wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We use random matrix theory (RMT) to analytically characterize the spectrum of the Jacobian and show how gating produces slow modes and marginal stability . Thus, gating is a potential mechanism to implement computations involving line attractor dynamics .
Revised:  We use random matrix theory (RMT) to analytically characterize the spectrum of the Jacobian and show how gating robustly produces marginal stability and line attractors -- important mechanisms for biologically-relevant computations requiring long memory .
Edit intent: meaning-changed
Original:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide an analytical prediction for the maximum Lyapunov exponent .
Revised:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide a novel analytical expression for the maximum Lyapunov exponent .
Edit intent: clarity
Original:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide an analytical prediction for the maximum Lyapunov exponent . We also show that gating gives rise to a novel, discontinuous transition to chaos, where the proliferation of critical points is decoupled with the appearance of chaotic dynamics ;
Revised:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide an analytical prediction for the maximum Lyapunov exponent demonstrating its close relation to relaxation time of the dynamics. Gating is also shown to give rise to a novel, discontinuous transition to chaos, where the proliferation of critical points is decoupled with the appearance of chaotic dynamics ;
Edit intent: meaning-changed
Original:  We also show that gating gives rise to a novel, discontinuous transition to chaos, where the proliferation of critical points is decoupled with the appearance of chaotic dynamics ; the nature of this chaotic state is characterized in detail. Using the DMFT and RMT, we produce phase diagramsfor gated RNN . Finally, we address the gradients by leveraging the adjoint sensitivity framework to develop a DMFT for the gradients.
Revised:  We also show that gating gives rise to a novel, discontinuous transition to chaos, where the proliferation of critical points is decoupled with the appearance of chaotic dynamics (dynamical complexity), contrary to a seminal result for additive RNNs. Critical surfaces and regions of marginal stability in the parameter space are indicated in phase diagrams, thus providing a map for principled parameter choices for ML practitioners . Finally, we address the gradients by leveraging the adjoint sensitivity framework to develop a DMFT for the gradients.
Edit intent: meaning-changed
Original:  Using the DMFT and RMT, we produce phase diagramsfor gated RNN . Finally, we address the gradients by leveraging the adjoint sensitivity framework to develop a DMFT for the gradients.
Revised:  Using the DMFT and RMT, we produce phase diagramsfor gated RNN . Finally, we develop a field-theory for gradients that arise in training, by incorporating the adjoint sensitivity framework to develop a DMFT for the gradients.
Edit intent: meaning-changed
Original:  Using the DMFT and RMT, we produce phase diagramsfor gated RNN . Finally, we address the gradients by leveraging the adjoint sensitivity framework to develop a DMFT for the gradients. The theory developed here sheds light on the rich dynamical behaviour produced by gating interactions and has implications for architectural choices and learning dynamics .
Revised:  Using the DMFT and RMT, we produce phase diagramsfor gated RNN . Finally, we address the gradients by leveraging the adjoint sensitivity framework from control theory in the DMFT. This paves the way for the use of powerful field-theoretic techniques to study training/gradients in large RNNs .
Edit intent: meaning-changed
Original: Recurrent neural networks (RNNs) are powerful dynamical models, widely used in machine learning (ML) for processing sequential data, and also in neuroscience, to understand the emergent properties of networks of real neurons.
Revised: Recurrent neural networks (RNNs) are powerful dynamical models, widely used in machine learning (ML) for processing sequential data, and in neuroscience, to understand the emergent properties of networks of real neurons.
Edit intent: coherence
Original:  We use random matrix theory to show how gating robustly produces marginal stability and line attractors -- important mechanisms for biologically-relevant computations requiring long memory. The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide a novel analytical expression for the maximum Lyapunov exponent demonstrating its close relation to relaxation time of the dynamics.
Revised:  We show that gating leads to slow modes and a novel, marginally-stable state. The network in this marginally-stable state can function as a robust integrator, and unlike previous approaches, gating permits this function without parameter fine-tuning or special symmetries. We study the long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide a novel analytical expression for the maximum Lyapunov exponent demonstrating its close relation to relaxation time of the dynamics.
