wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This tutorial elucidates step-by-step the problems typically encountered when training spiking neural networks, and guides the reader through the key concepts of synaptic plasticity and data-driven learning in the spiking setting.
Revised:  This article elucidates step-by-step the problems typically encountered when training spiking neural networks, and guides the reader through the key concepts of synaptic plasticity and data-driven learning in the spiking setting.
Edit intent: clarity
Original: Unfolding how the brain functionally shifts within the cognitive space remains an unresolved question . From a brain connectivity perspective, there exist two main concepts: cognitive shifts and cognitive flexibility. Although the former is the proxy of the latter, the biggest challenge, in terms of bridging these two concepts, lies in the fact that cognitive shifts are governed by topological rules whereas cognitive flexibility is purely numerical. In this paper, we bridge the aforementioned concepts while preserving the complexity of cognition by proposing a formalism based on a 2D network morphospace that quantifies trapping and exit characteristics of network subsystems, naturally interpreted as functional communities.
Revised: An unresolved question in network neuroscience is the quantification of reconfiguration in functional networks in response to varying cognitive demands. We propose that a mesoscopic generalizable framework would be most apt to investigate the breadth of functional (re-)configurations. We propose a 2D network morphospace that quantifies trapping and exit characteristics of network subsystems, naturally interpreted as functional communities.
Edit intent: clarity
Original:  In this paper, we bridge the aforementioned concepts while preserving the complexity of cognition by proposing a formalism based on a 2D network morphospace that quantifies trapping and exit characteristics of network subsystems, naturally interpreted as functional communities. We show that the constructed measurements reflect the emergent phenomenon of higher-order cognitive states in addition to being able to quantify cognitive flexibility, as a direct output. Leveraging this analytic framework , cognitive shifts among traversedly integrated/segregated states of cognition are shown to be projected from subject specific cognitive signatures. The evidence of individual fingerprint emerged from cognitive flexibility domain legitimizes the quest to explore the intrinsic relationship between flexibility of functional networks and behavioral measures, including fluid intelligence. The constructed multi-linear models using flexibility descriptors demonstrate an above chance level of specificity. Finally, through the associations between behavioral measures and flexibility theory, we found that frontoparietal (FP) activation level, expressed through FP preconfiguration, and default mode network (DMN) efficiency, expressed through DMN preconfiguration, are positively correlated with all behavioral measures .
Revised:  In this paper, we bridge the aforementioned concepts while preserving the complexity of cognition by proposing a formalism based on a 2D network morphospace using novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), that characterize the topology of mesoscopic structures and the flow of information within and between them. This framework captures the behavior of a reference set of functional networks (FNs) with changing mental states. We show that this morphospace is sensitive to different FNs, cognitive tasks and subjects. We propose that functional connectivity changes in FNs may be categorized into three different types of reconfigurations: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration; and quantify the Network Configural Breadth across different tasks. In essence, we put forth a framework that can be used to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity; a tool that can also quantify the changes that result from such an exploration, as the brain switches between mental states .
Edit intent: meaning-changed
Original:  We propose that  a mesoscopic generalizable framework would be most apt to investigate the breadth of functional (re-) configurations.
Revised:  We propose that such functional reconfigurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration. In order to quantify these reconfigurations, we propose a mesoscopic framework focused on functional networks (FNs) or communities. To do so, we introduce a mesoscopic generalizable framework would be most apt to investigate the breadth of functional (re-) configurations.
Edit intent: meaning-changed
Original:  We propose that  a mesoscopic generalizable framework would be most apt to investigate the breadth of functional (re-) configurations. We propose a 2D network morphospace using novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), that characterize the topology of mesoscopic structures and the flow of information within and between them.
Revised:  We propose that a 2D network morphospace using novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), that characterize the topology of mesoscopic structures and the flow of information within and between them.
Edit intent: clarity
Original:  We propose a 2D network morphospace using novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), that characterize the topology of mesoscopic structures and the flow of information within and between them.
Revised:  We propose a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), that characterize the topology of mesoscopic structures and the flow of information within and between them.
Edit intent: clarity
Original:  We propose a 2D network morphospace using novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), that characterize the topology of mesoscopic structures and the flow of information within and between them.
