wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The model predicts pitch contours during inference , and generates speech that could be further controlled with predicted contours. FastPitch can thus change the perceived emotional state of the speaker or put emphasis on certain lexical units. We find that uniformly increasing or decreasing the pitch with FastPitch generates speech that resembles the voluntary modulation of voice.
Revised:  The model predicts pitch contours during inference . By altering these predictions, the generated speech can be more expressive, better match the semantic of the utterance, and in the end more engaging to the listener. Uniformly increasing or decreasing the pitch with FastPitch generates speech that resembles the voluntary modulation of voice.
Edit intent: clarity
Original:  It does not introduce an overhead, and FastPitch retains the favorable, fully-parallel Transformer architecture of FastSpeech with a similar speed of mel-scale spectrogram synthesis , orders of magnitude faster than real-time  .
Revised:  It does not introduce an overhead, and FastPitch retains the favorable, fully-parallel Transformer architecture of FastSpeech with a similar speed of mel-scale spectrogram synthesis , orders of magnitude faster than real-time factor for mel-spectrogram synthesis of a typical utterance .
Edit intent: meaning-changed
Original:  The connection leads to (1) probability ratio clipping that regularizes generator training to prevent excessively large updates, and (2) a sample re-weighting mechanism that stabilizes discriminator training by downplaying bad-quality fake samples.
Revised:  The connection leads to (1) probability ratio clipping that regularizes generator training to prevent excessively large updates, and (2) a sample re-weighting mechanism that improves discriminator training by downplaying bad-quality fake samples.
Edit intent: clarity
Original:  We provide theoretical analysis on the convergence of our approach . By plugging the training approach in diverse state-of-the-art GAN architectures, we obtain significantly improved performance over a range of tasks, including text generation, text style transfer, and image generation.
Revised:  Moreover, our variational GAN framework can provably overcome the training issue in many GANs that an optimal discriminator cannot provide any informative gradient to training generator . By plugging the training approach in diverse state-of-the-art GAN architectures, we obtain significantly improved performance over a range of tasks, including text generation, text style transfer, and image generation.
Edit intent: meaning-changed
Original:  Models of contact networks are increasingly used for these purposes , and are often appropriate for infections that spread from person to person . Real-world human contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another.
Revised:  Models of disease transmission through networks of contacts--between individuals or between population centres--are increasingly used for these purposes , and are often appropriate for infections that spread from person to person . Real-world human contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another.
Edit intent: meaning-changed
Original:  Models of contact networks are increasingly used for these purposes , and are often appropriate for infections that spread from person to person . Real-world human contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another.
Revised:  Models of contact networks are increasingly used for these purposes . Real-world human contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another.
Edit intent: coherence
Original:  Models of contact networks are increasingly used for these purposes , and are often appropriate for infections that spread from person to person . Real-world human contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another.
Revised:  Models of contact networks are increasingly used for these purposes , and are often appropriate for infections that spread from person to person . Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another.
Edit intent: clarity
Original:  Using mathematical models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10 %DIFDELCMD < \\%%% %DIF < more than state-of-the-art edge-betweenness methods.
Revised:  Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10 %DIFDELCMD < \\%%% %DIF < more than state-of-the-art edge-betweenness methods.
Edit intent: clarity
Original: The stochastic kinetics of chemical reaction networks can be described by the master equation, which provides the time course evolution of the probability distribution across the discrete state space consisting of vectors of population levels of the interacting  species.
Revised: Quantitative mechanistic models based on reaction networks with stochastic chemical kinetics can help elucidate fundamental biological process where random fluctuations are relevant, such as in single cells. The dynamics of such models is described by the master equation, which provides the time course evolution of the probability distribution across the discrete state space consisting of vectors of population levels of the interacting species.
Edit intent: meaning-changed
Original: The stochastic kinetics of chemical reaction networks can be described by the master equation, which provides the time course evolution of the probability distribution across the discrete state space consisting of vectors of population levels of the interacting  species.
Revised: The stochastic kinetics of chemical reaction networks can be described by the master equation, which provides the time course evolution of the probability distribution across the discrete state space consisting of vectors of population levels of the interacting biochemical species.
Edit intent: meaning-changed
Original: We study the Voronoi diagrams of a finite set of Cauchy distributions and their dual complexes from the viewpoint of information geometry by considering the Fisher-Rao distance, the Kullback-Leibler divergence, the chi square divergence, and a flat divergence derived from Tsallis  entropy related to the conformal flattening of the Fisher-Rao  geometry.