Edit intent: meaning-changed
Original:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide a novel analytical expression for the maximum Lyapunov exponent demonstrating its close relation to relaxation time of the dynamics.
Revised:  The long-time behavior of the gated network using its Lyapunov spectrum, and the DMFT is used to provide a novel analytical expression for the maximum Lyapunov exponent demonstrating its close relation to relaxation time of the dynamics.
Edit intent: clarity
Original:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide a novel analytical expression for the maximum Lyapunov exponent demonstrating its close relation to relaxation time of the dynamics.
Revised:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and provide a novel analytical expression for the maximum Lyapunov exponent demonstrating its close relation to relaxation time of the dynamics.
Edit intent: coherence
Original:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide a novel analytical expression for the maximum Lyapunov exponent demonstrating its close relation to relaxation time of the dynamics.
Revised:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide a novel relation between the maximum Lyapunov exponent demonstrating its close relation to relaxation time of the dynamics.
Edit intent: clarity
Original:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide a novel analytical expression for the maximum Lyapunov exponent demonstrating its close relation to relaxation time of the dynamics.
Revised:  The long-time behavior of the gated network is studied using its Lyapunov spectrum, and the DMFT is used to provide a novel analytical expression for the maximum Lyapunov exponent and the relaxation time of the dynamics.
Edit intent: clarity
Original:  Gating is also shown to give rise to a novel, discontinuous transition to chaos, where the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity), contrary to a seminal result for additive RNNs. Critical surfaces and regions of marginal stability in the parameter space are indicated in phase diagrams, thus providing a map for principled parameter choices for ML practitioners.
Revised:  Gating is also shown to give rise to a novel, discontinuous transition to chaos, where the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity), in contrast to a seminal result for additive RNNs. Critical surfaces and regions of marginal stability in the parameter space are indicated in phase diagrams, thus providing a map for principled parameter choices for ML practitioners.
Edit intent: coherence
Original:  Gating is also shown to give rise to a novel, discontinuous transition to chaos, where the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity), contrary to a seminal result for additive RNNs. Critical surfaces and regions of marginal stability in the parameter space are indicated in phase diagrams, thus providing a map for principled parameter choices for ML practitioners.
Revised:  Gating is also shown to give rise to a novel, discontinuous transition to chaos, where the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity), contrary to a seminal result for additive RNNs. The rich dynamical behavior is summarized in a phase diagram indicating critical surfaces and regions of marginal stability in the parameter space are indicated in phase diagrams, thus providing a map for principled parameter choices for ML practitioners.
Edit intent: meaning-changed
Original:  Gating is also shown to give rise to a novel, discontinuous transition to chaos, where the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity), contrary to a seminal result for additive RNNs. Critical surfaces and regions of marginal stability in the parameter space are indicated in phase diagrams, thus providing a map for principled parameter choices for ML practitioners.
Revised:  Gating is also shown to give rise to a novel, discontinuous transition to chaos, where the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity), contrary to a seminal result for additive RNNs. Critical surfaces and regions of marginal stability -- thus, providing a map for principled parameter choices for ML practitioners.
Edit intent: coherence
Original:  Gating is also shown to give rise to a novel, discontinuous transition to chaos, where the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity), contrary to a seminal result for additive RNNs. Critical surfaces and regions of marginal stability in the parameter space are indicated in phase diagrams, thus providing a map for principled parameter choices for ML practitioners.
Revised:  Gating is also shown to give rise to a novel, discontinuous transition to chaos, where the proliferation of critical points (topological complexity) is decoupled from the appearance of chaotic dynamics (dynamical complexity), contrary to a seminal result for additive RNNs. Critical surfaces and regions of marginal stability in the parameter space are indicated in phase diagrams, thus providing a map for principled parameter choices to ML practitioners.
