wanyu/IteraTeR_v2 huggingface dataset:
Original:  Afterwards, training losses are computed using connectionist temporal classification (CTC).
Revised:  Afterwards, training losses are computed by connectionist temporal classification (CTC).
Edit intent: fluency
Original:  During routing, two kinds of information, learnable weights and iteration outputs are shared across the slices.
Revised:  During routing, learnable weights and iteration outputs are shared across the slices.
Edit intent: clarity
Original:  By sharing the information , the required parameter numbers can be controlled by the given window size regardless of the length of sequences.
Revised:  By sharing the two , the required parameter numbers can be controlled by the given window size regardless of the length of sequences.
Edit intent: clarity
Original:  By sharing the information , the required parameter numbers can be controlled by the given window size regardless of the length of sequences.
Revised:  By sharing the information , the required number of parameters can be controlled by the given window size regardless of the length of sequences.
Edit intent: clarity
Original:  By sharing the information , the required parameter numbers can be controlled by the given window size regardless of the length of sequences.
Revised:  By sharing the information , the required parameter numbers can be controlled by the window size regardless of the length of sequences.
Edit intent: clarity
Original:  Moreover, the method can minimize decoding speed degradation caused by the routing iterations since it can operate in a non-iterative manner at inference time without dropping accuracy.
Revised:  Moreover, the method can minimize decoding speed degradation caused by the routing iterations since it can operate in a non-iterative manner without dropping accuracy.
Edit intent: clarity
Original:  The method achieves a similar word error rate of 16.9 %DIFDELCMD < \\%%% %DIF < on the Wall Street Journal corpus compared to  bidirectional long short-term memory-based CTC networks.
Revised:  The method achieves a similar word error rate at 16.9 %DIFDELCMD < \\%%% %DIF < on the Wall Street Journal corpus compared to bidirectional long short-term memory-based CTC networks.
Edit intent: fluency
Original:  Here, we use this clustering to classify the official national SARS-CoV-2 daily infection counts and check for infection counts that are unusual by being anti-clustered.
Revised:  Here, we use this clustering to classify the official national SARS-CoV-2 daily infection counts and check for infection counts that are unusually anti-clustered.
Edit intent: clarity
Original:  The P _{ij values should be uniformly distributed.
Revised:  The P '_{ij values should be uniformly distributed.
Edit intent: fluency
Original:  We find the value \phi_i that minimises the Kolmogorov--Smirnov distance from a uniform distribution.
Revised:  We find the value \phi_i that minimises the Kolmogorov-Smirnov distance from a uniform distribution.
Edit intent: fluency
Original:  The 28-day sequences of DZ , BY, TR, AE have strongly sub-Poissonian preferred models , with \phi_i^{28} < 0.5;
Revised:  The 28-day sequences of DZ , BY, TR, AE have strongly sub-Poissonian , with \phi_i^{28} < 0.5;
Edit intent: clarity
Original:  Most are found to be consistent with the \phi_i model , the 28-, 14- and 7-day least noisy sequences for several countries are best modelled as sub-Poissonian, suggesting a distinct epidemiological family.
Revised:  Most are found to be consistent with the \phi_i model . The 28-, 14- and 7-day least noisy sequences for several countries are best modelled as sub-Poissonian, suggesting a distinct epidemiological family.
Edit intent: fluency
Original:  The 28-day least noisy sequence of DZ (Algeria ) has a preferred model that is strongly sub-Poissonian, with \phi_i^{28} < 0.1. TJ, TR, RU, BY, AL, AE, and NI have preferred models that are also sub-Poissonian, with \phi_i^{28} < 0.5.
Revised:  The 28-day least noisy sequence of Algeria has a preferred model that is strongly sub-Poissonian, with \phi_i^{28} < 0.1. TJ, TR, RU, BY, AL, AE, and NI have preferred models that are also sub-Poissonian, with \phi_i^{28} < 0.5.
