wanyu/IteraTeR_v2 huggingface dataset:
Original:  The described approach to quantify the evolution of ideas may be relevant in making predictions about the future of science and how to guide its development.
Revised:  The described approach to quantify the evolution of ideas may be relevant in making predictions about the future of science and thus help to guide its development.
Edit intent: clarity
Original:  The communities we identify map to known scientific fields, and their age strongly depends on their size and activity.
Revised:  The communities we identify map to known scientific fields, and their age depends on their size and activity.
Edit intent: clarity
Original:  We discuss the applications in the neuroscience and the relations to the generalised linear model used for the analysis of spike trains  .
Revised:  We discuss the relations to a class of generalised linear model used for the analysis of spike trains .
Edit intent: clarity
Original:  The expected intensities of the process are approximated by a differential system of first-order and the stability of the solutions of this equation is studied.
Revised:  The expected rates of all neurons in the network are approximated by a differential system of first-order and the stability of the solutions of this equation is studied.
Edit intent: clarity
Original:  The expected intensities of the process are approximated by a differential system of first-order and the stability of the solutions of this equation is studied.
Revised:  The expected intensities of the process are approximated by a first-order differential system. We study the stability of the solutions of this equation is studied.
Edit intent: coherence
Original:  We discuss the relations to a class of generalised linear model used for the analysis of spike trains and we show how to use this formalism to implement a robust winner-takes-all network that operates on a wide range of parameters  .
Revised:  We discuss the relations to a class of generalised linear model used for the analysis of spike trains and we show how to use this formalism to implement a winner-takes-all network that operates on a wide range of parameters .
Edit intent: clarity
Original:  We discuss the relations to a class of generalised linear model used for the analysis of spike trains and we show how to use this formalism to implement a robust winner-takes-all network that operates on a wide range of parameters  .
Revised:  We discuss the relations to a class of generalised linear model used for the analysis of spike trains and we show how to use this formalism to implement a robust winner-takes-all network that operates robustly for a wide range of parameters .
Edit intent: clarity
Original:  We use a Boolean model for the regulation dynamics and comment on the impact of various Boolean function classes that have been suggested to better represent in vivo regulatory interactions.
Revised:  We use a Boolean model of regulatory dynamics with various classes of update functions to represent in vivo regulatory interactions.
Edit intent: clarity
Original:  We further investigate the robustness of the networks under minor perturbations.
Revised:  Finally, we investigate the robustness of the networks under minor perturbations.
Edit intent: coherence
Original:  The  difference between the model and the Yeast in terms of stability is marginal, which is consistent with the type of statistically outlier motifs found in the core .
Revised:  The difference between the model and the Yeast 's core is marginal, which is consistent with the type of statistically outlier motifs found in the core .
Edit intent: clarity
Original:  In both regimes, the full system dynamical complexity is trivial compared to its apparent structural complexity.
Revised:  In both regimes, the dynamical complexity of the full system is trivial compared to its apparent structural complexity.
Edit intent: clarity
Original:  Similar simplicity will arise in the dynamics of other complex biochemical processes.
Revised:  Similar simplicity is likely to arise in the dynamics of other complex biochemical processes.
Edit intent: clarity
Original:  We consider an economy of n firms which may default directly or may be infected by another defaulting firm (a domino effect being also possible).
Revised:  We consider an economy of n firms which may default directly or may be infected by other defaulting firms (a domino effect being also possible).
Edit intent: fluency
Original:  The spontaneous default without external influence and the infections are described by not necessary independent Bernoulli-type random variables.
Revised:  The spontaneous default without external influence and the infections are described by not necessarily independent Bernoulli-type random variables.
Edit intent: fluency
Original:  Moreover, several contaminations could be necessary to infect another firm.
Revised:  Moreover, several contaminations could be required to infect another firm.
Edit intent: fluency
Original: The absence of arbitrages of the first kind, a weakening of the " No Free Lunch with Vanishing Risk " condition, is analyzed in a general semimartingale financial market model.
