wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The results of sentiment analysis indicate that 45.7  express positive sentiment towards Trump in Twitter and 33.8  positive sentiment towards Biden, while 14.55  of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7  positive sentiment towards Biden  .
Revised:  The results of sentiment analysis indicate that 45.7 \% express positive sentiment towards Trump in Twitter and 33.8 positive sentiment towards Biden, while 14.55 of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7 positive sentiment towards Biden .
Edit intent: meaning-changed
Original:  The results of sentiment analysis indicate that 45.7  express positive sentiment towards Trump in Twitter and 33.8  positive sentiment towards Biden, while 14.55  of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7  positive sentiment towards Biden  .
Revised:  The results of sentiment analysis indicate that 45.7 express positive sentiment towards Trump in Twitter and 33.8 \% positive sentiment towards Biden, while 14.55 of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7 positive sentiment towards Biden .
Edit intent: meaning-changed
Original:  The results of sentiment analysis indicate that 45.7  express positive sentiment towards Trump in Twitter and 33.8  positive sentiment towards Biden, while 14.55  of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7  positive sentiment towards Biden  .
Revised:  The results of sentiment analysis indicate that 45.7 express positive sentiment towards Trump in Twitter and 33.8 positive sentiment towards Biden, while 14.55 \% of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7 positive sentiment towards Biden .
Edit intent: clarity
Original:  The results of sentiment analysis indicate that 45.7  express positive sentiment towards Trump in Twitter and 33.8  positive sentiment towards Biden, while 14.55  of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7  positive sentiment towards Biden  .
Revised:  The results of sentiment analysis indicate that 45.7 express positive sentiment towards Trump in Twitter and 33.8 positive sentiment towards Biden, while 14.55 of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7 \% positive sentiment towards Biden .
Edit intent: meaning-changed
Original:  The results of sentiment analysis indicate that 45.7  express positive sentiment towards Trump in Twitter and 33.8  positive sentiment towards Biden, while 14.55  of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7  positive sentiment towards Biden  .
Revised:  The results of sentiment analysis indicate that 45.7 express positive sentiment towards Trump in Twitter and 33.8 positive sentiment towards Biden, while 14.55 of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7 positive sentiment towards Biden . Our analysis fill the gap between the connection of offline events and their consequences in social media by monitoring important events in real world and measuring public volume and sentiment before and after the event in social media .
Edit intent: meaning-changed
Original:  Particularly, we study the daily traffic per prevalent hashtags and show the evolution of the retweet graph from July to September 2020, highlighting the two main entities ('Biden' and 'Trump') contained in our dataset .
Revised:  Particularly, we study the daily traffic per prevalent hashtags , plot the retweet graph from July to September 2020, highlighting the two main entities ('Biden' and 'Trump') contained in our dataset .
Edit intent: clarity
Original:  Particularly, we study the daily traffic per prevalent hashtags and show the evolution of the retweet graph from July to September 2020, highlighting the two main entities ('Biden' and 'Trump') contained in our dataset .
Revised:  Particularly, we study the daily traffic per prevalent hashtags and show the evolution of the retweet graph from July to September 2020, show how its main connected component becomes denser in the period closer to the elections and highlight the two main entities ('Biden' and 'Trump') contained in our dataset .
Edit intent: meaning-changed
Original:  Particularly, we study the daily traffic per prevalent hashtags and show the evolution of the retweet graph from July to September 2020, highlighting the two main entities ('Biden' and 'Trump') contained in our dataset .
Revised:  Particularly, we study the daily traffic per prevalent hashtags and show the evolution of the retweet graph from July to September 2020, highlighting the two main entities ('Biden' and 'Trump') .
Edit intent: clarity
Original:  In particular, this work addresses lifelong MAPF (LMAPF) -- an online variant of the problem where agents are immediately assigned a new goal upon reaching their current one -- in dense and highly structured environments, typical of real-world warehouses operations.
Revised:  In particular, this work addresses lifelong MAPF (LMAPF) - an online variant of the problem where agents are immediately assigned a new goal upon reaching their current one -- in dense and highly structured environments, typical of real-world warehouses operations.
Edit intent: fluency
Original:  In particular, this work addresses lifelong MAPF (LMAPF) -- an online variant of the problem where agents are immediately assigned a new goal upon reaching their current one -- in dense and highly structured environments, typical of real-world warehouses operations.
Revised:  In particular, this work addresses lifelong MAPF (LMAPF) -- an online variant of the problem where agents are immediately assigned a new goal upon reaching their current one - in dense and highly structured environments, typical of real-world warehouses operations.
Edit intent: fluency
Original:  In particular, this work addresses lifelong MAPF (LMAPF) -- an online variant of the problem where agents are immediately assigned a new goal upon reaching their current one -- in dense and highly structured environments, typical of real-world warehouses operations.