Revised: We study the Voronoi diagrams of a finite set of Cauchy distributions and their dual complexes from the viewpoint of information geometry by considering the Fisher-Rao distance, the Kullback-Leibler divergence, the chi square divergence, and a flat divergence derived from Tsallis ' quadratic entropy related to the conformal flattening of the Fisher-Rao geometry.
Edit intent: meaning-changed
Original: We study the Voronoi diagrams of a finite set of Cauchy distributions and their dual complexes from the viewpoint of information geometry by considering the Fisher-Rao distance, the Kullback-Leibler divergence, the chi square divergence, and a flat divergence derived from Tsallis  entropy related to the conformal flattening of the Fisher-Rao  geometry.
Revised: We study the Voronoi diagrams of a finite set of Cauchy distributions and their dual complexes from the viewpoint of information geometry by considering the Fisher-Rao distance, the Kullback-Leibler divergence, the chi square divergence, and a flat divergence derived from Tsallis entropy related to the conformal flattening of the Fisher-Rao curved geometry.
Edit intent: meaning-changed
Original:  The primal Bregman Voronoi diagram is the Euclidean Voronoi diagram and the dual Bregman Voronoi diagram coincides with the Cauchy hyperbolic Voronoi diagram.
Revised:  The primal Bregman-Tsallis Voronoi diagram corresponds to the hyperbolic Voronoi diagram and the dual Bregman Voronoi diagram coincides with the Cauchy hyperbolic Voronoi diagram.
Edit intent: meaning-changed
Original:  The primal Bregman Voronoi diagram is the Euclidean Voronoi diagram and the dual Bregman Voronoi diagram coincides with the Cauchy hyperbolic Voronoi diagram.
Revised:  The primal Bregman Voronoi diagram is the Euclidean Voronoi diagram and the dual Bregman-Tsallis Voronoi diagram coincides with the Cauchy hyperbolic Voronoi diagram.
Edit intent: meaning-changed
Original:  The primal Bregman Voronoi diagram is the Euclidean Voronoi diagram and the dual Bregman Voronoi diagram coincides with the Cauchy hyperbolic Voronoi diagram.
Revised:  The primal Bregman Voronoi diagram is the Euclidean Voronoi diagram and the dual Bregman Voronoi diagram coincides with the ordinary Euclidean Voronoi diagram.
Edit intent: meaning-changed
Original:  While it can be shown that the approximation error converges to zero if all four parameters are sent to infinity in the right order, we demonstrate in this paper that stochastic gradient descent fails to converge for rectified linear unit networks if their depth is much larger than their width and the number of random initializations does not increase to infinity fast enough.
Revised:  While it can be shown that the approximation error converges to zero if all four parameters are sent to infinity in the right order, we demonstrate in this paper that stochastic gradient descent fails to converge for ReLU networks if their depth is much larger than their width and the number of random initializations does not increase to infinity fast enough.
Edit intent: meaning-changed
Original:  The resulting rules have a 3-factor Hebbian structure: they require pre- and post-synaptic firing rates and a global error signal - the third factor - that can be supplied by a neuromodulator.
Revised:  The resulting rules have a 3-factor Hebbian structure: they require pre- and post-synaptic firing rates and an error signal - the third factor - that can be supplied by a neuromodulator.
Edit intent: clarity
Original:  The resulting rules have a 3-factor Hebbian structure: they require pre- and post-synaptic firing rates and a global error signal - the third factor - that can be supplied by a neuromodulator. Moreover, they do not require precise labels;
Revised:  The resulting rules have a 3-factor Hebbian structure: they require pre- and post-synaptic firing rates and a global error signal - the third factor - consisting of a global teaching signal and a layer-specific term, both available without a top-down pass. They do not require precise labels;
Edit intent: meaning-changed
Original: Spiking neural networks are nature's solution for parallel information processing with high temporal precision at a low metabolic energy cost. To that end , biological neurons integrate inputs as an analog sum and communicate their outputs digitally as spikes, i.e., sparse binary events in time.
Revised: Spiking neural networks are nature's solution for parallel information processing with high temporal precision at a low metabolic cost , biological neurons integrate inputs as an analog sum and communicate their outputs digitally as spikes, i.e., sparse binary events in time.
Edit intent: clarity
Original:  Nevertheless, training spiking neural networks with sparse activity on hardware devices remains a major challenge . Primarily this is due to the lack of suitable training methods that take into account device-specific imperfections and operate at the level of individual spikes instead of firing rates.