Revised: The absence of arbitrages of the first kind, a weakening of the `` No Free Lunch with Vanishing Risk " condition, is analyzed in a general semimartingale financial market model.
Edit intent: fluency
Original: The absence of arbitrages of the first kind, a weakening of the " No Free Lunch with Vanishing Risk " condition, is analyzed in a general semimartingale financial market model.
Revised: The absence of arbitrages of the first kind, a weakening of the " No Free Lunch with Vanishing Risk '' condition, is analyzed in a general semimartingale financial market model.
Edit intent: fluency
Original: The absence of arbitrages of the first kind, a weakening of the ``No Free Lunch with Vanishing Risk'' condition, is analyzed in a general semimartingale financial market model .
Revised: In a semimartingale financial market model .
Edit intent: coherence
Original: We propose a new class of stochastic dynamical models of neural networks with complex architectures enabling us to describe activation of neural networks by an external stimulation, pacemakers or due to spontaneous activity.
Revised: We propose a stochastic dynamical model of neural networks with complex architectures enabling us to describe activation of neural networks by an external stimulation, pacemakers or due to spontaneous activity.
Edit intent: clarity
Original: We propose a new class of stochastic dynamical models of neural networks with complex architectures enabling us to describe activation of neural networks by an external stimulation, pacemakers or due to spontaneous activity.
Revised: We propose a new class of stochastic dynamical models of neural networks with complex architectures . We discuss activation of neural networks by an external stimulation, pacemakers or due to spontaneous activity.
Edit intent: coherence
Original: We propose a new class of stochastic dynamical models of neural networks with complex architectures enabling us to describe activation of neural networks by an external stimulation, pacemakers or due to spontaneous activity.
Revised: We propose a new class of stochastic dynamical models of neural networks with complex architectures enabling us to describe activation of neural networks by a stimulus, pacemakers and spontaneous activity.
Edit intent: clarity
Original:  These models have a complex phase diagram with self-organized active neural states, hybrid phase transitions, and a rich array of activities including decaying and stable oscillations, stochastic resonance, and avalanches.
Revised:  This model has a complex phase diagram with self-organized active neural states, hybrid phase transitions, and a rich array of activities including decaying and stable oscillations, stochastic resonance, and avalanches.
Edit intent: fluency
Original:  We find that even networks of 50 neurons reveal oscillations remarkably robust against fluctuations which are generally strong in small systems .
Revised:  We find that even networks of 50 neurons .
Edit intent: clarity
Original:  This model has a complex phase diagram with self-organized active neural states, hybrid phase transitions, and a rich array of activities .
Revised:  This model has a complex phase diagram with self-organized active neural states, hybrid phase transitions, and a rich array of behavior .
Edit intent: clarity
Original:  Our main result is a {\em  polynomial time approximation scheme (PTAS) for this problem on UDG. In fact, we present a robust algorithm that given a graph G (not necessarily UDG) with edge-lengths, it either (i) computes a clique partition or (ii) gives a certificate that the graph is not a UDG;
Revised:  Our main result is a {\em polynomial time approximation scheme (PTAS) for UDGs expressed with edge-lengths, it either (i) computes a clique partition or (ii) gives a certificate that the graph is not a UDG;
Edit intent: clarity
Original:  Our main technical contribution involves showing the property of%DIFDELCMD < {\em %%% separability of an optimal clique partition;
Revised:  Our %DIFDELCMD < {\em %%% separability of an optimal clique partition;
Edit intent: clarity
Original:  Our main technical contribution involves showing the property of%DIFDELCMD < {\em %%% separability of an optimal clique partition;
Revised:  Our main technical contribution involves showing the property of%DIFDELCMD < {\em %%% of an optimal clique partition;
Edit intent: clarity
Original:  Our algorithm can be transformed into an O( \frac{\log^*n{\eps^{O(1)}}} {\eps^{O(1)}}} ) time distributed polynomial-time approximation scheme (PTAS).
