent variables.
Edit intent: fluency
Original:  Noise in cellular physiology has been found to play an important role in many fundamental cellular processes, including transcription, translation, stem cell differentiation and response to medication .
Revised:  Noise in cellular physiology plays an important role in many fundamental cellular processes, including transcription, translation, stem cell differentiation and response to medication .
Edit intent: clarity
Original:  We expose a knitting of cell cycle variability, transcription rate variation and mitochondrial variation.
Edit intent: coherence
Original:  We also explore two recent regulatory network-based models for stem cell differentiation, and find that extrinsic noise in transcription rate causes significant variability in the behaviour of these model systems.
Revised:  We also explore two recent regulatory network-based models for stem cell differentiation, and find that extrinsic noise in transcription rate causes appreciable variability in the behaviour of these model systems.
Edit intent: clarity