Revised:  Nevertheless, emulating high-performing spiking networks on such hardware remains a significant challenge due to device-mismatch and the lack of suitable training methods that take into account device-specific imperfections and operate at the level of individual spikes instead of firing rates.
Edit intent: clarity
Original:  Nevertheless, training spiking neural networks with sparse activity on hardware devices remains a major challenge . Primarily this is due to the lack of suitable training methods that take into account device-specific imperfections and operate at the level of individual spikes instead of firing rates. To tackle this issue, we developed a hardware-in-the-loop strategy to train multi-layer spiking networks using surrogate gradients on the analog BrainScales-2 chip.
Revised:  Nevertheless, training spiking neural networks with sparse activity on hardware devices remains a major challenge . Primarily this is due to the lack of efficient training algorithms. Here, we introduce a general in-the-loop learning framework that resolves these issues. Using the BrainScales-2 chip.
Edit intent: clarity
Original:  To tackle this issue, we developed a hardware-in-the-loop strategy to train multi-layer spiking networks using surrogate gradients on the analog BrainScales-2 chip. Specifically, we used the hardware to compute the forward pass of the network, while the backward pass was computed in software. We evaluated our approach on downscaled 16x16 versions of the MNIST and the fashion MNIST datasets in which spike latencies encoded pixel intensities. The analog neuromorphic substrate closely matched the performance of equivalently sized networks implemented in software. It is capable of processing 70 k patterns per second with a power consumption of less than 300 mW. Added activity regularization resulted in sparse network activity withabout 20 spikes per input, at little to no reduction in classification performance. Thus, overall, our work demonstrates low-energy spiking network processing on an analog neuromorphic substrate and sets several new benchmarks for hardware systems in terms of classification accuracy, processing speed, and efficiency.
Revised:  To tackle this issue, we developed a hardware-in-the-loop strategy to train multi-layer spiking networks using surrogate gradients on the analog BrainScales-2 neuromorphic system, we show that learning self-corrects for device mismatch resulting in competitive spiking network performance on vision and speech benchmarks. Our networks display sparse spiking activity with, on average, far less than one spike per hidden neuron, perform inference at rates of up to 85 k frames/second, and consume less than 200 mW. In summary, our work sets several new benchmarks for low-energy spiking network processing on an analog neuromorphic substrate and sets several new benchmarks for hardware systems in terms of classification accuracy, processing speed, and efficiency.
Edit intent: meaning-changed
Original:  Thus, overall, our work demonstrates low-energy spiking network processing on an analog neuromorphic substrate and sets several new benchmarks for hardware systems in terms of classification accuracy, processing speed, and efficiency. Importantly, our work emphasizes the value of hardware-in-the-loop training and paves the way toward energy-efficient information processing on non-von-Neumann architectures .
Revised:  Thus, overall, our work demonstrates low-energy spiking network processing on analog neuromorphic substrates and constitutes an important step toward on-chip learning .
Edit intent: clarity
Original:  Using the BrainScales-2 neuromorphic system, we show that learning self-corrects for device mismatch resulting in competitive spiking network performance on  vision and speech benchmarks.
Revised:  Using the BrainScaleS-2 neuromorphic system, we show that learning self-corrects for device mismatch resulting in competitive spiking network performance on vision and speech benchmarks.
Edit intent: fluency
Original:  Using the BrainScales-2 neuromorphic system, we show that learning self-corrects for device mismatch resulting in competitive spiking network performance on  vision and speech benchmarks.
Revised:  Using the BrainScales-2 neuromorphic system, we show that learning self-corrects for device mismatch resulting in competitive spiking network performance on both vision and speech benchmarks.
Edit intent: clarity
Original:  Our networks display sparse spiking activity with, on average, far less than one spike per hidden neuron  , perform inference at rates of up to 85 k frames/second, and consume less than 200 mW.
Revised:  Our networks display sparse spiking activity with, on average, far less than one spike per hidden neuron and input , perform inference at rates of up to 85 k frames/second, and consume less than 200 mW.
Edit intent: meaning-changed
Original:  We investigate how agents who never change their minds ("stubborn") can influence the opinion of a social group and foster the formation of polarised communities. We study the voter model in which users are divided in two camps and repeatedly update their opinions based on others they connect with.
Revised:  We investigate how agents who never change their minds ("stubborn") can influence the opinion of a social group . This is done by means of the voter model in which users are divided in two camps and repeatedly update their opinions based on others they connect with.
