wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We reveal that neural networks perceive time intervals through the evolution of population state along stereotypical trajectory, maintain time intervals by line attractor along which most neurons vary their activities monotonically with the duration of the maintained interval, and produce or compare time intervals by scaling the evolution velocity of population state .
Revised:  We revealed that neural networks perceive time intervals through the evolution of population state along stereotypical trajectory, maintain time intervals by line attractor along which most neurons vary their activities monotonically with the duration of the maintained interval, and produce or compare time intervals by scaling the evolution velocity of population state .
Edit intent: fluency
Original:  We reveal that neural networks perceive time intervals through the evolution of population state along stereotypical trajectory, maintain time intervals by line attractor along which most neurons vary their activities monotonically with the duration of the maintained interval, and produce or compare time intervals by scaling the evolution velocity of population state .
Revised:  We reveal that neural networks perceive elapsed time through state evolution along stereotypical trajectory, maintain time intervals by line attractor along which most neurons vary their activities monotonically with the duration of the maintained interval, and produce or compare time intervals by scaling the evolution velocity of population state .
Edit intent: clarity
Original:  We reveal that neural networks perceive time intervals through the evolution of population state along stereotypical trajectory, maintain time intervals by line attractor along which most neurons vary their activities monotonically with the duration of the maintained interval, and produce or compare time intervals by scaling the evolution velocity of population state .
Revised:  We reveal that neural networks perceive time intervals through the evolution of population state along stereotypical trajectory, maintain time intervals in working memory in the monotonic increase or decrease of the firing rates of interval-tuned neurons, and compare or produce time intervals by scaling the evolution velocity of population state .
Edit intent: clarity
Original:  We reveal that neural networks perceive time intervals through the evolution of population state along stereotypical trajectory, maintain time intervals by line attractor along which most neurons vary their activities monotonically with the duration of the maintained interval, and produce or compare time intervals by scaling the evolution velocity of population state . Time and non-time information is coded in subspaces orthogonal with each other, which facilitates generalizable decoding of time and non-time information .
Revised:  We reveal that neural networks perceive time intervals through the evolution of population state along stereotypical trajectory, maintain time intervals by line attractor along which most neurons vary their activities monotonically with the duration of the maintained interval, and produce or compare time intervals by scaling state evolution speed. Temporal and non-temporal information are coded in subspaces orthogonal with each other, which facilitates generalizable decoding of time and non-time information .
Edit intent: clarity
Original:  Time and non-time information is coded in subspaces orthogonal with each other, which facilitates generalizable decoding of time and non-time information . This is based on the network structure of multiple feedforward sequences that mutually excite or inhibit depending on whether their preferences of non-timing information are similar or not.
Revised:  Time and non-time information is coded in subspaces orthogonal with each other, and the state trajectories with time at different non-temporal information are quasi-parallel and isomorphic. Such coding geometry facilitates the decoding generalizability of temporal and non-temporal information across each other. The network structure exhibits multiple feedforward sequences that mutually excite or inhibit depending on whether their preferences of non-timing information are similar or not.
Edit intent: meaning-changed
Original:  The eigenvalue decomposition of a correlation matrix into partial correlations - market, group and random modes, enables identification of dominant stocks or "influential leaders" and sectors or "communities". The correlation-based network of leaders and communities changes with time, especially during market events like crashes , bubbles, etc.
Revised:  The correlation structures change drastically, akin to phase transitions in physical phenomena, as do the influential stocks (leaders) and sectors (communities), during market events like crashes , bubbles, etc.
Edit intent: clarity
Original:  The correlation-based network of leaders and communities changes with time, especially during market events like crashes , bubbles, etc. Using the eigen-entropy measure , computed from the eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market correlation and its different modes.
Revised:  The correlation-based network of leaders and communities changes with time, especially during market events like crashes . It is crucial to detect their signatures for timely intervention or prevention. Here we use eigenvalue decomposition and eigen-entropy measure , computed from the eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market correlation and its different modes.
Edit intent: meaning-changed
Original:  Using the eigen-entropy measure , computed from the eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market correlation and its different modes.
Revised:  Using the eigen-entropy , computed from the eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market correlation and its different modes.
Edit intent: clarity
Original:  Using the eigen-entropy measure , computed from the eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market correlation and its different modes.
Revised:  Using the eigen-entropy measure , computed from eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market correlation and its different modes.
Edit intent: fluency
Original:  Using the eigen-entropy measure , computed from the eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market correlation and its different modes.
Revised:  Using the eigen-entropy measure , computed from the eigen-centralities of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market correlation and its different modes.