Edit intent: clarity
Original:  This study aims to provide researchers with an interdisciplinary perspective on time  .
Revised:  This study aims to provide researchers with an interdisciplinary perspective on time perception .
Edit intent: clarity
Original:  Firstly, we briefly discussed the necessary information from psychology and neuroscience , such as characteristics and models of time perception and  related abilities.
Revised:  Firstly, we introduce a brief background from the psychology and neuroscience , such as characteristics and models of time perception and related abilities.
Edit intent: clarity
Original:  Firstly, we briefly discussed the necessary information from psychology and neuroscience , such as characteristics and models of time perception and  related abilities.
Revised:  Firstly, we briefly discussed the necessary information from psychology and neuroscience literature, covering the characteristics and models of time perception and related abilities.
Edit intent: clarity
Original:  Firstly, we briefly discussed the necessary information from psychology and neuroscience , such as characteristics and models of time perception and  related abilities.
Revised:  Firstly, we briefly discussed the necessary information from psychology and neuroscience , such as characteristics and models of time perception and the related abilities.
Edit intent: fluency
Original:  Secondly, we investigated the emergent computational and robotic models of time perception.
Revised:  Secondly, we summarize the emergent computational and robotic models of time perception.
Edit intent: clarity
Original:  As a result of the review, we observed that most timing models showed a sign of dedicated time processing like the emergence of  clock-like mechanism from the neural network dynamics and revealed a relationship between  embodiment and time perception.
Revised:  A general overview to the literature reveals that a substantial amount of timing models are based on a dedicated time processing like the emergence of clock-like mechanism from the neural network dynamics and revealed a relationship between embodiment and time perception.
Edit intent: clarity
Original:  As a result of the review, we observed that most timing models showed a sign of dedicated time processing like the emergence of  clock-like mechanism from the neural network dynamics and revealed a relationship between  embodiment and time perception.
Revised:  As a result of the review, we observed that most timing models showed a sign of dedicated time processing like the emergence of a clock-like mechanism from the neural network dynamics and revealed a relationship between embodiment and time perception.
Edit intent: fluency
Original:  As a result of the review, we observed that most timing models showed a sign of dedicated time processing like the emergence of  clock-like mechanism from the neural network dynamics and revealed a relationship between  embodiment and time perception.
Revised:  As a result of the review, we observed that most timing models showed a sign of dedicated time processing like the emergence of clock-like mechanism from the neural network dynamics and reveal a relationship between embodiment and time perception.
Edit intent: fluency
Original:  As a result of the review, we observed that most timing models showed a sign of dedicated time processing like the emergence of  clock-like mechanism from the neural network dynamics and revealed a relationship between  embodiment and time perception.
Revised:  As a result of the review, we observed that most timing models showed a sign of dedicated time processing like the emergence of clock-like mechanism from the neural network dynamics and revealed a relationship between the embodiment and time perception.
Edit intent: fluency
Original:  We also noticed that most models of timing  developed for either sensory timing , the ability of assessment of an interval , or motor timing , ability to reproduce an interval .
Revised:  We also notice that most models of timing developed for either sensory timing , the ability of assessment of an interval , or motor timing , ability to reproduce an interval .
Edit intent: fluency
Original:  We also noticed that most models of timing  developed for either sensory timing , the ability of assessment of an interval , or motor timing , ability to reproduce an interval .
Revised:  We also noticed that most models of timing are developed for either sensory timing , the ability of assessment of an interval , or motor timing , ability to reproduce an interval .
Edit intent: clarity
Original:  We also noticed that most models of timing  developed for either sensory timing , the ability of assessment of an interval , or motor timing , ability to reproduce an interval .
Revised:  We also noticed that most models of timing developed for either sensory timing , the ability of assessment of an interval ) or motor timing , ability to reproduce an interval .
Edit intent: fluency
Original:  We also noticed that most models of timing  developed for either sensory timing , the ability of assessment of an interval , or motor timing , ability to reproduce an interval .