Revised:  In particular, this work addresses lifelong MAPF (LMAPF) -- an online variant of the problem where agents are immediately assigned a new goal upon reaching their current one -- in dense and highly structured environments, typical of real-world warehouse operations.
Edit intent: fluency
Original:  We extend our previous work, which was effective in low-density sparsely occupied worlds, to highly structured and constrained worlds by identifying behaviors and conventions which improve implicit agent coordination, and enabling their learning through the construction of a novel local agent observation and various training aids.
Revised:  We extend our previous work, which was effective in low-density sparsely occupied worlds, to highly structured and constrained worlds by identifying behaviors and conventions which improve implicit agent coordination, and enable their learning through the construction of a novel local agent observation and various training aids.
Edit intent: fluency
Original:  We present extensive results of PRIMAL2 in both MAPF and LMAPF environments with up to 1024 agents and compare its performance to complete state-of-the-art planners .
Revised:  We present extensive results of PRIMAL2 in both MAPF and LMAPF environments with up to 1024 agents and compare its performance to state-of-the-art planners .
Edit intent: clarity
Original:  We present extensive results of PRIMAL2 in both MAPF and LMAPF environments with up to 1024 agents and compare its performance to complete state-of-the-art planners . We experimentally observe that agents successfully learn to follow ideal conventions and can exhibit selfless coordinated maneuvers that maximize joint rewards. We find that not only does PRIMAL2 significantly surpass our previous work , it is also able to perform on par and even outperform state-of-the-art planners in terms of throughput .
Revised:  We present extensive results of PRIMAL2 in both MAPF and LMAPF environments with up to 1024 agents and compare its performance to complete state-of-the-art planners in terms of makespan and throughput. We show that PRIMAL2 significantly surpass our previous work , it is also able to perform on par and even outperform state-of-the-art planners in terms of throughput .
Edit intent: clarity
Original:  We find that not only does PRIMAL2 significantly surpass our previous work , it is also able to perform on par and even outperform state-of-the-art planners in terms of throughput .
Revised:  We find that not only does PRIMAL2 significantly surpasses our previous work , it is also able to perform on par and even outperform state-of-the-art planners in terms of throughput .
Edit intent: fluency
Original:  We find that not only does PRIMAL2 significantly surpass our previous work , it is also able to perform on par and even outperform state-of-the-art planners in terms of throughput .
Revised:  We find that not only does PRIMAL2 significantly surpass our previous work and performs comparably to these baselines, while allowing real-time re-planning and scaling up to 2048 agents .
Edit intent: meaning-changed
Original:  In this context, synaptic plasticity refers to the set of mechanisms driving the dynamics of neuronal connections, called synapses {\em  and represented by a scalar value, the synaptic weight.
Revised:  In this context, synaptic plasticity refers to the set of mechanisms driving the dynamics of neuronal connections, called synapses {\em synapses and represented by a scalar value, the synaptic weight.
Edit intent: meaning-changed
Original:  A Spike-Timing Dependent Plasticity (STDP) rule is a biologically-based model representing the time evolution of the synaptic weight as a functional of the past spiking activity of adjacent neurons.
Revised:  Spike-Timing Dependent Plasticity (STDP) rule is a biologically-based model representing the time evolution of the synaptic weight as a functional of the past spiking activity of adjacent neurons.
Edit intent: coherence
Original:  A Spike-Timing Dependent Plasticity (STDP) rule is a biologically-based model representing the time evolution of the synaptic weight as a functional of the past spiking activity of adjacent neurons.
Revised:  A Spike-Timing Dependent Plasticity (STDP) is a biologically-based model representing the time evolution of the synaptic weight as a functional of the past spiking activity of adjacent neurons.
Edit intent: clarity
Original:  In this article, a new, general, mathematical framework to study the phenomenon of synaptic plasticity associated to STDP rules is introduced. A system composed of two neuronal cells connected by a single synapse is investigated and a stochastic process describing its dynamical behavior is presented and analyzed.
Revised:  In this article, a new, general, mathematical framework to study the phenomenon of synaptic plasticity associated to different STDP rules. The system composed of two neuronal cells connected by a single synapse is investigated and a stochastic process describing its dynamical behavior is presented and analyzed.
Edit intent: meaning-changed
Original:  Experiments show that long-term synaptic plasticity evolves on a much slower timescale than the cellular mechanisms driving the activity of neuronal cells. For this reason a scaling model of our stochastic model is also introduced and averaging principles for a sub-class of plasticity kernels are stated, and proved in a companion paper. These results are used to analyze two STDP models widely used in applied physics: Pair-based rules and calcium-based rules. We compare results of computational neuroscience on models of timing-based synaptic plasticity with our results derived from averaging principles .