Revised:  We propose a 2D network morphospace using novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between them.
Edit intent: clarity
Original:  We propose a 2D network morphospace using novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), that characterize the topology of mesoscopic structures and the flow of information within and between them. This framework captures the behavior of a reference set of functional networks (FNs) with changing mental states.
Revised:  We propose a 2D network morphospace using novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), that characterize the topology of mesoscopic structures and the flow of information within and between a reference set of functional networks (FNs) with changing mental states.
Edit intent: clarity
Original:  We propose that functional connectivity changes in FNs may be categorized into three different types of reconfigurations: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration; and quantify the Network Configural Breadth across different tasks .
Revised:  We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence .
Edit intent: meaning-changed
Original:  In essence, we put forth a framework that can be used to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity ; a tool that can also quantify the changes that result from such an exploration, as the brain switches between mental states.
Revised:  In essence, we put forth a framework that can be used to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity . This tool that can also quantify the changes that result from such an exploration, as the brain switches between mental states.
Edit intent: coherence
Original:  However, this will require a system for going from EM images to 3D volume meshes which can be used in finite element simulations . Methods: In this paper, we develop an end-to-end pipeline for this task by adapting and extending computer graphics mesh processing and smoothing algorithms.
Revised:  However, this will require a system for going from EM images to 3D volume meshes , which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method (FEM). In this paper, we develop an end-to-end pipeline for this task by adapting and extending computer graphics mesh processing and smoothing algorithms.
Edit intent: clarity
Original:  Results: We apply this pipeline to a series of electron micrographs of  dendrite morphology explored at three different length scales and show that the resultant meshes are suitable for finite element simulations.
Revised:  Results: We apply this pipeline to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resultant meshes are suitable for finite element simulations.
Edit intent: meaning-changed
Original:  Enabling such simulations will require a system for going from electron micrographs to 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method (FEM) . In this paper, we develop an end-to-end pipeline for this task by adapting and extending computer graphics mesh processing and smoothing algorithms.
Revised:  Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method (FEM) . In this paper, we develop an end-to-end pipeline for this task by adapting and extending computer graphics mesh processing and smoothing algorithms.
Edit intent: clarity
Original:  Enabling such simulations will require a system for going from electron micrographs to 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method (FEM) . In this paper, we develop an end-to-end pipeline for this task by adapting and extending computer graphics mesh processing and smoothing algorithms.
Revised:  Enabling such simulations will require a system for going from electron micrographs to 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method . In this paper, we develop an end-to-end pipeline for this task by adapting and extending computer graphics mesh processing and smoothing algorithms.
Edit intent: clarity
Original:  Enabling such simulations will require a system for going from electron micrographs to 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method (FEM) . In this paper, we develop an end-to-end pipeline for this task by adapting and extending computer graphics mesh processing and smoothing algorithms. Our workflow makes use of our recently rewritten mesh processing software, GAMer 2, which implements several mesh conditioning algorithms and serves as a platform to connect different pipeline steps.
Revised:  Enabling such simulations will require a system for going from electron micrographs to 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the Finite Element Method (FEM) . In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, which implements several mesh conditioning algorithms and serves as a platform to connect different pipeline steps.
Edit intent: clarity
Original:  Our workflow makes use of our recently rewritten mesh processing software, GAMer 2, which implements several mesh conditioning algorithms and serves as a platform to connect different pipeline steps. We apply this pipeline to  a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and demonstrate that the resultant meshes are suitable for finite element simulations.
Revised:  Our workflow makes use of our recently rewritten mesh processing software, GAMer 2, to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and demonstrate that the resultant meshes are suitable for finite element simulations.
Edit intent: coherence
Original:  We apply this pipeline to  a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and demonstrate that the resultant meshes are suitable for finite element simulations.
Revised:  We apply this pipeline to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and demonstrate that the resultant meshes are suitable for finite element simulations.
Edit intent: meaning-changed
Original:  The importance of including a model of taxation has first been observed by Moenig and Bauer while considering the Black-Scholes model to describe the underlying security.
Revised:  The importance of including a model of taxation has first been observed by Moenig and Bauer (2016) while considering a GMWB Variable Annuity. In particular, they consider the simple Black-Scholes model to describe the underlying security.
