wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show the existence of SSA states with similar behaviours to those observed in experimental recordings, such as very low and irregular neuronal firing rates, and spike-train power spectra with slow fluctuations, only for balanced networks of large size .
Revised:  We show the existence of SSA states with similar behaviours to those observed in experimental recordings, such as very low and irregular neuronal firing rates, and fast-spiking (inhibitory) neurons. We show that both the connection probability and network size are fundamental properties that give rise to self-sustained activity in qualitative agreement with our experimental results. Finally, we provide a more detailed description of the self-sustained activity in terms of lifetime distributions, synaptic conductances, and synaptic currents .
Edit intent: meaning-changed
Original:  Here we demonstrate, using mass spectrometry (MS)  and fluorescence resonance energy transfer (FRET), that parallel-stranded structures consisting of up to 20 G-Ag(I)-G or 30 C-Ag(I)-C contiguous base pairs are formed when natural DNA sequences are mixed with silver cations in aqueous solution.
Revised:  Here we demonstrate, using mass spectrometry (MS) , ion mobility spectrometry (IMS) and fluorescence resonance energy transfer (FRET), that parallel-stranded structures consisting of up to 20 G-Ag(I)-G or 30 C-Ag(I)-C contiguous base pairs are formed when natural DNA sequences are mixed with silver cations in aqueous solution.
Edit intent: meaning-changed
Original:  Here we demonstrate, using mass spectrometry (MS)  and fluorescence resonance energy transfer (FRET), that parallel-stranded structures consisting of up to 20 G-Ag(I)-G or 30 C-Ag(I)-C contiguous base pairs are formed when natural DNA sequences are mixed with silver cations in aqueous solution.
Revised:  Here we demonstrate, using mass spectrometry (MS) and fluorescence resonance energy transfer (FRET), that parallel-stranded structures consisting of up to 20 G-Ag(I)-G contiguous base pairs are formed when natural DNA sequences are mixed with silver cations in aqueous solution.
Edit intent: meaning-changed
Original:   Silver-mediated G-duplexes form preferentially over G-quadruplexes, and the ability of Ag+ to convert G-quadruplexes into silver-paired duplexes may provide a new route to manipulating these biologically relevant structures.
Revised:  FRET indicates that duplexes formed by poly(cytosine) strands with 20 contiguous C-Ag(I)-C base pairs are also parallel. Silver-mediated G-duplexes form preferentially over G-quadruplexes, and the ability of Ag+ to convert G-quadruplexes into silver-paired duplexes may provide a new route to manipulating these biologically relevant structures.
Edit intent: meaning-changed
Original:  Here we report computational evidence that Structural Maintenance of Chromosomes (SMC) proteins -- such as cohesins and condensins -- can cooperate with TopoII to establish a synergistic mechanism to resolve topological entanglements : SMC-driven loop extrusion (or diffusion) induces the spatial localisation of entanglements in turn increasing the topological pressure within knotted or linked regions.
Revised:  Here we report computational evidence that Structural Maintenance of Chromosomes (SMC) proteins -- such as cohesins and condensins -- can cooperate with TopoII to establish a synergistic mechanism to resolve topological entanglements . SMC-driven loop extrusion (or diffusion) induces the spatial localisation of entanglements in turn increasing the topological pressure within knotted or linked regions.
Edit intent: fluency
Original:  Here we report computational evidence that Structural Maintenance of Chromosomes (SMC) proteins -- such as cohesins and condensins -- can cooperate with TopoII to establish a synergistic mechanism to resolve topological entanglements : SMC-driven loop extrusion (or diffusion) induces the spatial localisation of entanglements in turn increasing the topological pressure within knotted or linked regions. As a consequence, knots , links and ensuing entanglements may be readily simplified by Topoisomerase enzymes even in crowded and confined conditions.
Revised:  Here we report computational evidence that Structural Maintenance of Chromosomes (SMC) proteins -- such as cohesins and condensins -- can cooperate with TopoII to establish a synergistic mechanism to resolve topological entanglements : SMC-driven loop extrusion (or diffusion) induces the spatial localisation of essential crossings in turn catalysing the simplification of knots and links by TopoII enzymes even in crowded and confined conditions.
Edit intent: clarity
Original:  The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Reinforcement Learning and to argue why Deep RL can have a lot of potential in the field of High Frequency Trading.
Revised:  The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Neural Networks at its heart and to argue why Deep RL can have a lot of potential in the field of High Frequency Trading.