Revised:  Several aspects of these models are discussed and compared to canonical models of computational neuroscience on models of timing-based synaptic plasticity with our results derived from averaging principles .
Edit intent: clarity
Original:  We compare results of computational neuroscience on models of timing-based synaptic plasticity with our results derived from averaging principles .
Revised:  We compare results of computational neuroscience . An important sub-class of plasticity kernels with a Markovian formulation is also defined and investigated. In these models, the time evolution of cellular processes such as the neuronal membrane potential and the concentrations of chemical components created/suppressed by spiking activity has the Markov property .
Edit intent: meaning-changed
Original: We propose a framework for bundle adjustment (BA) on sparse lidar points and incorporate it to a lidar odometry and mapping (LOAM) to lower the drift. A local BA on a sliding window of keyframes has been widely used in visual SLAM and has proved to be very effective in lowering the drift.
Revised: We propose a framework for bundle adjustment (BA) on a sliding window of keyframes has been widely used in visual SLAM and has proved to be very effective in lowering the drift.
Edit intent: coherence
Original:  Our method is to enforce feature points lie on the same edge or plane by minimizing the eigenvalue of the covariance matrix.
Revised:  Our method is to enforce feature points lie on the same edge or plane . Unlike the visual SLAM (and prior plane adjustment method in lidar SLAM) where the feature has to be co-determined along with the pose, we show that the feature can be analytically solved and removed from the BA, the resultant BA is only dependent on the scan poses. This greatly reduces the optimization scale and allows large-scale dense plane and edge features to be used.
Edit intent: meaning-changed
Original:  In this paper, we study how the Evidence Lower Bound (ELBO) used in Variational Auto-Encoders (VAEs) is affected by the diversity of image samples . Different tasks or setups in visual reinforcement learning incur varying diversity.
Revised:  Observing that the Evidence Lower Bound (ELBO) used in Variational Auto-Encoders (VAEs) is affected by the diversity of image samples . Different tasks or setups in visual reinforcement learning incur varying diversity.
Edit intent: coherence
Original:  In this paper, we study how the Evidence Lower Bound (ELBO) used in Variational Auto-Encoders (VAEs) is affected by the diversity of image samples . Different tasks or setups in visual reinforcement learning incur varying diversity.
Revised:  In this paper, we study how the Evidence Lower Bound (ELBO) used in Variational Auto-Encoders correlates with the diversity of image samples . Different tasks or setups in visual reinforcement learning incur varying diversity.
Edit intent: clarity
Original:  In this paper, we study how the Evidence Lower Bound (ELBO) used in Variational Auto-Encoders (VAEs) is affected by the diversity of image samples . Different tasks or setups in visual reinforcement learning incur varying diversity. We exploit the ELBO to create an auto-tuning technique in self-supervised reinforcement learning.
Revised:  In this paper, we study how the Evidence Lower Bound (ELBO) used in Variational Auto-Encoders (VAEs) is affected by the diversity of image samples , we propose an auto-tuning technique in self-supervised reinforcement learning.
Edit intent: clarity
Original:  We use the state-of-the-art self-supervised robotic learning framework (Reinforcement Learning with Imagined Goals (RIG) using Soft Actor-Critic) as baseline for experimental verification.
Revised:  We use a state-of-the-art self-supervised robotic learning framework (Reinforcement Learning with Imagined Goals (RIG) using Soft Actor-Critic) as baseline for experimental verification.
Edit intent: fluency
Original:  We use the state-of-the-art self-supervised robotic learning framework (Reinforcement Learning with Imagined Goals (RIG) using Soft Actor-Critic) as baseline for experimental verification.
Revised:  We use the state-of-the-art self-supervised robot learning framework (Reinforcement Learning with Imagined Goals (RIG) using Soft Actor-Critic) as baseline for experimental verification.
Edit intent: fluency
Original: As a response to the Great Recession, many central banks resorted to unconventional monetary policies , in the form of a balance sheet expansion. Our research aims at analyzing the impact of the ECB policies on stock market volatility in four Eurozone countries (France, Germany, Italy, and Spain)within the Multiplicative Error Model framework.
Revised: Taking the European Central Bank unconventional policies as a reference, we suggest a class of Multiplicative Error Models (MEM) taylored to analyze the impact such policies have on stock market volatility in four Eurozone countries (France, Germany, Italy, and Spain)within the Multiplicative Error Model framework.
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Original:  Our research aims at analyzing the impact of the ECB policies on stock market volatility in four Eurozone countries (France, Germany, Italy, and Spain)within the Multiplicative Error Model framework. We propose a model that allows us to quantify the part of market volatility depending directly on unconventional policies by distinguishing between the announcement effect and the implementation effect. While we observe an increase in volatility on announcement days , we find a negative implementation effect, which causes a remarkable reduction in volatility in the long term.