Edit intent: meaning-changed
Original:  Anyway , GMWB are long term products and thus accounting for stochastic interest rate has a relevant impact on both the financial evaluation and the policy holder behavior, as observed by Gouden\`ege et al.  . In this paper we investigate the impact of these two elements together on GMWB evaluation.
Revised:  Nevertheless , GMWB are long term products and thus accounting for stochastic interest rate has a relevant impact on both the financial evaluation and the policy holder behavior, as observed by Gouden\`ege et al. . In this paper we investigate the impact of these two elements together on GMWB evaluation.
Edit intent: fluency
Original:  Anyway , GMWB are long term products and thus accounting for stochastic interest rate has a relevant impact on both the financial evaluation and the policy holder behavior, as observed by Gouden\`ege et al.  . In this paper we investigate the impact of these two elements together on GMWB evaluation.
Revised:  Anyway , GMWB are long term products and thus accounting for stochastic interest rate has relevant effects on both the financial evaluation and the policy holder behavior, as observed by Gouden\`ege et al. . In this paper we investigate the impact of these two elements together on GMWB evaluation.
Edit intent: clarity
Original:  Anyway , GMWB are long term products and thus accounting for stochastic interest rate has a relevant impact on both the financial evaluation and the policy holder behavior, as observed by Gouden\`ege et al.  . In this paper we investigate the impact of these two elements together on GMWB evaluation.
Revised:  Anyway , GMWB are long term products and thus accounting for stochastic interest rate has a relevant impact on both the financial evaluation and the policy holder behavior, as observed by Gouden\`ege et al. (2018) . In this paper we investigate the impact of these two elements together on GMWB evaluation.
Edit intent: meaning-changed
Original:  Anyway , GMWB are long term products and thus accounting for stochastic interest rate has a relevant impact on both the financial evaluation and the policy holder behavior, as observed by Gouden\`ege et al.  . In this paper we investigate the impact of these two elements together on GMWB evaluation.
Revised:  Anyway , GMWB are long term products and thus accounting for stochastic interest rate has a relevant impact on both the financial evaluation and the policy holder behavior, as observed by Gouden\`ege et al. . In this paper we investigate the outcomes of these two elements together on GMWB evaluation.
Edit intent: clarity
Original:  To this aim  we develop a numerical framework which allows one to efficiently compute the fair fee value of a contract. Results show that accounting for both taxation and stochastic interest rate has determinant effects on the behavior of the policy holder and on the cost of GMWB contracts  .
Revised:  To this aim we develop a numerical framework which allows one to efficiently compute the fair fee value of a policy. Numerical results show that accounting for both taxation and stochastic interest rate has determinant effects on the behavior of the policy holder and on the cost of GMWB contracts .
Edit intent: coherence
Original:  Results show that accounting for both taxation and stochastic interest rate has determinant effects on the behavior of the policy holder and on the cost of GMWB contracts  .
Revised:  Results show that accounting for both taxation and stochastic interest rate has determinant effects on the behavior of the policy holder and on the cost of GMWB contracts . In addition, it can explain why these products are so popular with people looking for a protected form of investment for retirement .
Edit intent: meaning-changed
Original:  This papercontains the results from computational experiments aimed at investigating the attractor structure of Boolean networks defined by functions of a given canalizing depth. We observe that Boolean networks with higher canalizing depth have generally fewer attractors, the attractors are smaller, and the basins are larger, with implications for the stability and robustness of the models.
Revised:  In this paper, we investigate the dynamics of a random Boolean network with such properties using analytical methods and simulations. From our simulations, we observe that Boolean networks with higher canalizing depth have generally fewer attractors, the attractors are smaller, and the basins are larger, with implications for the stability and robustness of the models.
Edit intent: clarity
Original:  Moreover, our results show that, from the standpoint of the attractor structure, high canalizing depth, compared to relatively small positive canalizing depth, has a very modest impact on dynamics  .
