wanyu/IteraTeR_v2 huggingface dataset:
Original:  This was confirmed by a comparison of our empirical findings with those of a recently described computational model of network growth based on fusion-fission balance.
Revised:  We confirmed our results by contrasting our emprirical findings with the predictions of a recently described computational model of network growth based on fusion-fission balance.
Edit intent: clarity
Original: Energy transparency is a concept that makes a program's energy consumption visible  from hardware up to software, through the different system layers.
Revised: Energy transparency is a concept that makes a program's energy consumption visible , from hardware up to software, through the different system layers.
Edit intent: fluency
Original:  We apply our energy estimation techniques to a comprehensive set of benchmarks, including single-threaded and also multi-threaded embedded programs from two commonly used concurrency patterns, task farms and pipelines.
Revised:  We apply our energy estimation techniques to a comprehensive set of benchmarks, including single- and also multi-threaded embedded programs from two commonly used concurrency patterns, task farms and pipelines.
Edit intent: fluency
Original:  Our profiling technique captures the actual energy consumption at the LLVM IR level with an average error of less than 3 %DIFDELCMD < \\%%% %DIF < .abstract 
Revised:  Our profiling technique captures the actual energy consumption at the LLVM IR level with an average error of 3 %DIFDELCMD < \\%%% %DIF < .abstract 
Edit intent: clarity
Original: Unimodular networks are stochastic generalizations of finite graphs  .
Revised: Unimodular networks are a generalization of finite graphs .
Edit intent: clarity
Original:  We prove a lower bound on the spectral radius of both the adjacency and random walk operator of a unimodular network in terms of its average degree.
Revised:  We prove a lower bound to the spectral radius of both the adjacency and random walk operator of a unimodular network in terms of its average degree.
Edit intent: fluency
Original:  Using this we prove an Alon-Boppana type bound for the largest eigenvalues in absolute value of large, connected, bounded degree graphs .
Revised:  This allows to prove an Alon-Boppana type bound for the largest eigenvalues in absolute value of large, connected, bounded degree graphs .
Edit intent: clarity
Original:  We also provide a lower bound on the volume growth rate of a unimodular tree in terms of its average degree.
Revised:  We also provide a lower bound on the volume growth rate of an unimodular tree in terms of its average degree.
Edit intent: fluency
Original:  This allows to prove an Alon-Boppana type bound for the largest eigenvalues in absolute value of large, connected, bounded degree graphs, which generalizes the Alon-Boppana Theorem for regular graphs.
Revised:  This allows to prove an Alon-Boppana type bound for the largest eigenvalues in absolute value of large, connected, bounded degree graphs, which generalizes the Alon-Boppana theorem for regular graphs.
Edit intent: fluency
Original: Hebbian plasticity allows biological agents to learn from their lifetime experience , extending the fixed information provided by evolutionary search.
Revised: Hebbian plasticity is a powerful principle that allows biological brains to learn from their lifetime experience , extending the fixed information provided by evolutionary search.
Edit intent: clarity
Original:  Here we use backpropagation to train fully-differentiable plastic networks, such that backpropagation determines not only the baseline weights  , but also the plasticityof each connection.
Revised:  Here we use backpropagation to train not just the baseline weights , but also the plasticityof each connection.
Edit intent: coherence
Original:  Here we use backpropagation to train fully-differentiable plastic networks, such that backpropagation determines not only the baseline weights  , but also the plasticityof each connection.
Revised:  Here we use backpropagation to train fully-differentiable plastic networks, such that backpropagation determines not only the baseline weights of the connections , but also the plasticityof each connection.
Edit intent: clarity
Original:  For a BSS with CoMP based system, we propose various BSS patterns to achieve suitable trade-off between energy efficiency and throughput.
Revised:  For a BSS with CoMP based system, we propose various BSS patterns to achieve suitable trade-offs between energy savings and throughput.
Edit intent: clarity
Original:  We utilize these results to formulate the BSS with CoMP as an optimization problem.
Revised:  We utilize these results to formulate the BSS with CoMP optimization problem.
Edit intent: clarity
Original:  A heuristic that solves this problem for a given rate threshold is presented.
Revised:  A heuristic that solves this problem is presented.
Edit intent: clarity
Original: The vast majority of recent studies in market impact assess each product individually, and the interactions between their order flows are disregarded.
Revised: The vast majority of market impact studies assess each product individually, and the interactions between their order flows are disregarded.