Edit intent: clarity
Original:  Using the eigen-entropy measure , computed from the eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market correlation and its different modes.
Revised:  Using the eigen-entropy measure , computed from the eigen-centralities (ranks) of different stocks in the cross-correlation matrix, to extract information about the "disorder " (or randomness) in the market correlation and its different modes.
Edit intent: clarity
Original:  Using the eigen-entropy measure , computed from the eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market correlation and its different modes. The relative-entropy measures computed for these modes enable us to construct a "phase space", where the different market events undergo "phase-separation" and display " order-disorder " transitions, as observed in critical phenomena in physics.
Revised:  Using the eigen-entropy measure , computed from the eigen-centralities (ranks) of different stocks in the correlation network, we extract information about the "disorder " (or randomness) in the market . We construct a `phase space', where different market events undergo "phase-separation" and display " order-disorder " transitions, as observed in critical phenomena in physics.
Edit intent: clarity
Original:  The relative-entropy measures computed for these modes enable us to construct a "phase space", where the different market events undergo "phase-separation" and display " order-disorder " transitions, as observed in critical phenomena in physics.
Revised:  The relative-entropy measures computed for these modes enable us to construct a "phase space", where the different market events (bubbles, crashes, etc.) undergo phase separation and display order-disorder " transitions, as observed in critical phenomena in physics.
Edit intent: meaning-changed
Original:  The relative-entropy measures computed for these modes enable us to construct a "phase space", where the different market events undergo "phase-separation" and display " order-disorder " transitions, as observed in critical phenomena in physics. We choose the US S P-500 and Japanese Nikkei-225 financial markets, over a 32-year period, and study the evolution of the cross-correlation matrices and their corresponding eigen-entropies.
Revised:  The relative-entropy measures computed for these modes enable us to construct a "phase space", where the different market events undergo "phase-separation" and display " order-disorder P-500 and Japanese Nikkei-225 financial markets, over a 32-year period, and study the evolution of the cross-correlation matrices and their corresponding eigen-entropies.
Edit intent: coherence
Original:  We choose the US S P-500 and Japanese Nikkei-225 financial markets, over a 32-year period, and study the evolution of the cross-correlation matrices and their corresponding eigen-entropies. One of the relative entropy measures displays "universal scaling " behaviorwith respect to the mean market correlation. Further, a functional of the relative entropy measure acts as a good gauge for the "market fragility" (minimum risk of the market portfolio) and the "market fear" (volatility index).
Revised:  We choose the US S transitions. An entropy functional exhibits scaling behavior. We propose a generic indicator that facilitates the continuous monitoring of the internal structure of the market portfolio) and the "market fear" (volatility index).
Edit intent: clarity
Original:  Further, a functional of the relative entropy measure acts as a good gauge for the "market fragility" (minimum risk of the market portfolio) and the "market fear" (volatility index). This new methodology helps us to better understand market dynamics and characterize the events in different phases as anomalies, bubbles, crashes, etc. that display intriguing phase separation and universal scaling behavior .
Revised:  Further, a functional of the relative entropy measure acts as a good gauge for the "market fragility" (minimum risk of the market -- important for managing risk and stress-testing the financial system. Our methodology would help in understanding and foreseeing tipping points or fluctuation patterns in complex systems .
Edit intent: clarity
Original:  Methods: Data sampling from two brain regions, the HIP and BLA, is performed by extracellular recordings of male Wistar rats and spike sorting is done using Plexon offline sorter.
Revised:  Methods- Data sampling from two brain regions, the HIP and BLA, is performed by extracellular recordings of male Wistar rats and spike sorting is done using Plexon offline sorter.
Edit intent: coherence
Original:  Methods: Data sampling from two brain regions, the HIP and BLA, is performed by extracellular recordings of male Wistar rats and spike sorting is done using Plexon offline sorter.
Revised:  Methods: Data sampling from two brain regions, the HIP and BLA, was performed by the extracellular recordings of male Wistar rats and spike sorting is done using Plexon offline sorter.
Edit intent: fluency
Original:  Methods: Data sampling from two brain regions, the HIP and BLA, is performed by extracellular recordings of male Wistar rats and spike sorting is done using Plexon offline sorter.
Revised:  Methods: Data sampling from two brain regions, the HIP and BLA, is performed by extracellular recordings of male Wistar rats and spike sorting was conducted by Plexon offline sorter.
Edit intent: clarity
Original:  In order to optimize the Izhikevich model parameters, the genetic algorithm is used. Results: In the present study, the possible firing patterns of the real single neurons of the HIP and BLA are identified.