Edit intent: meaning-changed
Original:  The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Reinforcement Learning and to argue why Deep RL can have a lot of potential in the field of High Frequency Trading.
Revised:  The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Reinforcement Learning and to argue why Deep Learning methods can have a lot of potential in the field of High Frequency Trading.
Edit intent: clarity
Original: The ability to give precise and fast prediction for the price movement of stocks is the key to profitability in High Frequency Trading. The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Neural Networks at its heart and to argue why Deep Learning methods can have a lot of potential in the field of High Frequency Trading.
Revised: Financial trading is at the forefront of time-series analysis, and has grown hand-in-hand with it. The advent of electronic trading has allowed complex machine learning solutions to enter the field of High Frequency Trading.
Edit intent: meaning-changed
Original:  The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Neural Networks at its heart and to argue why Deep Learning methods can have a lot of potential in the field of High Frequency Trading. The paper goes on to analyze the model's performance based on it's prediction accuracy as well as prediction speed across full-day trading simulations.
Revised:  The main objective of this paper is to propose a novel way of modeling the high frequency trading problem using Deep Neural Networks at its heart and to argue why Deep Learning methods can have a lot of potential in the field of financial trading. Financial markets have both long term and short term signals and thus a good predictive model in financial trading should be able to incorporate them together. One of the most sought after forms of electronic trading is high-frequency trading (HFT), typically known for microsecond sensitive changes, which results in a tremendous amount of data. LSTMs are one of the most capable variants of the RNN family that can handle long-term dependencies, but even they are not equipped to handle such long sequences of the order of thousands of data points like in HFT. We propose very-long short term memory networks, or VLSTMs, to deal with such extreme length sequences. We explore the importance of VLSTMs in the context of HFT. We compare our model on publicly available dataset and got a 3.14
Edit intent: meaning-changed
Original: We contribute to the theoretical literature on difference-in-differences estimation for policy evaluation by allowing the common trend assumption to hold conditional on a high-dimensional covariate set. In particular, the covariates can enter the difference-in-differences model in a very flexible form leading to estimation procedures that involve supervised machine learning methods.
Revised: We contribute to the theoretical literature on difference-in-differences estimation for policy evaluation by allowing the common trend assumption that is only valid after conditioning on covariates. We suggest estimators that allow the covariates to enter the model in a very flexible form leading to estimation procedures that involve supervised machine learning methods.
Edit intent: meaning-changed
Original:  We derive asymptotic results for semiparametric and parametric estimators for repeated cross-sections and panel data and show  desirable statistical properties . Notably, a non-standard semiparametric efficiency bound for  difference-in-differences estimation that incorporates the repeated cross-section case is established.
Revised:  We derive asymptotic results for new semiparametric and linear model based estimators for repeated cross-sections and panel data and show desirable statistical properties . Notably, a non-standard semiparametric efficiency bound for difference-in-differences estimation that incorporates the repeated cross-section case is established.
Edit intent: meaning-changed
Original:  We derive asymptotic results for semiparametric and parametric estimators for repeated cross-sections and panel data and show  desirable statistical properties . Notably, a non-standard semiparametric efficiency bound for  difference-in-differences estimation that incorporates the repeated cross-section case is established.
Revised:  We derive asymptotic results for semiparametric and parametric estimators for repeated cross-sections and panel data and show that they have desirable statistical properties . Notably, a non-standard semiparametric efficiency bound for difference-in-differences estimation that incorporates the repeated cross-section case is established.
Edit intent: clarity
Original:  We derive asymptotic results for semiparametric and parametric estimators for repeated cross-sections and panel data and show  desirable statistical properties . Notably, a non-standard semiparametric efficiency bound for  difference-in-differences estimation that incorporates the repeated cross-section case is established.
Revised:  We derive asymptotic results for semiparametric and parametric estimators for repeated cross-sections and panel data and show desirable statistical properties like asymptotic normality and double robustness. Further, we establish a semiparametric efficiency bound for difference-in-differences estimation that incorporates the repeated cross-section case is established.
Edit intent: meaning-changed
Original:  We derive asymptotic results for semiparametric and parametric estimators for repeated cross-sections and panel data and show  desirable statistical properties . Notably, a non-standard semiparametric efficiency bound for  difference-in-differences estimation that incorporates the repeated cross-section case is established.
Revised:  We derive asymptotic results for semiparametric and parametric estimators for repeated cross-sections and panel data and show desirable statistical properties . Notably, a non-standard semiparametric efficiency bound for panel difference-in-differences estimation that incorporates the repeated cross-section case is established.