Edit intent: clarity
Original: The vast majority of recent studies in market impact assess each product individually, and the interactions between their order flows are disregarded.
Revised: The vast majority of recent studies in market impact assess each product individually, and the interactions between the different order flows are disregarded.
Edit intent: clarity
Original:  Transactions  mediate a significant part of the interaction between different instruments.
Revised:  Transactions in fact mediate a significant part of the interaction between different instruments.
Edit intent: clarity
Original:  Transactions  mediate a significant part of the interaction between different instruments.
Revised:  Transactions mediate a significant part of the correlation between different instruments.
Edit intent: clarity
Original:  We introduce a multivariate linear propagator model that successfully describes such a structure, and reproduces well the response and a significant fraction of the covariance matrix of returns.
Revised:  We introduce a multivariate linear propagator model that successfully describes such a structure, and accounts for a significant fraction of the covariance matrix of returns.
Edit intent: clarity
Original:  We introduce a multivariate linear propagator model that successfully describes such a structure, and reproduces well the response and a significant fraction of the covariance matrix of returns.
Revised:  We introduce a multivariate linear propagator model that successfully describes such a structure, and reproduces well the response and a significant fraction of the covariance of stock returns.
Edit intent: clarity
Original:  We explain in detail the various dynamical mechanisms that contribute to these quantities .
Revised:  We dissect the various dynamical mechanisms that contribute to these quantities .
Edit intent: clarity
Original:  We explain in detail the various dynamical mechanisms that contribute to these quantities .
Revised:  We explain in detail the various dynamical mechanisms that contribute to the joint dynamics of assets .
Edit intent: clarity
Original:  We also define two simplified models with substantially less parameters  to reduce overfitting, and show that they have superior out-of-sample performance.
Revised:  We also define two simplified models with substantially less parameters in order to reduce overfitting, and show that they have superior out-of-sample performance.
Edit intent: coherence
Original:  While it is still unknown how populations of neurons can learn to solve such a diverse set of problems, techniques have recently emerged that allow us to determine how to couple neurons to form networks that solve tasks of similar complexity.
Revised:  While it is still unknown how populations of neurons can learn to solve such a diverse set of problems, techniques have recently emerged that allow us to create networks of model neurons that solve tasks of similar complexity.
Edit intent: clarity
Original:  The most versatile of these approaches are referred to as reservoir computing based techniques.
Edit intent: coherence
Original:  Here, we demonstrate the direct applicability of the FORCE method to spiking neurons by training networks to mimic various dynamical systems .
Revised:  Here, we demonstrate the direct applicability of FORCE training to spiking neurons by training networks to mimic various dynamical systems .
Edit intent: clarity
Original:  Techniques have recently emerged that allow us to create networks of model neurons that solve tasks of similar complexity.
Revised:  Techniques have recently emerged that allow us to create networks of model neurons that display behaviours of similar complexity.
Edit intent: clarity
Original: This paper proposes the first implementation of a regular register by n servers that is tolerant to both mobile Byzantine agents, and transient failures (it is self-stabilizing) in a round-free synchronous model.
Revised: This paper proposes the first implementation of a self-stabilizing regular register emulated by n servers that is tolerant to both mobile Byzantine agents, and transient failures (it is self-stabilizing) in a round-free synchronous model.
Edit intent: clarity
Original: This paper proposes the first implementation of a regular register by n servers that is tolerant to both mobile Byzantine agents, and transient failures (it is self-stabilizing) in a round-free synchronous model.
Revised: This paper proposes the first implementation of a regular register by n servers that is tolerant to both mobile Byzantine agents, and transient failures in a round-free synchronous model.
Edit intent: coherence
Original:  We consider the most difficult model for mobile Byzantine agents to date where the message delay , \delta, and the speed of mobile Byzantine agents , \Delta, are completely decoupled .
Revised:  We consider the most difficult model for mobile Byzantine agents to date where the message delay (i.e., \delta) and the period of mobile Byzantine agents , \Delta, are completely decoupled .
Edit intent: clarity
Original: The register is maintained by n servers and our algorithm tolerates (i) any number of transient failures, and (ii) up to f Mobile Byzantine agents.
Revised: The register is maintained by n servers and our algorithm tolerates (i) any number of transient failures, and (ii) up to f Mobile Byzantine agents.
