wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We benchmark our method against standard option pricing models, such as Heston with and without jumps. We validate our method both on training sets, and testing sets, namely, highlighting both their capacity to reproduce observed prices and predict new ones.
Revised:  We benchmark our method against standard IVS models. By evaluating our method on both training sets, and testing sets, namely, highlighting both their capacity to reproduce observed prices and predict new ones.
Edit intent: clarity
Original:  A secondary aim was to investigate whether there is a relationship between alexithymic traits and sensorimotor simulation as a mechanism for fine facial expression discrimination. In order to examine the time-course of face processing, we monitored P1 and N170 components of the event-related potentials (ERP) in participants performing a fine discrimination task of facial expressions .
Revised:  A secondary aim was to investigate whether there is a relationship between alexithymic traits and sensorimotor simulation . We monitored P1 and N170 components of the event-related potentials (ERP) in participants performing a fine discrimination task of facial expressions .
Edit intent: clarity
Original:  In order to examine the time-course of face processing, we monitored P1 and N170 components of the event-related potentials (ERP) in participants performing a fine discrimination task of facial expressions . An animal discrimination task was implemented as a control condition.
Revised:  In order to examine the time-course of face processing, we monitored P1 and N170 components of the event-related potentials (ERP) in participants performing a fine discrimination task of facial expressions while implementing an animal discrimination task was implemented as a control condition.
Edit intent: clarity
Original:  Our results revealed that the P1 ERP component was not modulated by the mimicry manipulation while the N170 amplitude was larger in the blocked mimicry condition when compared to the free mimicry condition selectively for the face stimuli.
Revised:  Our results revealed that , on average, both P1 ERP component was not modulated by the mimicry manipulation while the N170 amplitude was larger in the blocked mimicry condition when compared to the free mimicry condition selectively for the face stimuli.
Edit intent: meaning-changed
Original:  Our results revealed that the P1 ERP component was not modulated by the mimicry manipulation while the N170 amplitude was larger in the blocked mimicry condition when compared to the free mimicry condition selectively for the face stimuli. Interestingly, this modulation interacted with the alexithymic traits, with a reduction of the N170 amplitude modulation as a function of the facial mimicry manipulationfor participants with the highest levels of alexithymic traits.
Revised:  Our results revealed that the P1 and N170 ERP components were not sensitive to mimicry manipulation. However, when taking into account alexithymic traits, with a reduction of the N170 amplitude modulation as a function of the facial mimicry manipulationfor participants with the highest levels of alexithymic traits.
Edit intent: coherence
Original:  Interestingly, this modulation interacted with the alexithymic traits, with a reduction of the N170 amplitude modulation as a function of the facial mimicry manipulationfor participants with the highest levels of alexithymic traits.
Revised:  Interestingly, this modulation interacted with the alexithymic traits, a scenario corroborating sensorimotor simulation models emerged, with two dissociable temporal windows affected by mimicry manipulation as a function of alexithymia levels. Specifically, as a function of the facial mimicry manipulationfor participants with the highest levels of alexithymic traits.
Edit intent: meaning-changed
Original:  Interestingly, this modulation interacted with the alexithymic traits, with a reduction of the N170 amplitude modulation as a function of the facial mimicry manipulationfor participants with the highest levels of alexithymic traits. These results demonstrate that sensorimotor simulation influences visual processing of facial expressions at early stages and that participants with higher alexithymic traits tend to underutilize the sensorimotor simulation system .
Revised:  Interestingly, this modulation interacted with the alexithymic traits, with a reduction of the N170 amplitude modulation as a function of mimicry manipulation, individuals with lower alexithymic traits showed modulations of the P1 amplitude, while individuals with higher alexithymic traits showed modulations of the later N170). Furthermore, connectivity analysis at the scalp level suggested increased connectivity between sensorimotor and extrastriate visual regions in individuals with lower alexithymic traits compared to individuals with higher alexithymic traits. Overall, we interpreted these ERPs modulations as compensative visual processing under conditions of interference on the sensorimotor processing .