Revised:  Our algorithm can be transformed into an O( \frac{\log^*n{\eps^{O(1)}}} \frac{\log^* n{\eps^{O(1)}}} ) time distributed polynomial-time approximation scheme (PTAS).
Edit intent: fluency
Original: Protein-surface interactions cause the desirable effect of controlled protein deposition onto biodevices as well as the undesirable effect of protein-surface fouling.
Revised: Protein-surface interactions cause the desirable effect of controlled protein adsorption onto biodevices as well as the undesirable effect of protein-surface fouling.
Edit intent: clarity
Original: Protein-surface interactions cause the desirable effect of controlled protein deposition onto biodevices as well as the undesirable effect of protein-surface fouling.
Revised: Protein-surface interactions cause the desirable effect of controlled protein deposition onto biodevices as well as the undesirable effect of protein fouling.
Edit intent: fluency
Original:  The key to controlling protein-surface adsorption is to identify two distinct types of forces responsible for the adsorption: extrinsic protein deposition-associated forces and intrinsic protein-surface interaction forces . Bulk measurements cannot reveal the dynamic protein-surface binding pathways and thus cannot differentiate between extrinsic and intrinsic forces .
Revised:  The key to controlling protein-surface adsorptions is to identify two distinct types of forces responsible for the adsorption: extrinsic protein deposition-associated forces and intrinsic protein-surface interaction forces . Bulk measurements cannot reveal the dynamic protein-surface binding pathways and thus cannot differentiate between extrinsic and intrinsic forces .
Edit intent: clarity
Original:  The key to controlling protein-surface adsorption is to identify two distinct types of forces responsible for the adsorption: extrinsic protein deposition-associated forces and intrinsic protein-surface interaction forces . Bulk measurements cannot reveal the dynamic protein-surface binding pathways and thus cannot differentiate between extrinsic and intrinsic forces .
Revised:  The key to controlling protein-surface adsorption is to identify and quantify the main adsorption mechanisms: adsorptions that occur (1) while depositing a protein solution onto dry surfaces and (2) after the deposition . Bulk measurements cannot reveal the dynamic protein-surface binding pathways and thus cannot differentiate between extrinsic and intrinsic forces .
Edit intent: clarity
Original:  The key to controlling protein-surface adsorption is to identify two distinct types of forces responsible for the adsorption: extrinsic protein deposition-associated forces and intrinsic protein-surface interaction forces . Bulk measurements cannot reveal the dynamic protein-surface binding pathways and thus cannot differentiate between extrinsic and intrinsic forces .
Revised:  The key to controlling protein-surface adsorption is to identify two distinct types of forces responsible for the adsorption: extrinsic protein deposition-associated forces and intrinsic protein-surface interaction forces . Bulk measurements cannot reveal the dynamic protein adsorption pathways and thus cannot differentiate between extrinsic and intrinsic forces .
Edit intent: clarity
Original:  The key to controlling protein-surface adsorption is to identify two distinct types of forces responsible for the adsorption: extrinsic protein deposition-associated forces and intrinsic protein-surface interaction forces . Bulk measurements cannot reveal the dynamic protein-surface binding pathways and thus cannot differentiate between extrinsic and intrinsic forces .
Revised:  The key to controlling protein-surface adsorption is to identify two distinct types of forces responsible for the adsorption: extrinsic protein deposition-associated forces and intrinsic protein-surface interaction forces . Bulk measurements cannot reveal the dynamic protein-surface binding pathways and thus cannot differentiate the two adsorption mechanisms .
Edit intent: clarity
Original:  The key to controlling protein-surface adsorptions is to identify and quantify the main adsorption mechanisms: adsorptions that occur (1) while depositing a protein solution onto dry surfaces and (2) after the deposition  . Bulk measurements cannot reveal the dynamic protein adsorption pathways and thus cannot differentiate  the two adsorption mechanisms.
Revised:  The key to controlling protein-surface adsorptions is to identify and quantify the main adsorption mechanisms: adsorptions that occur (1) while depositing a protein solution onto dry surfaces and (2) after the deposition onto wet surfaces . Bulk measurements cannot reveal the dynamic protein adsorption pathways and thus cannot differentiate the two adsorption mechanisms.