Edit intent: fluency
Original:  Our implementation uses bounded timestamps from the \mathbb{Z\_}{  is optimal with respect to the number of tolerated mobile Byzantine agents .
Revised:  In addition, our implementation uses bounded timestamps from the \mathbb{Z\_}{ is optimal with respect to the number of tolerated mobile Byzantine agents .
Edit intent: coherence
Original:  Our implementation uses bounded timestamps from the \mathbb{Z\_}{  is optimal with respect to the number of tolerated mobile Byzantine agents .
Revised:  Our implementation uses bounded timestamps from the \mathbb{Z\_}{ domain and it is optimal with respect to the number of tolerated mobile Byzantine agents .
Edit intent: clarity
Original:  The convergence time of our solution is upper bounded by 3\Delta + T\_ 5write()%DIFDELCMD < } %%% , where T_{5write() .
Revised:  The convergence time of our solution is upper bounded by 3\Delta + T\_ %DIFDELCMD < } %%% , where T_{5write() .
Edit intent: fluency
Original:  Equivalently, postselected quantum computers cannot break SZK or NISZK in a black-box manner.
Edit intent: coherence
Original:  Using our techniques, we also give oracles relative to which the following two separations hold: honest-verifier perfect zero knowledge ( HVPZK ) is not contained in its complement ( coHVPZK ), and SZK (indeed, even NISZK) is not contained in PZK (indeed, even HVPZK).
Revised:  Using our techniques, we also give oracles relative to which the following two separations hold: perfect zero knowledge ( HVPZK ) is not contained in its complement ( coHVPZK ), and SZK (indeed, even NISZK) is not contained in PZK (indeed, even HVPZK).
Edit intent: clarity
Original:  Using our techniques, we also give oracles relative to which the following two separations hold: honest-verifier perfect zero knowledge ( HVPZK ) is not contained in its complement ( coHVPZK ), and SZK (indeed, even NISZK) is not contained in PZK (indeed, even HVPZK).
Revised:  Using our techniques, we also give oracles relative to which the following two separations hold: honest-verifier perfect zero knowledge ( PZK ) is not contained in its complement ( coHVPZK ), and SZK (indeed, even NISZK) is not contained in PZK (indeed, even HVPZK).
Edit intent: fluency
Original:  Using our techniques, we also give oracles relative to which the following two separations hold: honest-verifier perfect zero knowledge ( HVPZK ) is not contained in its complement ( coHVPZK ), and SZK (indeed, even NISZK) is not contained in PZK (indeed, even HVPZK).
Revised:  Using our techniques, we also give oracles relative to which the following two separations hold: honest-verifier perfect zero knowledge ( HVPZK ) is not contained in its complement ( coPZK ), and SZK (indeed, even NISZK) is not contained in PZK (indeed, even HVPZK).
Edit intent: fluency
Original: In both query and communication complexity , we give separations between the class NISZK, containing those problemswith non-interactive statistical zero knowledge proof systems, and the  class UPP, containing those problems with randomized algorithms with unbounded error.
Revised: In both query and communication complexity , we give the class UPP, containing those problems with randomized algorithms with unbounded error.
Edit intent: clarity
Original: In both query and communication complexity , we give separations between the class NISZK, containing those problemswith non-interactive statistical zero knowledge proof systems, and the  class UPP, containing those problems with randomized algorithms with unbounded error.
Revised: In both query and communication complexity , we give separations between the class NISZK, containing those problemswith non-interactive statistical zero knowledge proof systems, and the class UPP, containing those problems solvable by randomized algorithms with unbounded error.
Edit intent: clarity
Original:  These results significantly improve on earlier query separations of Vereschagin Ver95%DIFDELCMD < ] %%% and Aaronson Aar12%DIFDELCMD < ] %%% and earlier communication complexity separations of Klauck Kla11%DIFDELCMD < ] %%% and Razborov and Sherstov RS10%DIFDELCMD < ]%%% . In addition, our results imply an oracle relative to which the class NISZK is not contained in PP.
Revised:  Ver95%DIFDELCMD < ] %%% and Aaronson Aar12%DIFDELCMD < ] %%% and earlier communication complexity separations of Klauck Kla11%DIFDELCMD < ] %%% and Razborov and Sherstov RS10%DIFDELCMD < ]%%% . In addition, our results imply an oracle relative to which the class NISZK is not contained in PP.