Revised:  We also noticed that most models of timing developed for either sensory timing , the ability of assessment of an interval , or motor timing (i.e. ability to reproduce an interval .
Edit intent: coherence
Original:  In this light, we discussed possible research directions to promote interdisciplinary collaboration for time perception.
Revised:  In this light, we discuss the possible research directions to promote interdisciplinary collaboration for time perception.
Edit intent: fluency
Original:  Temporal information is required for higher-level cognitive abilities such as planning, decision making, communication  and effective cooperation.
Revised:  Temporal information is required for higher-level cognitive abilities such as planning, decision making, communication , and effective cooperation.
Edit intent: fluency
Original:  Firstly, we introduce a brief background from the psychology and neuroscience literature, covering the characteristics and models of time perception and the related abilities.
Revised:  Firstly, we introduce a brief background from the psychology and neuroscience literature, covering the characteristics and models of time perception and related abilities.
Edit intent: fluency
Original:  We also notice that most models of timing are developed for either sensory timing (i.e. the ability of assessment of an interval) or motor timing (i.e. ability to reproduce an interval).
Revised:  We also notice that most models of timing are developed for either sensory timing (i.e. ability to assess an interval) or motor timing (i.e. ability to reproduce an interval).
Edit intent: clarity
Original:  In this light, we discuss the possible research directions to promote interdisciplinary collaboration for time perception.
Revised:  In this light, we discuss the possible research directions to promote interdisciplinary collaboration in the field of time perception.
Edit intent: clarity
Original: We extend to natural deduction the approach of Linear Nested Sequents and  2-sequents.
Revised: We extend to natural deduction the approach of Linear Nested Sequents and of 2-sequents.
Edit intent: fluency
Original:  Formulas are decorated with a spatial coordinate, which allows a formulation of formal systems in the original spirit of natural deduction---only one introduction and one elimination rule per connective, no additional (structural) rule, no explicit reference to the accessibility relation of the intended Kripke models.
Revised:  Formulas are decorated with a spatial coordinate, which allows a formulation of formal systems in the original spirit of natural deduction -- only one introduction and one elimination rule per connective, no additional (structural) rule, no explicit reference to the accessibility relation of the intended Kripke models.
Edit intent: fluency
Original:  For the intuitionistic versions of the systems, we define proof reduction, and prove proof normalisation , thus obtaining a syntactical proof of consistency.
Revised:  For the intuitionistic versions of the systems, we define proof reduction, and prove proof normalization , thus obtaining a syntactical proof of consistency.
Edit intent: fluency
Original: Spiking Neural Networks (SNNs) offer a novel computational paradigm that captures some of efficiency of biological brains for inference and learning via recursive processing and binary neural activations .
Revised: Spiking Neural Networks (SNNs) capture some of the efficiency of biological brains for inference and learning via recursive processing and binary neural activations .
Edit intent: clarity
Original:  Most existing training algorithms for SNNs assume spiking neuron modelsin which a neuron outputs individual spikes as a function of the dynamics of an internal state variable known as membrane potential.
Revised:  Most existing learning algorithms for SNNs assume spiking neuron modelsin which a neuron outputs individual spikes as a function of the dynamics of an internal state variable known as membrane potential.
Edit intent: clarity
Original:  The derived online learning algorithms follow three-factor rules with global learning signals  . Experimental results on a structured output memorization task and classification task with a standard neuromorphic data set demonstrate significant improvements in terms of accuracy and calibration with an increasing number of compartments .
Revised:  The derived online learning algorithms follow three-factor form with global learning signals . Experimental results on a structured output memorization task and classification task with a standard neuromorphic data set demonstrate significant improvements in terms of accuracy and calibration with an increasing number of compartments .
Edit intent: clarity
Original:  The derived online learning algorithms follow three-factor rules with global learning signals  . Experimental results on a structured output memorization task and classification task with a standard neuromorphic data set demonstrate significant improvements in terms of accuracy and calibration with an increasing number of compartments .