Edit intent: fluency
Original:  Finally, we develop a field-theory for gradients that arise in training, by incorporating the adjoint sensitivity framework from control theory in the DMFT . This paves the way for the use of powerful field-theoretic techniques to study training / gradients in large RNNs.
Revised:  Finally, we develop a field theory for gradients that arise in training, by incorporating the adjoint sensitivity framework from control theory in the DMFT . This paves the way for the use of powerful field-theoretic techniques to study training / gradients in large RNNs.
Edit intent: fluency
Original:  Finally, we develop a field-theory for gradients that arise in training, by incorporating the adjoint sensitivity framework from control theory in the DMFT . This paves the way for the use of powerful field-theoretic techniques to study training / gradients in large RNNs.
Revised:  Finally, we develop a field-theory for gradients that arise in training, by combining the adjoint formalism from control theory in the DMFT . This paves the way for the use of powerful field-theoretic techniques to study training / gradients in large RNNs.
Edit intent: clarity
Original:  Finally, we develop a field-theory for gradients that arise in training, by incorporating the adjoint sensitivity framework from control theory in the DMFT . This paves the way for the use of powerful field-theoretic techniques to study training / gradients in large RNNs.
Revised:  Finally, we develop a field-theory for gradients that arise in training, by incorporating the adjoint sensitivity framework from control theory with the dynamical mean-field theory . This paves the way for the use of powerful field-theoretic techniques to study training / gradients in large RNNs.
Edit intent: meaning-changed
Original:  Finally, we develop a field-theory for gradients that arise in training, by incorporating the adjoint sensitivity framework from control theory in the DMFT . This paves the way for the use of powerful field-theoretic techniques to study training / gradients in large RNNs.
Revised:  Finally, we develop a field-theory for gradients that arise in training, by incorporating the adjoint sensitivity framework from control theory in the DMFT . This paves the way for the use of powerful field theoretic techniques to study training / gradients in large RNNs.
Edit intent: fluency
Original:  Finally, we develop a field-theory for gradients that arise in training, by incorporating the adjoint sensitivity framework from control theory in the DMFT . This paves the way for the use of powerful field-theoretic techniques to study training / gradients in large RNNs.
Revised:  Finally, we develop a field-theory for gradients that arise in training, by incorporating the adjoint sensitivity framework from control theory in the DMFT . This paves the way for the use of powerful field-theoretic techniques to study training and gradients in large RNNs.
Edit intent: fluency
Original: This work constructs, analyzes, and simulates a new  SEIR-type model for the dynamics and potential control of the current coronavirus ( COVID-19 ) pandemic. It has a compartmental structure and a differential inclusion for a variable infection rate.
Revised: This work constructs, analyzes, and simulates a new SEIR-type model for the dynamics and potential control of the current coronavirus ( COVID-19 pandemic.
Edit intent: coherence
Original:  First, the population is separated, according to compliance with the disease control directives (  shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile .
Revised:  First, the population is divided according to its compliance with disease control directives ( shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile .
Edit intent: clarity
Original:  First, the population is separated, according to compliance with the disease control directives (  shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile .
Revised:  First, the population is separated, according to compliance with the disease control directives ( lockdown, shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile .
Edit intent: meaning-changed
Original:  First, the population is separated, according to compliance with the disease control directives (  shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile .
Revised:  First, the population is separated, according to compliance with the disease control directives ( shelter-in-place, masks/face coverings, physical distancing, etc.) into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile .
Edit intent: fluency
Original:  First, the population is separated, according to compliance with the disease control directives (  shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile .
Revised:  First, the population is separated, according to compliance with the disease control directives ( shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully comply and those who only partially comply with the directives or are necessarily mobile .
Edit intent: clarity
Original:  First, the population is separated, according to compliance with the disease control directives (  shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile .
Revised:  First, the population is separated, according to compliance with the disease control directives ( shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully follow the directives and those who follow the directives partially, or are necessarily mobile .
Edit intent: clarity
Original:  First, the population is separated, according to compliance with the disease control directives (  shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile . This allows the assessment of the overall effectiveness of the control measures and the impact of their relaxing or tightening on the disease spread.