Revised:  Our research aims at analyzing the impact of the ECB policies on stock market volatility . The new set of models, called MEM with Asymmetry and Policy effects (MAP), keeps the base volatility dynamics separate from a component reproducing policy effects, with an increase in volatility on announcement days , we find a negative implementation effect, which causes a remarkable reduction in volatility in the long term.
Edit intent: meaning-changed
Original:  While we observe an increase in volatility on announcement days , we find a negative implementation effect, which causes a remarkable reduction in volatility in the long term. A Model Confidence Set approach finds how the forecasting power of the proxy improves significantly after the policy announcement;
Revised:  While we observe an increase in volatility on announcement days and a decrease unfolding implementation effects. When applied to four Eurozone markets, a Model Confidence Set approach finds how the forecasting power of the proxy improves significantly after the policy announcement;
Edit intent: meaning-changed
Original:  A Model Confidence Set approach finds how the forecasting power of the proxy improves significantly after the policy announcement; a multi-step ahead forecasting exercise estimates the duration of the effect, and, by shocking the policy variable, we are able to quantify the reduction in volatility which is more marked for debt-troubled countries.
Revised:  A Model Confidence Set approach finds how the forecasting power of the proxy after the Expanded Asset Purchase Programme implementation; a multi--step ahead forecasting exercise estimates the duration of the effect, and, by shocking the policy variable, we are able to quantify the reduction in volatility which is more marked for debt-troubled countries.
Edit intent: meaning-changed
Original:  a multi-step ahead forecasting exercise estimates the duration of the effect, and, by shocking the policy variable, we are able to quantify the reduction in volatility which is more marked for debt-troubled countries.
Revised:  a multi-step ahead forecasting exercise estimates the duration of the effect, and, by shocking the policy variable, we are able to quantify the reduction in volatility which is more marked for debt--troubled countries.
Edit intent: fluency
Original:  In this paper, we propose a multi-task learning method to incorporate information from syntactic and semantic auxiliary tasks, including negation and speculation scope detection, to create  models that are more robust to these phenomena.
Revised:  In this paper, we propose a multi-task learning method to incorporate information from syntactic and semantic auxiliary tasks, including negation and speculation scope detection, to create English-language models that are more robust to these phenomena.
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Original:  We find that multi-task models and transfer learning from a language model can improve performance on these challenge datasets . However the results indicate that there is still much room for improvement in making our models more robust to linguistic phenomena such as negation and speculation.
Revised:  We find that multi-task models and transfer learning from a language model can improve performance on these challenge datasets , but the overall performances indicate that there is still much room for improvement in making our models more robust to linguistic phenomena such as negation and speculation.
Edit intent: clarity
Original: We study a family of temporal reachability problems under waiting-time restrictions . In particular, given a temporal graph and a set of source vertices, we find the set of vertices that are reachable from a source via a time-respecting path, and such that the  difference in timestamps between consecutive edges is at most a resting time.
Revised: We study a family of temporal reachability problems under waiting-time restrictions in temporal and vertex-colored temporal graphs. Given a temporal graph and a set of source vertices, we find the set of vertices that are reachable from a source via a time-respecting path, and such that the difference in timestamps between consecutive edges is at most a resting time.
Edit intent: meaning-changed
Original:  In particular, given a temporal graph and a set of source vertices, we find the set of vertices that are reachable from a source via a time-respecting path, and such that the  difference in timestamps between consecutive edges is at most a resting time.
Revised:  In particular, given a temporal graph and a set of source vertices, we find the set of vertices that are reachable from a source via a time-respecting path, where the difference in timestamps between consecutive edges is at most a resting time. Given a vertex-colored temporal graph and a multiset query of colors, we find the set of vertices reachable from a source via a time-respecting path such that the difference in timestamps between consecutive edges is at most a resting time.
Edit intent: meaning-changed
Original:  In particular, given a temporal graph and a set of source vertices, we find the set of vertices that are reachable from a source via a time-respecting path, and such that the  difference in timestamps between consecutive edges is at most a resting time.
Revised:  In particular, given a temporal graph and a set of source vertices, we find the set of vertices that are reachable from a source via a time-respecting path, and such that the vertex colors of the path agree with the multiset query and the difference in timestamps between consecutive edges is at most a resting time.
Edit intent: meaning-changed
Original:  This kind of problems have several interesting applications in understanding the spread of a disease in a network, tracing contacts in epidemic outbreaks, and finding signaling pathways in the brain network  .
Revised:  This kind of problems have several interesting applications in understanding the spread of a disease in a network, tracing contacts in epidemic outbreaks, and finding signaling pathways in the brain network , and recommending tours for tourists .
Edit intent: meaning-changed