Edit intent: clarity
Original:  These results significantly improve on earlier query separations of Vereschagin Ver95%DIFDELCMD < ] %%% and Aaronson Aar12%DIFDELCMD < ] %%% and earlier communication complexity separations of Klauck Kla11%DIFDELCMD < ] %%% and Razborov and Sherstov RS10%DIFDELCMD < ]%%% . In addition, our results imply an oracle relative to which the class NISZK is not contained in PP.
Revised:  These results significantly improve on earlier query separations of Vereschagin %DIFDELCMD < ] %%% and Aaronson Aar12%DIFDELCMD < ] %%% and earlier communication complexity separations of Klauck Kla11%DIFDELCMD < ] %%% and Razborov and Sherstov RS10%DIFDELCMD < ]%%% . In addition, our results imply an oracle relative to which the class NISZK is not contained in PP.
Edit intent: clarity
Original:  These results significantly improve on earlier query separations of Vereschagin Ver95%DIFDELCMD < ] %%% and Aaronson Aar12%DIFDELCMD < ] %%% and earlier communication complexity separations of Klauck Kla11%DIFDELCMD < ] %%% and Razborov and Sherstov RS10%DIFDELCMD < ]%%% . In addition, our results imply an oracle relative to which the class NISZK is not contained in PP.
Revised:  These results significantly improve on earlier query separations of Vereschagin Ver95%DIFDELCMD < ] %%% Aar12%DIFDELCMD < ] %%% and earlier communication complexity separations of Klauck Kla11%DIFDELCMD < ] %%% and Razborov and Sherstov RS10%DIFDELCMD < ]%%% . In addition, our results imply an oracle relative to which the class NISZK is not contained in PP.
Edit intent: fluency
Original:  These results significantly improve on earlier query separations of Vereschagin Ver95%DIFDELCMD < ] %%% and Aaronson Aar12%DIFDELCMD < ] %%% and earlier communication complexity separations of Klauck Kla11%DIFDELCMD < ] %%% and Razborov and Sherstov RS10%DIFDELCMD < ]%%% . In addition, our results imply an oracle relative to which the class NISZK is not contained in PP.
Revised:  These results significantly improve on earlier query separations of Vereschagin Ver95%DIFDELCMD < ] %%% and Aaronson %DIFDELCMD < ] %%% and earlier communication complexity separations of Klauck Kla11%DIFDELCMD < ] %%% and Razborov and Sherstov RS10%DIFDELCMD < ]%%% . In addition, our results imply an oracle relative to which the class NISZK is not contained in PP.
Edit intent: fluency
Original:  These results significantly improve on earlier query separations of Vereschagin Ver95%DIFDELCMD < ] %%% and Aaronson Aar12%DIFDELCMD < ] %%% and earlier communication complexity separations of Klauck Kla11%DIFDELCMD < ] %%% and Razborov and Sherstov RS10%DIFDELCMD < ]%%% . In addition, our results imply an oracle relative to which the class NISZK is not contained in PP.
Revised:  These results significantly improve on earlier query separations of Vereschagin Ver95%DIFDELCMD < ] %%% and Aaronson Aar12%DIFDELCMD < ] %%% Kla11%DIFDELCMD < ] %%% and Razborov and Sherstov RS10%DIFDELCMD < ]%%% . In addition, our results imply an oracle relative to which the class NISZK is not contained in PP.
Edit intent: clarity
Original:  Using our techniques, we also give oracles relative to which the following two separations hold: perfect zero knowledge (PZK) is not contained in its complement (coPZK), and SZK (indeed, even NISZK ) is not contained in PZK (indeed, even HVPZK).
Revised:  Using our techniques, we also give oracles relative to which the following two separations hold: perfect zero knowledge (PZK) is not contained in PZK, NISZK is not contained in PZK (indeed, even HVPZK).
Edit intent: clarity
Original:  We prove a number of implications of these results , which may be of independent interest } outside of structural complexity.
Revised:  We prove a number of implications of these results , which may be of independent interest \aa} outside of structural complexity.
Edit intent: fluency
Original:  Specifically, our oracle separation implies that certain parameters of the Polarization Lemma of Sahai and Vadhan SV03%DIFDELCMD < ] %%% cannot be much improved in a black-box manner.
Revised:  SV03%DIFDELCMD < ] %%% cannot be much improved in a black-box manner.