Edit intent: meaning-changed
Original:  A new variable stiffness actuator (VSA) is presented, designedfor reproducing the full versatility of human motions from delicate tasks to precise positioning tasks through teleoperation, and highly dynamic tasks like hammering in particular.
Revised: This study is part of research aiming at increasing the range of dynamic tasks for teleoperated field robotics in order to allow operators to use the full range of human motions without being limited by the dynamics of the robotic manipulator. A new variable stiffness actuator (VSA) is presented, designedfor reproducing the full versatility of human motions from delicate tasks to precise positioning tasks through teleoperation, and highly dynamic tasks like hammering in particular.
Edit intent: meaning-changed
Original:  A new variable stiffness actuator (VSA) is presented, designedfor reproducing the full versatility of human motions from delicate tasks to precise positioning tasks through teleoperation, and highly dynamic tasks like hammering in particular.
Revised:  A new variable impedance actuator (VIA) was designed, capable of reproducing motions through teleoperation from precise positioning tasks through teleoperation, and highly dynamic tasks like hammering in particular.
Edit intent: clarity
Original:  A new variable stiffness actuator (VSA) is presented, designedfor reproducing the full versatility of human motions from delicate tasks to precise positioning tasks through teleoperation, and highly dynamic tasks like hammering in particular.
Revised:  A new variable stiffness actuator (VSA) is presented, designedfor reproducing the full versatility of human motions from delicate tasks to precise positioning tasks to highly dynamic tasks like hammering in particular.
Edit intent: clarity
Original:  A new variable stiffness actuator (VSA) is presented, designedfor reproducing the full versatility of human motions from delicate tasks to precise positioning tasks through teleoperation, and highly dynamic tasks like hammering in particular. Existing VSAs show good performance in terms of either quick stiffness changing time, high output velocity, or variable damping, but not in the combination required for highly dynamic tasks. Goal of the presented design is to reach with one system a stiffness changing time of 50 ms, a peak output velocity of 20 rad/s and variable damping , based on the requirements of literature and our previous research.
Revised:  A new variable stiffness actuator (VSA) is presented, designedfor reproducing the full versatility of human motions from delicate tasks to precise positioning tasks through teleoperation, and highly dynamic tasks . The design requirements based on previous human user studies were a stiffness changing time of 50 ms, a peak output velocity of 20 rad/s and variable damping , based on the requirements of literature and our previous research.
Edit intent: meaning-changed
Original:  Goal of the presented design is to reach with one system a stiffness changing time of 50 ms, a peak output velocity of 20 rad/s and variable damping , based on the requirements of literature and our previous research. A prototype was built and its performance measured with three motors in parallel configuration: two responsible for changing the VSAs neutral position and effective stiffness through a lever arm mechanism, and the third acting as variable damper.
Revised:  Goal of the presented design is to reach with one system a stiffness changing time of 50 ms, a peak output velocity of 20 rad/s and variable damping allowing to suppress undesired oscillations. This is a unique combination of features that was not met by other VIAs. The new design has three motors in parallel configuration: two responsible for changing the VSAs neutral position and effective stiffness through a lever arm mechanism, and the third acting as variable damper.
Edit intent: meaning-changed
Original:  A prototype was built and its performance measured with three motors in parallel configuration: two responsible for changing the VSAs neutral position and effective stiffness through a lever arm mechanism, and the third acting as variable damper.
Revised:  A prototype was built and its performance measured with three motors in parallel configuration: two responsible for changing the VIA's neutral position and effective stiffness through a lever arm mechanism, and the third acting as variable damper.
Edit intent: clarity
Original:  A prototype was built and its performance measured with three motors in parallel configuration: two responsible for changing the VSAs neutral position and effective stiffness through a lever arm mechanism, and the third acting as variable damper.