Edit intent: clarity
Original:  The key to controlling protein-surface adsorptions is to identify and quantify the main adsorption mechanisms: adsorptions that occur (1) while depositing a protein solution onto dry surfaces and (2) after the deposition  . Bulk measurements cannot reveal the dynamic protein adsorption pathways and thus cannot differentiate  the two adsorption mechanisms.
Revised:  The key to controlling protein-surface adsorptions is to identify and quantify the main adsorption mechanisms: adsorptions that occur (1) while depositing a protein solution onto dry surfaces and (2) after the deposition . Bulk measurements cannot reveal the dynamic protein adsorption pathways and thus cannot differentiate between the two adsorption mechanisms.
Edit intent: fluency
Original:  We have performed real-time single-molecule imaging experiments of streptavidin adsorption to hydrophobic fused-silica surfaces .
Revised:  We imaged the interactions of single streptavidin molecules with hydrophobic fused-silica surfaces .
Edit intent: clarity
Original:  Our analysis indicates that the deposition process is the primary cause of protein adsorption onto surfaces for sub-nanomolar to nanomolar protein concentrations.
Revised:  Our analysis shows that the protein solution deposition process is the primary cause of protein adsorption onto surfaces for sub-nanomolar to nanomolar protein concentrations.
Edit intent: clarity
Original:  Our analysis indicates that the deposition process is the primary cause of protein adsorption onto surfaces for sub-nanomolar to nanomolar protein concentrations.
Revised:  Our analysis indicates that the deposition process is the primary cause of protein adsorption onto surfaces at the sub-nanomolar to nanomolar protein concentrations.
Edit intent: clarity
Original:  We have also observed that hydrophilic fused-silica surfaces  prevent the adsorption of streptavidin proteins .
Revised:  Furthermore, we found that hydrophilic fused-silica surfaces prevent the adsorption of streptavidin proteins .
Edit intent: coherence
Original:  We have also observed that hydrophilic fused-silica surfaces  prevent the adsorption of streptavidin proteins .
Revised:  We have also observed that hydrophilic fused-silica surfaces can prevent the adsorption of streptavidin proteins .
Edit intent: clarity
Original:  We have also observed that hydrophilic fused-silica surfaces  prevent the adsorption of streptavidin proteins .
Revised:  We have also observed that hydrophilic fused-silica surfaces prevent the adsorption of streptavidin molecules .
Edit intent: clarity
Original: Consider the following coloring process in a simple directed graph G = (V,E).
Revised: Consider the following coloring process in a simple directed graph G (V,E).
Edit intent: coherence
Original:  For the special case when G is an undirected graph without isolated vertices , upper bounds of (2/3) \lceil | V | and\rceil \lfloor | V |/2 \rfloor are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Revised:  For the special case when G is an undirected connected graph , upper bounds of (2/3) \lceil | V | and\rceil \lfloor | V |/2 \rfloor are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Edit intent: clarity
Original:  For the special case when G is an undirected graph without isolated vertices , upper bounds of (2/3) \lceil | V | and\rceil \lfloor | V |/2 \rfloor are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Revised:  For the special case when G is an undirected graph without isolated vertices , upper bounds of \lceil | V | and\rceil \lfloor | V |/2 \rfloor are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Edit intent: coherence
Original:  For the special case when G is an undirected graph without isolated vertices , upper bounds of (2/3) \lceil | V | and\rceil \lfloor | V |/2 \rfloor are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Revised:  For the special case when G is an undirected graph without isolated vertices , upper bounds of (2/3) \lceil | V | and\rceil and\lfloor | V |/2 \rfloor are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Edit intent: fluency
Original:  Following Chang and Lyuu \mbox{ CL08 derive upper bounds of 0.7732 | V | and 0.727 | V |  on the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Revised:  We derive upper bounds of 0.7732 | V | and 0.727 | V | on the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Edit intent: coherence
Original:  The bounds hold whenever G has a positive minimum in-degree.