Revised:  The derived online learning algorithms follow three-factor rules with global learning signals . Experimental results on structured output memorization task and classification task with a standard neuromorphic data set demonstrate significant improvements in terms of accuracy and calibration with an increasing number of compartments .
Edit intent: fluency
Original:  The derived online learning algorithms follow three-factor rules with global learning signals  . Experimental results on a structured output memorization task and classification task with a standard neuromorphic data set demonstrate significant improvements in terms of accuracy and calibration with an increasing number of compartments .
Revised:  The derived online learning algorithms follow three-factor rules with global learning signals . Experimental results on a structured output memorization and classification on a standard neuromorphic data set demonstrate significant improvements in terms of accuracy and calibration with an increasing number of compartments .
Edit intent: clarity
Original:  The derived online learning algorithms follow three-factor rules with global learning signals  . Experimental results on a structured output memorization task and classification task with a standard neuromorphic data set demonstrate significant improvements in terms of accuracy and calibration with an increasing number of compartments .
Revised:  The derived online learning algorithms follow three-factor rules with global learning signals . Experimental results on a structured output memorization task and classification task with a standard neuromorphic data set demonstrate significant improvements in terms of log-likelihood, accuracy, and calibration when increasing the number of samples used for inference and training .
Edit intent: clarity
Original: Contact tracing promises to help fight the diffusion of Covid-19 via an early detection of possible sources of further contagion .
Revised: Contact tracing promises to help fight the spread of Covid-19 via an early detection of possible sources of further contagion .
Edit intent: clarity
Original:  In this paper, we relax the smartphone-centric assumption, and propose an IoT-enabled architecture for contact tracing that  enjoys the following features:  i) it reduces the overhead on the end-user to the bare minimum---it requires the mobile phone to only broadcast its beacons;
Revised:  In this paper, we propose an IoT-enabled architecture for contact tracing that enjoys the following features: i) it reduces the overhead on the end-user to the bare minimum---it requires the mobile phone to only broadcast its beacons;
Edit intent: coherence
Original:  In this paper, we relax the smartphone-centric assumption, and propose an IoT-enabled architecture for contact tracing that  enjoys the following features:  i) it reduces the overhead on the end-user to the bare minimum---it requires the mobile phone to only broadcast its beacons;
Revised:  In this paper, we relax the smartphone-centric assumption, and propose an IoT-enabled architecture for contact tracing that relaxes the smartphone-centric assumption, and provide a solution that enjoys the following features: i) it reduces the overhead on the end-user to the bare minimum---it requires the mobile phone to only broadcast its beacons;
Edit intent: clarity
Original:  In this paper, we relax the smartphone-centric assumption, and propose an IoT-enabled architecture for contact tracing that  enjoys the following features:  i) it reduces the overhead on the end-user to the bare minimum---it requires the mobile phone to only broadcast its beacons;
Revised:  In this paper, we relax the smartphone-centric assumption, and propose an IoT-enabled architecture for contact tracing that enjoys the following features: ( i) it reduces the overhead on the end-user to the bare minimum---it requires the mobile phone to only broadcast its beacons;
Edit intent: fluency
Original:  In this paper, we relax the smartphone-centric assumption, and propose an IoT-enabled architecture for contact tracing that  enjoys the following features:  i) it reduces the overhead on the end-user to the bare minimum---it requires the mobile phone to only broadcast its beacons;
Revised:  In this paper, we relax the smartphone-centric assumption, and propose an IoT-enabled architecture for contact tracing that enjoys the following features: i) it reduces the overhead on the end-user to the bare minimum---the mobile device only broadcasts its beacons;
Edit intent: clarity
Original:   ii) it provides the user with a degree of privacy not achieved by competing solutions---even in the most privacy adverse scenario, the solution provides k-anonymity;
Revised:  ( ii) it provides the user with a degree of privacy not achieved by competing solutions---even in the most privacy adverse scenario, the solution provides k-anonymity;
Edit intent: fluency
Original:  and,  iii) it is tunable: the very same architecture can be configured to support several models, ranging from the fully decentralized to the fully centralized ones.