Revised:  A prototype was built and its performance measured with three motors in parallel configuration: two responsible for changing the VSAs neutral position and effective stiffness through a sliding pivot point lever mechanism, and the third acting as variable damper.
Edit intent: clarity
Original:  Its effective stiffness can be changed continuously within 50 ms to 120 ms for small to large stiffness steps, its output velocity is up to 1100 rad/s and its oscillation behavior can be controlled precisely with the variable damper .
Revised:  A prototype was built and its performance measured with an effective stiffness changing time of 50 ms to 120 ms for small to large stiffness steps, its output velocity is up to 1100 rad/s and its oscillation behavior can be controlled precisely with the variable damper .
Edit intent: clarity
Original:  Its effective stiffness can be changed continuously within 50 ms to 120 ms for small to large stiffness steps, its output velocity is up to 1100 rad/s and its oscillation behavior can be controlled precisely with the variable damper .
Revised:  Its effective stiffness can be changed continuously within 50 to 120 ms for small to large stiffness steps, its output velocity is up to 1100 rad/s and its oscillation behavior can be controlled precisely with the variable damper .
Edit intent: fluency
Original:  Its effective stiffness can be changed continuously within 50 ms to 120 ms for small to large stiffness steps, its output velocity is up to 1100 rad/s and its oscillation behavior can be controlled precisely with the variable damper .
Revised:  Its effective stiffness can be changed continuously within 50 ms to 120 ms for small to large stiffness steps, nominal output velocity of 16 rad/s and its oscillation behavior can be controlled precisely with the variable damper .
Edit intent: meaning-changed
Original:  Its effective stiffness can be changed continuously within 50 ms to 120 ms for small to large stiffness steps, its output velocity is up to 1100 rad/s and its oscillation behavior can be controlled precisely with the variable damper .
Revised:  Its effective stiffness can be changed continuously within 50 ms to 120 ms for small to large stiffness steps, its output velocity is up to 1100 rad/s and a variable damper with a damping torque from 0 to 3 Nm .
Edit intent: meaning-changed
Original:  Its effective stiffness range is 0.2 Nm/rad to 313 Nm/rad and its maximum continuous torque 9.4 Nm. This unique combination makes the new actuator  particularly suitable for highly dynamic tasks , while being also very versatile, and makes it especially interesting for teleoperation and human-robot collaboration.
Revised:  Its effective stiffness range is 0.2 to 313 Nm/rad and its maximum continuous torque 9.4 Nm. This unique combination makes the new actuator particularly suitable for highly dynamic tasks , while being also very versatile, and makes it especially interesting for teleoperation and human-robot collaboration.
Edit intent: fluency
Original:  Its effective stiffness range is 0.2 Nm/rad to 313 Nm/rad and its maximum continuous torque 9.4 Nm. This unique combination makes the new actuator  particularly suitable for highly dynamic tasks , while being also very versatile, and makes it especially interesting for teleoperation and human-robot collaboration.
Revised:  Its effective stiffness range is 0.2 Nm/rad to 313 Nm/rad . This concludes that the new actuator particularly suitable for highly dynamic tasks , while being also very versatile, and makes it especially interesting for teleoperation and human-robot collaboration.
Edit intent: coherence
Original:  Its effective stiffness range is 0.2 Nm/rad to 313 Nm/rad and its maximum continuous torque 9.4 Nm. This unique combination makes the new actuator  particularly suitable for highly dynamic tasks , while being also very versatile, and makes it especially interesting for teleoperation and human-robot collaboration.
Revised:  Its effective stiffness range is 0.2 Nm/rad to 313 Nm/rad and its maximum continuous torque 9.4 Nm. This unique combination makes the new actuator is particularly suitable for highly dynamic tasks , while being also very versatile, and makes it especially interesting for teleoperation and human-robot collaboration.