Revised:  Moreover, our results show that, from the standpoint of the attractor structure, high canalizing depth, compared to relatively small positive canalizing depth, has a very modest impact on dynamics . Motivated by these observations, we conduct mathematical study of the attractor structure of a random Boolean network of canalizing depth one (i.e., the smallest positive depth). For every positive integer \ell, we give an explicit formula for the limit of the expected number of attractors of length \ell in an n-state random Boolean network as n goes to infinity .
Edit intent: meaning-changed
Original:  Given SGAs of a tumor, the model can instantiate the states of the hidden layer, providing  abstract representation (tumor embedding) reflecting characteristics of perturbed molecular/cellular processes in the tumor, which in turn can be used to predict multiple phenotypes.
Revised:  Given SGAs of a tumor, the model can instantiate the states of the hidden layer, providing an abstract representation (tumor embedding) reflecting characteristics of perturbed molecular/cellular processes in the tumor, which in turn can be used to predict multiple phenotypes.
Edit intent: fluency
Original:  In particular, motivated by error correction and reconstruction problems in DNA-based data storage applications, we determine the size of "balls" of radius one in the TDRL metric, as well as the cardinality of the maximum intersection of two balls .
Revised:  In particular, motivated by error correction and reconstruction problems in DNA-based data storage applications, we determine the size of "balls" of radius one in the TDRL metric, as well as the cardinality of the maximum intersection of two such balls, are determined .
Edit intent: clarity
Original:  The localized phase settles above a critical resetting rate, or rate of memory use, and the probability density asymptotically adopts in this regime a non-equilibrium steady state with properties similar to the well known problem of diffusion with resetting to the origin.
Revised:  The localized phase settles above a critical resetting rate, or rate of memory use, and the probability density asymptotically adopts in this regime a non-equilibrium steady state similar to that of the well known problem of diffusion with resetting to the origin.
Edit intent: clarity
Original: Neurons typically show two distinct dynamical regimes: Resting state, corresponding to a stable fixpoint, are often observed for low average input. Higher input commonly result in tonic spiking, which corresponds to a stable limit cycle . Some neurons , however, show an additional intermediate, bistable regime, with coexistence of resting-state fixpoint and limit-cycle spiking .
Revised: Neuronal voltage dynamics of regularly firing neurons typically has one stable attractor: either a fixed point (like in the subthreshold regime) or a limit cycle that defines the tonic firing of action potentials (in the suprathreshold regime). In two of the three spike onset bifurcation sequences that are known to give rise to all-or-none type action potentials , however, show an additional intermediate, bistable regime, with coexistence of resting-state fixpoint and limit-cycle spiking .
Edit intent: meaning-changed
Original:  Higher input commonly result in tonic spiking, which corresponds to a stable limit cycle . Some neurons , however, show an additional intermediate, bistable regime, with coexistence of resting-state fixpoint and limit-cycle spiking .
Revised:  Higher input commonly result in tonic spiking, which corresponds to a stable limit cycle . Some neurons , however, the resting-state fixpoint and limit-cycle spiking .
Edit intent: clarity
Original:  Higher input commonly result in tonic spiking, which corresponds to a stable limit cycle . Some neurons , however, show an additional intermediate, bistable regime, with coexistence of resting-state fixpoint and limit-cycle spiking . In this article, a bistable neuron with spike onset at a saddle-homoclinic orbit bifurcation is investigated. In the bistable region , noise can switch between rest and spiking, and thus increase the variability of the spike train compared to neurons with only one stable attractor.
Revised:  Higher input commonly result in tonic spiking, which corresponds to a stable limit cycle . Some neurons , however, show an additional intermediate, bistable regime, with coexistence of resting-state fixpoint and limit cycle spiking can coexist in an intermediate regime, resulting in bistable dynamics. Here , noise can switch between rest and spiking, and thus increase the variability of the spike train compared to neurons with only one stable attractor.
Edit intent: clarity
Original:  This has consequences for spike-time coding as it effectively increases the output entropy of neuronal ensembles. Here, the spiking statistics is derived for homoclinic conductance-based neuron models.
Revised:  Qualitative features of the resulting spike statistics depend on the spike onset bifurcations. This study focuses on the creation of the spiking limit cycle via the saddle-homoclinic orbit (HOM) bifurcation and derives interspike interval (ISI) densities for a conductance-based neuron models.
Edit intent: meaning-changed