Edit intent: meaning-changed
Original:  We derive asymptotic results for semiparametric and parametric estimators for repeated cross-sections and panel data and show  desirable statistical properties . Notably, a non-standard semiparametric efficiency bound for  difference-in-differences estimation that incorporates the repeated cross-section case is established. Our proposed semiparametric estimator is shown to attain this bound.
Revised:  We derive asymptotic results for semiparametric and parametric estimators for repeated cross-sections and panel data and show desirable statistical properties . Notably, a non-standard semiparametric efficiency bound for difference-in-differences estimation . The proposed semiparametric estimator is shown to attain this bound.
Edit intent: coherence
Original: In this study , we consider difference-in-differences models with a common trend assumption that is only valid after conditioning on covariates. We suggest novel two-step estimators that allow the covariates to enter the model in a very flexible form. In particular, the first stages can be estimated using supervised machine learning methods. We derive asymptotic results for new semiparametric and linear model based estimators for repeated cross-sections and panel data and show that they have desirable statistical properties like asymptotic normality and double robustness.
Revised: In this study , we consider difference-in-differences models under different assumptions on the relation between the treatment group identifier, time and covariates for cross-sectional and panel data and show that they have desirable statistical properties like asymptotic normality and double robustness.
Edit intent: clarity
Original:  We derive asymptotic results for new semiparametric and linear model based estimators for repeated cross-sections and panel data and show that they have desirable statistical properties like asymptotic normality and double robustness. Further, we establish semiparametric efficiency bounds for difference-in-differences estimation with cross-sectional and panel data. The proposed semiparametric estimators attain the bounds. The usability of the methods is assessed by replicating a study on an employment protection reform. We demonstrate that the notion of high-dimensional common trend confounding has implications for the economic interpretation of the policy evaluation results . Notably, measured reform effects are substantially decreased or even reversed when covariates are included in a data-driven manner .
Revised:  We derive asymptotic results for new semiparametric and linear model based estimators for repeated cross-sections and panel data . The variance lower bound is shown to be sensitive to the model assumptions imposed implying a robustness-efficiency trade-off. The obtained efficient influence functions lead to estimators that are rate double robust and have desirable asymptotic properties under weak first stage convergence conditions. This enables to use sophisticated machine-learning algorithms that can cope with settings where common trend confounding is high-dimensional. The usefulness of the proposed estimators is assessed in an empirical example. It is shown that the efficiency-robustness trade-offs and the choice of first stage predictors can lead to divergent empirical results in practice .
Edit intent: meaning-changed
Original:  In this paper we present a neural network model based on the work of Lombardi, Herrmann, de Arcangelis et al. that captures the relevant dynamics of neural avalanches, and we show how tuning the fraction of inhibitory neurons in this model  removes exponential cut-offs present in the distributions of avalanche strength and duration, and transitions the power spectral density of the network into an epileptic regime.
Revised:  We consider a neural network model based on the work of Lombardi, Herrmann, de Arcangelis et al. that captures the relevant dynamics of neural avalanches, and we show how tuning the fraction of inhibitory neurons in this model removes exponential cut-offs present in the distributions of avalanche strength and duration, and transitions the power spectral density of the network into an epileptic regime.
Edit intent: clarity
Original:  In this paper we present a neural network model based on the work of Lombardi, Herrmann, de Arcangelis et al. that captures the relevant dynamics of neural avalanches, and we show how tuning the fraction of inhibitory neurons in this model  removes exponential cut-offs present in the distributions of avalanche strength and duration, and transitions the power spectral density of the network into an epileptic regime.
Revised:  In this paper we present a neural network model based on the work of Lombardi, Herrmann, De Arcangelis et al. that captures the relevant dynamics of neural avalanches, and we show how tuning the fraction of inhibitory neurons in this model removes exponential cut-offs present in the distributions of avalanche strength and duration, and transitions the power spectral density of the network into an epileptic regime.
Edit intent: fluency
Original:  In this paper we present a neural network model based on the work of Lombardi, Herrmann, de Arcangelis et al. that captures the relevant dynamics of neural avalanches, and we show how tuning the fraction of inhibitory neurons in this model  removes exponential cut-offs present in the distributions of avalanche strength and duration, and transitions the power spectral density of the network into an epileptic regime.
Revised:  In this paper we present a neural network model based on the work of Lombardi, Herrmann, de Arcangelis et al. that captures the relevant dynamics of neural avalanches, and we show how tuning the fraction of inhibitory neurons in this model alters the connectivity of the network over time, removes exponential cut-offs present in the distributions of avalanche strength and duration, and transitions the power spectral density of the network into an epileptic regime.