Edit intent: coherence
Original:  For the special case when G is an undirected connected graph, upper bounds of \lceil | V |/2 \rceil and%DIFDELCMD < \lfloor %%% | V |/2%DIFDELCMD < \rfloor %%% are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Revised:  For the special case when G is an undirected connected graph, we prove the existence of an irreversible dynamo with size at most \lceil | V |/2 \rceil and%DIFDELCMD < \lfloor %%% | V |/2%DIFDELCMD < \rfloor %%% are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Edit intent: clarity
Original:  For the special case when G is an undirected connected graph, upper bounds of \lceil | V |/2 \rceil and%DIFDELCMD < \lfloor %%% | V |/2%DIFDELCMD < \rfloor %%% are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Revised:  For the special case when G is an undirected connected graph, upper bounds of \lceil | V |/2 \rceil %DIFDELCMD < \lfloor %%% | V |/2%DIFDELCMD < \rfloor %%% are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Edit intent: fluency
Original:  For the special case when G is an undirected connected graph, upper bounds of \lceil | V |/2 \rceil and%DIFDELCMD < \lfloor %%% | V |/2%DIFDELCMD < \rfloor %%% are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Revised:  For the special case when G is an undirected connected graph, upper bounds of \lceil | V |/2 \rceil and%DIFDELCMD < \lfloor %%% %DIFDELCMD < \rfloor %%% are given for the minimum sizes of irreversible dynamos under the strict and the simple-majority scenarios, respectively.
Edit intent: fluency
Original:  Unless \text{NP}\subseteq \text{TIME}(n^{O(\ln \ln n)}), we show that no polynomial-time, o( \ln | V |)-approximation algorithms exist for finding the minimum irreversible dynamo under either the strict or the simple-majority scenario.
Revised:  Unless \text{NP}\subseteq \text{TIME}(n^{O(\ln \ln n)}), no polynomial-time, o( \ln | V |)-approximation algorithms exist for finding the minimum irreversible dynamo under either the strict or the simple-majority scenario.
Edit intent: coherence
Original:  A unique value of this paper, compared to other Hedge Fund literature freely available on the internet, is that this review is fully sourced from academic references (such as peer reviewed journals) and is thus a bona fide study.
Revised:  A unique value of this paper, compared to other Hedge Fund literature freely available on the internet, is that this review is fully sourced from academic references (such as peer reviewed journals) and is thus a bona fide study.
Edit intent: fluency
Original:  Specializing in the case of full  information, we obtain as a consequence that in a viable market all wealth processes have versions that are semimartingales .
Revised:  Specializing to the case of full information, we obtain as a consequence that in a viable market all wealth processes have versions that are semimartingales .
Edit intent: fluency
Original: We undertake a study of market viability from the perspective of a financial agent with limited access to information.
Revised: We undertake a study of markets from the perspective of a financial agent with limited access to information.
Edit intent: clarity
Original:  Specializing to the case of full agent's information, we obtain as a consequence that in a viable market every properly discounted wealth processes has a version that is a semimartingale.
Edit intent: coherence
Original: This paper investigates the problem of building and maintaining distributed spanning trees in dynamic networks.
Revised: We address the problem of building and maintaining distributed spanning trees in dynamic networks.
Edit intent: clarity
Original:  Hence, we present an algorithm that relies on a perpetual alternation of topology-induced splittings and computation-induced mergings \emph{ of a forest of spanning trees , using random walks of tokens.
Revised:  Hence, we present an algorithm that relies on a perpetual alternation of topology-induced splittings \emph{ of a forest of spanning trees , using random walks of tokens.
Edit intent: clarity
Original:  Hence, we present an algorithm that relies on a perpetual alternation of topology-induced splittings and computation-induced mergings \emph{ of a forest of spanning trees , using random walks of tokens.
Revised:  Hence, we present an algorithm that relies on a perpetual alternation of topology-induced splittings and computation-induced mergings and\emph{ of a forest of spanning trees , using random walks of tokens.
Edit intent: coherence