Revised:  In order to optimize the Izhikevich model parameters, a genetic algorithm was used. The process of comparison in each iteration leads to the survival of better populations until achieving the optimum solution. Results- In the present study, the possible firing patterns of the real single neurons of the HIP and BLA are identified.
Edit intent: meaning-changed
Original:  Additionally, improvement of the Izhikevich model is achieved. As a result , the real neuronal spiking pattern of these regions neurons , and the corresponding cases of the Izhikevich neuron spiking pattern are adjusted with great accuracy.
Revised:  Additionally, an improved Izhikevich model was achieved. Accordingly , the real neuronal spiking pattern of these regions neurons , and the corresponding cases of the Izhikevich neuron spiking pattern are adjusted with great accuracy.
Edit intent: coherence
Original:  Conclusion: This study is conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large scale neural networks simulations, as well as reducing the modeling complexity.
Revised:  Conclusion: This study is conducted to elevate our knowledge of neural interactions in different structures of the brain and accelerate the quality of future large-scale neural network simulations, as well as reducing the modeling complexity.
Edit intent: fluency
Original:  In doing so, the same model as for the Ideal Gases (IG) emerges from the experimental reaction time data. We show that the law governing the experimental RT of a group of individuals is the same as the law governing the dynamics of an inanimate system , namely, a system of non-interacting particles (IG) . Both systems are characterized by a collective parameter which is k_BT for the system of particles and what we have called life span parameter for the system of brains.
Revised:  In doing so, the same model as for the Ideal Gases (IG) ( an inanimate system , namely, a system of non-interacting particles (IG) . Both systems are characterized by a collective parameter which is k_BT for the system of particles and what we have called life span parameter for the system of brains.
Edit intent: clarity
Original:  We show that the law governing the experimental RT of a group of individuals is the same as the law governing the dynamics of an inanimate system , namely, a system of non-interacting particles (IG) . Both systems are characterized by a collective parameter which is k_BT for the system of particles and what we have called life span parameter for the system of brains.
Revised:  We show that the law governing the experimental RT of a group of individuals is the same as the law governing the dynamics of an inanimate system of non-interacting particles (IG) . Both systems are characterized by a collective parameter which is k_BT for the system of particles and what we have called life span parameter for the system of brains.
Edit intent: clarity
Original:  We show that the law governing the experimental RT of a group of individuals is the same as the law governing the dynamics of an inanimate system , namely, a system of non-interacting particles (IG) . Both systems are characterized by a collective parameter which is k_BT for the system of particles and what we have called life span parameter for the system of brains.
Revised:  We show that the law governing the experimental RT of a group of individuals is the same as the law governing the dynamics of an inanimate system , namely, a system of non-interacting particles ) emerges from the experimental RT data . Both systems are characterized by a collective parameter which is k_BT for the system of particles and what we have called life span parameter for the system of brains.
Edit intent: meaning-changed
Original:  We show that the law governing the experimental RT of a group of individuals is the same as the law governing the dynamics of an inanimate system , namely, a system of non-interacting particles (IG) . Both systems are characterized by a collective parameter which is k_BT for the system of particles and what we have called life span parameter for the system of brains.
Revised:  We show that the law governing the experimental RT of a group of individuals is the same as the law governing the dynamics of an inanimate system , namely, a system of non-interacting particles (IG) . Both systems are characterised by a collective parameter which is k_BT for the system of particles and what we have called life span parameter for the system of brains.
Edit intent: fluency
Original:  We show that the law governing the experimental RT of a group of individuals is the same as the law governing the dynamics of an inanimate system , namely, a system of non-interacting particles (IG) . Both systems are characterized by a collective parameter which is k_BT for the system of particles and what we have called life span parameter for the system of brains.
Revised:  We show that the law governing the experimental RT of a group of individuals is the same as the law governing the dynamics of an inanimate system , namely, a system of non-interacting particles (IG) . Both systems are characterized by a collective parameter which is k_BT in the case of the system of particles and what we have called life span parameter for the system of brains.
Edit intent: clarity
Original:  We are given a convex body K of unit diameter in Euclidean d-dimensional space (where d is a constant) along with an error parameter \varepsilon > 0. The objective is to determine a  polytope of low combinatorial complexity whose Hausdorff distance from K is at most \varepsilon.
Revised:  We are given a convex body K of unit diameter in Euclidean d-dimensional space (where d is a constant) along with an error parameter \varepsilon > 0, the objective is to determine a polytope of low combinatorial complexity whose Hausdorff distance from K is at most \varepsilon.