Edit intent: clarity
Original: We address the phenomenon of sedimentation of opinions in networks and investigate how agents who never change their minds ("stubborn") can influence the opinion of a social group. This is done by means of the voter model in which users are divided in two camps and repeatedly update their opinions based on others they connect with. Assuming a proportion of the agents are stubborn , the distribution of opinions reaches an equilibrium .
Revised: We study the voter model for opinion dynamics on a complete graph with stubborn nodes .
Edit intent: clarity
Original:  We give novel formulas based on Markov Chain analysis to compute the distribution of opinions at any time and speed of convergence to stationary equilibrium . Theoretical results are supported by numerical experiments on synthetic data, and as an example of application we discuss a strategy to mitigate the polarisation phenomenon .
Revised:  We give novel formulas based on Markov Chain analysis to compute the distribution of opinions at any time and speed of convergence to stationary equilibrium .
Edit intent: coherence
Original: We study the voter model for opinion dynamics on a complete graph withstubborn nodes.
Revised: We address the phenomenon of sedimentation of opinions in networks and investigate how stubborn agents who never change their minds ("zealots") can influence the opinion of a social group. This is done via the voter model in which users are divided in two camps and repeatedly update their opinions based on others they connect with. Assuming zealots are present on both sides, the distribution of opinions reaches an equilibrium.
Edit intent: meaning-changed
Original:  We give novel formulas based on Markov Chain analysis to compute the distribution of opinions at any time and speed of convergence to stationary equilibrium  .
Revised:  We give novel formulas based on Markov Chain analysis to compute the distribution of opinions at any time and speed of convergence to stationary equilibrium . As an example of application we discuss a strategy to mitigate the polarisation of opinions in the presence of a backfire effect. Theoretical results are supported by numerical experiments on synthetic data .
Edit intent: meaning-changed
Original:  While the  staying-at-home  policy makes face-to-face interactions and interviews challenging, analysing real-time Twitter data that reflects public opinion toward policy measures is a cost-effective way to access public sentiment.
Revised:  While the ' staying-at-home policy makes face-to-face interactions and interviews challenging, analysing real-time Twitter data that reflects public opinion toward policy measures is a cost-effective way to access public sentiment.
Edit intent: fluency
Original:  While the  staying-at-home  policy makes face-to-face interactions and interviews challenging, analysing real-time Twitter data that reflects public opinion toward policy measures is a cost-effective way to access public sentiment.
Revised:  While the staying-at-home ' policy makes face-to-face interactions and interviews challenging, analysing real-time Twitter data that reflects public opinion toward policy measures is a cost-effective way to access public sentiment.
Edit intent: fluency
Original:  This study proposes two innovative solutions to address the complexities of a collaboration between a human and multiple reinforcement learning (RL)-based agents (referred to thereafter as Human-MARL teaming) where the goals pursued cannot be achieved by a human alone or agents alone. The first innovation is the introduction of a new open-source MARL framework, called COGMENT, to unite humans and agents in real-time complex dynamic systems and efficiently leverage their interactions as a source of learning.
Revised:  This study proposes a new open-source MARL framework, called COGMENT, to unite humans and agents in real-time complex dynamic systems and efficiently leverage their interactions as a source of learning.
Edit intent: coherence
Original:  We demonstrate these innovations by using a designed real-time environment with unmanned aerial vehicles driven by RL agents, collaborating with a human to fight fires. The team of RL agent drones autonomously look for fire seats and the human pilot douses the fires. The results of this study show that the proposed collaborative paradigm and the open-source framework leads to significant reductions in both human effort and exploration costs .
Revised:  We demonstrate these innovations by using a designed real-time environment with unmanned aerial vehicles driven by RL agents, collaborating with a human . The results of this study show that the proposed collaborative paradigm and the open-source framework leads to significant reductions in both human effort and exploration costs .
Edit intent: coherence
Original:  The results of this study show that the proposed collaborative paradigm and the open-source framework leads to significant reductions in both human effort and exploration costs . Also, the results of the proposed hybrid MARL method shows that it effectively improves the learning process to achieve more reliable Q-values for each action, by decoupling the estimation between state value and advantage value .
Revised:  The results of this study show that the proposed collaborative paradigm and the open-source framework leads to significant reductions in both human effort and exploration costs .
Edit intent: coherence
Original:  This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the 'digital divide' . In this paper we present a  method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia.
Revised:  This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the `digital divide` . In this paper we present a method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia.
Edit intent: fluency