Revised:  First, the population is separated, according to compliance with the disease control directives ( shelter-in-place, masks/face coverings, physical distancing, etc.) , into those who fully follow the directives and those who only partially comply with the directives or are necessarily mobile (such as medical staff). This split, indirectly, reflects on the quality and consistency of these measures. This allows the assessment of the overall effectiveness of the control measures and the impact of their relaxing or tightening on the disease spread.
Edit intent: meaning-changed
Original: The simulations show that the model captures  the pandemic dynamics in South Korea accurately , which provides confidence in the model predictions and its future use  .
Revised: The simulations show that the model captures the pandemic dynamics in South Korea accurately , which provides confidence in the model predictions and its future use . In particular, the model predicts that about 40\% of the infections were not documented, which implies that asymptomatic infections contribute silently but substantially to the spread of the disease indicating that more widespread asymptomatic testing is necessary .
Edit intent: meaning-changed
Original: COVID-19 was first detected in Hubei province of China and has had severe impact on the life in the country since then. We investigate how this epidemic has influenced attention dynamics on the biggest Chinese microblogging website Sina Weibo in the period December 16, 2019 - April 17, 2020.
Revised: COVID-19 was first detected in Hubei province of China and has had severe impact on the attention dynamics on the biggest Chinese microblogging website Sina Weibo in the period December 16, 2019 - April 17, 2020.
Edit intent: clarity
Original:  We investigate how this epidemic has influenced attention dynamics on the biggest Chinese microblogging website Sina Weibo in the period December 16, 2019 - April 17, 2020. We focus on the real-time Hot Search List (HSL), which provides the ranking of the most popular 50 hashtags based on the amount of Sina Weibo searches on them .
Revised:  We investigate how this epidemic has influenced attention dynamics on the biggest Chinese microblogging website Sina Weibo during the first four months of the pandemic. We study the real-time Hot Search List (HSL), which provides the ranking of the most popular 50 hashtags based on the amount of Sina Weibo searches on them .
Edit intent: meaning-changed
Original:  We show , how the specific events, measures and developments during the epidemic affected the emergence of new hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced.
Revised:  We show how the specific events, measures and developments during the epidemic affected the emergence of new hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced.
Edit intent: fluency
Original:  We show , how the specific events, measures and developments during the epidemic affected the emergence of new hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced.
Revised:  We show , how the specific events, measures and developments during the epidemic affected the emergence of different kinds of hashtags and the ranking on the HSL. A significant increase of COVID-19 related hashtags started to occur on HSL around January 20, 2020, when the transmission of the disease between humans was announced.
Edit intent: clarity
Original:  To quantify the dynamics of HSL we measured the lifetimes of hashtags on the list and the rank diversity at given ranks. Our observations indicate attention diversification since the COVID-19 outbreak in Mainland China and a higher rank diversity at the top 15 ranks on HSL due to the COVID-19 related hashtags, a drastic attention decay shortly after the outburst and a slower decay for a longer period .
Revised:  To quantify the dynamics of HSL we measured the lifetimes of hashtags on the list . This way we can obtain information about the decay of attention, which is important for decisions about the temporal placement of governmental measures to achieve permanent awareness. Furthermore, our observations indicate abnormally higher rank diversity at the top 15 ranks on HSL due to the COVID-19 related hashtags, a drastic attention decay shortly after the outburst and a slower decay for a longer period .
Edit intent: clarity
Original:  Our observations indicate attention diversification since the COVID-19 outbreak in Mainland China and a higher rank diversity at the top 15 ranks on HSL due to the COVID-19 related hashtags, a drastic attention decay shortly after the outburst and a slower decay for a longer period .
Revised:  Our observations indicate attention diversification since the COVID-19 outbreak in Mainland China and a higher rank diversity in the top 15 ranks on HSL due to the COVID-19 related hashtags, a drastic attention decay shortly after the outburst and a slower decay for a longer period .
Edit intent: fluency