Edit intent: coherence
Original:  Specifically, our oracle separation implies that certain parameters of the Polarization Lemma of Sahai and Vadhan SV03%DIFDELCMD < ] %%% cannot be much improved in a black-box manner.
Revised:  Specifically, our oracle separation implies that certain parameters of the Polarization Lemma of Sahai and Vadhan %DIFDELCMD < ] %%% cannot be much improved in a black-box manner.
Edit intent: clarity
Original:  CTY11%DIFDELCMD < ] %%% are surprisingly powerful in the sense that, with just logarithmic cost, they can compute functions outside of UPP^CC .
Revised:  %DIFDELCMD < ] %%% are surprisingly powerful in the sense that, with just logarithmic cost, they can compute functions outside of UPP^CC .
Edit intent: coherence
Original: We present an efficient numerical procedure to generate models of neural circuits which exhibit rich dynamical behavior.
Revised: This article describes a numerical procedure designed to tune the parameters of periodically-driven dynamical systems to a state in which they exhibit rich dynamical behavior.
Edit intent: clarity
Original:  This is achieved by imposing that the circuits are close to many nonlinear resonances .
Revised:  This is achieved by maximizing the diversity of subharmonic solutions available to the system within a range of the parameters that define the driving .
Edit intent: clarity
Original:  When driven by external input, the resulting circuits present multiple stable patterns of periodic activity organized in complex tuning diagrams and signatures of low dimensional chaos.
Revised:  When driven by external input, exhibit multiple stable patterns of periodic activity organized in complex tuning diagrams and signatures of low dimensional chaos.
Edit intent: clarity
Original: A graph G covers a graph H if there exists a locally bijective homomorphism from G to H. We deal with regular covers in which this homomorphism is prescribed by the action of a semiregular subgroup of %DIFDELCMD < {\rm %DIFDELCMD < %%% Aut (G).
Revised: A graph G covers a graph H if there exists a locally bijective homomorphism from G to H. We deal with regular covers where this homomorphism is prescribed by the action of a semiregular subgroup of %DIFDELCMD < {\rm %DIFDELCMD < %%% Aut (G).
Edit intent: fluency
Original: A graph G covers a graph H if there exists a locally bijective homomorphism from G to H. We deal with regular covers in which this homomorphism is prescribed by the action of a semiregular subgroup of %DIFDELCMD < {\rm %DIFDELCMD < %%% Aut (G).
Revised: A graph G covers a graph H if there exists a locally bijective homomorphism from G to H. We deal with regular covers in which this homomorphism is prescribed by the action of a semiregular subgroup of %DIFDELCMD < {\rm %DIFDELCMD < %%% Aut (G).
Edit intent: fluency
Original:  Regular covers have many applications constructions and studies of big objects all over mathematics and computer science.
Edit intent: coherence
Original:  Our main result shows that RegularCover can be solved for planar inputs G in FPT time O^*(2^{e(H)/2}) where e(H) denotes the number of the edges of H  . In comparison, B\'ilka et al. (2011) proved that testing general graph covers is NP-complete for planar inputs G when H is a small fixed graph such as K_4 or K_5.
Revised:  Our main result shows that RegularCover can be solved for planar inputs G in time O^*(2^{e(H)/2}) where e(H) denotes the number of the edges of H . In comparison, B\'ilka et al. (2011) proved that testing general graph covers is NP-complete for planar inputs G when H is a small fixed graph such as K_4 or K_5.
Edit intent: clarity
Original:  Our main result shows that RegularCover can be solved for planar inputs G in FPT time O^*(2^{e(H)/2}) where e(H) denotes the number of the edges of H  . In comparison, B\'ilka et al. (2011) proved that testing general graph covers is NP-complete for planar inputs G when H is a small fixed graph such as K_4 or K_5.
Revised:  Our main result shows that RegularCover can be solved for planar inputs G in FPT time O^*(2^{e(H)/2}) where e(H) denotes the number of edges of H . In comparison, B\'ilka et al. (2011) proved that testing general graph covers is NP-complete for planar inputs G when H is a small fixed graph such as K_4 or K_5.
Edit intent: fluency
Original:  Automatic text summarization helps the clinicians and researchers in the biomedical domain to access the intended information efficiently from the large volume of scientific literature and other textual resources .
Revised: Objective: Automatic text summarization helps the clinicians and researchers in the biomedical domain to access the intended information efficiently from the large volume of scientific literature and other textual resources .
Edit intent: coherence