Revised:  and, ( iii) it is tunable: the very same architecture can be configured to support several models, ranging from the fully decentralized to the fully centralized ones.
Edit intent: fluency
Original:  and,  iii) it is tunable: the very same architecture can be configured to support several models, ranging from the fully decentralized to the fully centralized ones.
Revised:  and, iii) it is flexible: the same architecture can be configured to support several models, ranging from the fully decentralized to the fully centralized ones.
Edit intent: clarity
Original:  and,  iii) it is tunable: the very same architecture can be configured to support several models, ranging from the fully decentralized to the fully centralized ones.
Revised:  and, iii) it is tunable: the very same architecture can be configured to support several models---ranging from the fully decentralized to the fully centralized ones.
Edit intent: fluency
Original:  In this paper, we propose an IoT-enabled architecture for contact tracing that relaxes the smartphone-centric assumption, and provide a solution that enjoys the following features: (i) it reduces the overhead on the end-user to the bare minimum---the mobile device only broadcasts its beacons;
Revised:  In this paper, we propose an IoT-enabled architecture for contact tracing that relaxes the smartphone-centric assumption, and provide a solution that enjoys the following features: (i) it reduces the overhead on the end-user to the bare minimum -- the mobile device only broadcasts its beacons;
Edit intent: fluency
Original:  (ii) it provides the user with a degree of privacy not achieved by competing solutions---even in the most privacy adverse scenario, the solution provides k-anonymity;
Revised:  (ii) it provides the user with a degree of privacy not achieved by competing solutions -- even in the most privacy adverse scenario, the solution provides k-anonymity;
Edit intent: fluency
Original:  and, (iii) it is flexible: the same architecture can be configured to support several models---ranging from the fully decentralized to the fully centralized ones---and the system parameters can be tuned to support the tracing of several social interaction models.
Revised:  and, (iii) it is flexible: the same architecture can be configured to support several models -- ranging from the fully decentralized to the fully centralized ones---and the system parameters can be tuned to support the tracing of several social interaction models.
Edit intent: fluency
Original:  and, (iii) it is flexible: the same architecture can be configured to support several models---ranging from the fully decentralized to the fully centralized ones---and the system parameters can be tuned to support the tracing of several social interaction models.
Revised:  and, (iii) it is flexible: the same architecture can be configured to support several models---ranging from the fully decentralized to the fully centralized ones -- and the system parameters can be tuned to support the tracing of several social interaction models.
Edit intent: fluency
Original:  However, due to the privacy-protective noise introduced in the training, the convergence of GANs becomes even more elusive, which often leads to poor utility in the output generator at the end of training.
Revised:  Due to the privacy-protective noise introduced in the training, the convergence of GANs becomes even more elusive, which often leads to poor utility in the output generator at the end of training.
Edit intent: coherence
Original:  Our method leverages the Private Multiplicative Weights method (Hardt and Rothblum, 2010) and the discriminator rejection sampling technique (Azadi et al., 2019) for reweighting generated samples, to obtain high quality synthetic data even in cases where GAN training does not converge .
Revised:  To that end, our method leverages the Private Multiplicative Weights method (Hardt and Rothblum, 2010) and the discriminator rejection sampling technique (Azadi et al., 2019) for reweighting generated samples, to obtain high quality synthetic data even in cases where GAN training does not converge .
Edit intent: coherence
Original:  Our method leverages the Private Multiplicative Weights method (Hardt and Rothblum, 2010) and the discriminator rejection sampling technique (Azadi et al., 2019) for reweighting generated samples, to obtain high quality synthetic data even in cases where GAN training does not converge .
Revised:  Our method leverages the Private Multiplicative Weights method (Hardt and Rothblum, 2010) to reweight generated samples .
Edit intent: coherence