Edit intent: meaning-changed
Original:  In this paper we present a neural network model based on the work of Lombardi, Herrmann, de Arcangelis et al. that captures the relevant dynamics of neural avalanches, and we show how tuning the fraction of inhibitory neurons in this model  removes exponential cut-offs present in the distributions of avalanche strength and duration, and transitions the power spectral density of the network into an epileptic regime.
Revised:  In this paper we present a neural network model based on the work of Lombardi, Herrmann, de Arcangelis et al. that captures the relevant dynamics of neural avalanches, and we show how tuning the fraction of inhibitory neurons in this model removes exponential cut-offs present in the distributions of avalanche strength and duration, and transitions the power spectral density of the network into an `epileptic' regime.
Edit intent: fluency
Original:  We propose that the brain operates away from this  regime of low inhibitory fraction to protect itself from the dominating avalanches present in these extended distributions  .
Revised:  We propose that the brain operates away from this regime of low inhibitory fraction to protect itself from the dominating avalanches present in these extended distributions . We present control strategies that curtail these power law distributions through either random or, more effectively, targeted disabling of excitatory neurons .
Edit intent: meaning-changed
Original:  It is however less discussed when massively collecting physiological signals in clinical setting. Here we test an off-the-shelf integrated Photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a stable Pulse transit time (PTT) signal.
Revised:  It is increasingly popular to collect as much data as possible in the hospital setting from clinical monitors for research purposes. However, in this setup the data calibration issue is often not discussed and, rather, implicitly assumed, while the clinical monitors might not be designed for the data analysis purpose. We hypothesize that this calibration issue for a secondary analysis may become an important source of artifacts in patient monitor data. We test an off-the-shelf integrated Photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a stable Pulse transit time (PTT) signal.
Edit intent: coherence
Original:  Here we test an off-the-shelf integrated Photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a stable Pulse transit time (PTT) signal.
Revised:  Here we test an off-the-shelf integrated photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a stable Pulse transit time (PTT) signal.
Edit intent: fluency
Original:  Here we test an off-the-shelf integrated Photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a stable Pulse transit time (PTT) signal.
Revised:  Here we test an off-the-shelf integrated Photoplethysmography (PPG) and electrocardiogram (ECG) monitoring device for its ability to yield a reliable pulse transit time (PTT) signal.
Edit intent: clarity
Original:  Method : This is a retrospective clinical study using two databases: one containing 35 subjects who underwent laparoscopic cholecystectomy, another containing 22 subjects who underwent spontaneous breathing test in the intensive care unit.
Revised:  Approach : This is a retrospective clinical study using two databases: one containing 35 subjects who underwent laparoscopic cholecystectomy, another containing 22 subjects who underwent spontaneous breathing test in the intensive care unit.
Edit intent: fluency
Original:  The role of the resulting mitochondrial networks in determining the time evolution of the cellular proportion of mutated mtDNA molecules (heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy variance), remains incompletely understood.
Revised:  The role of the resulting mitochondrial networks in the time evolution of the cellular proportion of mutated mtDNA molecules (heteroplasmy), and cell-to-cell variability in heteroplasmy (heteroplasmy variance), remains incompletely understood.
Edit intent: clarity
Original:  We show that, for a range of models, the rate of increase in heteroplasmy variance , and the rate ofde novo mutation , is proportionately modulated by the  fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Revised:  We show that, under a range of models, the rate of increase in heteroplasmy variance , and the rate ofde novo mutation , is proportionately modulated by the fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Edit intent: clarity
Original:  We show that, for a range of models, the rate of increase in heteroplasmy variance , and the rate ofde novo mutation , is proportionately modulated by the  fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Revised:  We show that, for a range of conditions, the (genetic) rate of increase in heteroplasmy variance , and the rate ofde novo mutation , is proportionately modulated by the fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Edit intent: clarity
Original:  We show that, for a range of models, the rate of increase in heteroplasmy variance , and the rate ofde novo mutation , is proportionately modulated by the  fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Revised:  We show that, for a range of models, the rate of increase in heteroplasmy variance de novo mutation , is proportionately modulated by the fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Edit intent: coherence
Original:  We show that, for a range of models, the rate of increase in heteroplasmy variance , and the rate ofde novo mutation , is proportionately modulated by the  fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Revised:  We show that, for a range of models, the rate of increase in heteroplasmy variance , and the rate of mutation , is proportionately modulated by the fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Edit intent: clarity