Edit intent: fluency
Original:  By combinatorial complexity we mean the total number of faces of all dimensions of the polytope. In the mid-1970's, a result by Dudley showed that O(1/\varepsilon^{(d-1)/2}) facets suffice, and Bronshteyn and Ivanov presented a similar bound on the number of vertices .
Revised:  By combinatorial complexity we mean the total number of faces of all dimensions . Classical constructions by Dudley and Bronshteyn/Ivanov show that O(1/\varepsilon^{(d-1)/2}) facets suffice, and Bronshteyn and Ivanov presented a similar bound on the number of vertices .
Edit intent: clarity
Original:  In the mid-1970's, a result by Dudley showed that O(1/\varepsilon^{(d-1)/2}) facets suffice, and Bronshteyn and Ivanov presented a similar bound on the number of vertices . While both results match known worst-case lower bounds , obtaining a similar upper bound on the total combinatorial complexity has been open for over 40 years. Recently, we made a first step forward towards this objective, obtaining a suboptimal bound.
Revised:  In the mid-1970's, a result by Dudley showed that O(1/\varepsilon^{(d-1)/2}) facets or vertices are possible, respectively, but neither achieves both bounds simultaneously.
Edit intent: clarity
Original:  In this paper, we settle this problem with an asymptotically optimal bound of O(1/\varepsilon^{(d-1)/2})  . Our result is based on a new relationship between \varepsilon-width caps of a convex body and its polar  . Using this relationship, we are able to obtain a volume-sensitive bound on the number of approximating caps that are "essentially different."
Revised:  In this paper, we show that it is possible to construct a polytope with O(1/\varepsilon^{(d-1)/2}) . Our result is based on a new relationship between \varepsilon-width caps of a convex body and its polar . Using this relationship, we are able to obtain a volume-sensitive bound on the number of approximating caps that are "essentially different."
Edit intent: clarity
Original:  In this paper, we settle this problem with an asymptotically optimal bound of O(1/\varepsilon^{(d-1)/2})  . Our result is based on a new relationship between \varepsilon-width caps of a convex body and its polar  . Using this relationship, we are able to obtain a volume-sensitive bound on the number of approximating caps that are "essentially different."
Revised:  In this paper, we settle this problem with an asymptotically optimal bound of O(1/\varepsilon^{(d-1)/2}) combinatorial complexity, which is optimal in the worst case . Our result is based on a new relationship between \varepsilon-width caps of a convex body and its polar . Using this relationship, we are able to obtain a volume-sensitive bound on the number of approximating caps that are "essentially different."
Edit intent: meaning-changed
Original:  In this paper, we settle this problem with an asymptotically optimal bound of O(1/\varepsilon^{(d-1)/2})  . Our result is based on a new relationship between \varepsilon-width caps of a convex body and its polar  . Using this relationship, we are able to obtain a volume-sensitive bound on the number of approximating caps that are "essentially different."
Revised:  In this paper, we settle this problem with an asymptotically optimal bound of O(1/\varepsilon^{(d-1)/2}) . Our result is based on a new relationship between \varepsilon-width caps of a convex body and its polar body . Using this relationship, we are able to obtain a volume-sensitive bound on the number of approximating caps that are "essentially different."
Edit intent: clarity
Original: Shape decomposition is a fundamental problem in geometry processing where an arbitrary object is regarded as an arrangement of simple primitives or semantic components. The application of 3D shape decomposition in the context of image segmentation, however, is not well-studied. In this paper, we develop a shape decomposition algorithm called cylindrical shape decomposition (CSD) to be applied for the segmentation of tubular structures in large-scale 3D images.
Revised: We develop a cylindrical shape decomposition (CSD) to be applied for the segmentation of tubular structures in large-scale 3D images.
Edit intent: coherence
Original:  In this paper, we develop a shape decomposition algorithm called cylindrical shape decomposition (CSD) to be applied for the segmentation of tubular structures in large-scale 3D images. CSD starts by partitioning the curve skeleton of a tubular object into maximal-length sub-skeletons , minimizing an orientation objective.
Revised:  In this paper, we develop a shape decomposition algorithm called cylindrical shape decomposition (CSD) algorithm to decompose an object, which is a union of several tubular structures, into its semantic components. We decompose the object using its curve skeleton and translational sweeps. For that, CSD partitions the curve skeleton of a tubular object into maximal-length sub-skeletons , minimizing an orientation objective.
Edit intent: meaning-changed