Edit intent: fluency
Original:  Its effective stiffness range is 0.2 Nm/rad to 313 Nm/rad and its maximum continuous torque 9.4 Nm. This unique combination makes the new actuator  particularly suitable for highly dynamic tasks , while being also very versatile, and makes it especially interesting for teleoperation and human-robot collaboration.
Revised:  Its effective stiffness range is 0.2 Nm/rad to 313 Nm/rad and its maximum continuous torque 9.4 Nm. This unique combination makes the new actuator particularly suitable for highly dynamic tasks . At the same time, the new actuator is also very versatile, and makes it especially interesting for teleoperation and human-robot collaboration.
Edit intent: coherence
Original:  Its effective stiffness range is 0.2 Nm/rad to 313 Nm/rad and its maximum continuous torque 9.4 Nm. This unique combination makes the new actuator  particularly suitable for highly dynamic tasks , while being also very versatile, and makes it especially interesting for teleoperation and human-robot collaboration.
Revised:  Its effective stiffness range is 0.2 Nm/rad to 313 Nm/rad and its maximum continuous torque 9.4 Nm. This unique combination makes the new actuator particularly suitable for highly dynamic tasks , while being also very versatile, making it especially interesting for teleoperation and human-robot collaboration.
Edit intent: fluency
Original:  We provide stability results for the cover filtration and show how the construction is equivalent to the Cech filtration under certain settings  .
Revised:  We prove a stability result: the cover filtrations of two covers are \alpha/m interleaved, where \alpha is a bound on bottleneck distance between covers and m is the size of smallest set in either cover. We also show our construction is equivalent to the Cech filtration under certain settings .
Edit intent: meaning-changed
Original:  For explainable machine learning, we apply the Mapper for model induction, providing explanations in the form of paths between subpopulations or observations. Our framework provides an alternative way of building a filtration from a single mapper that is then used to explore stable paths . As a direct illustration, we build a mapper from a supervised machine learning model trained on the FashionMNIST data set .
Revised:  For explainable machine learning, we apply the Mapper for model induction, providing explanations in the form of paths between subpopulations . Stable paths in the Mapper from a supervised machine learning model trained on the FashionMNIST data set .
Edit intent: clarity
Original:  As a direct illustration, we build a mapper from a supervised machine learning model trained on the FashionMNIST data set . We show that the stable paths in the cover filtration provide improved explanations of relationships between subpopulations of images.
Revised:  As a direct illustration, we build a mapper from a supervised machine learning model trained on the FashionMNIST data set provide improved explanations of relationships between subpopulations of images.
Edit intent: coherence
Original: Motivational salience is a mechanism that determines URLanism's current level of attraction to or repulsion from a particular object, event, or outcome. Motivational salience is described by modulating the reward by an externally controlled parameter that remains constant within a single behavioral episode. The vector of perceived values of various outcomes determines motivation of URLanism toward different goals. Organism's behavior should be able to adapt to the varying-in-time motivation vector.
Revised: How can animals behave effectively in conditions involving different motivational contexts?
Edit intent: clarity
Original:  Finally, we show that firing patterns displayed by neurons in the ventral pallidum  , a basal ganglia structure playing a crucial role in motivated behaviors , are similar to the responses of neuronsin recurrent neural networks trained in similar conditions.
Revised:  Finally, we show that in Pavlovian conditioning task, the responses of the neurons in our model resemble the firing patterns of neurons in the ventral pallidum , a basal ganglia structure playing a crucial role in motivated behaviors , are similar to the responses of neuronsin recurrent neural networks trained in similar conditions.
Edit intent: meaning-changed
Original:  Finally, we show that firing patterns displayed by neurons in the ventral pallidum  , a basal ganglia structure playing a crucial role in motivated behaviors , are similar to the responses of neuronsin recurrent neural networks trained in similar conditions.
Revised:  Finally, we show that firing patterns displayed by neurons in the ventral pallidum (VP) , a basal ganglia structure playing a crucial role in motivated behaviors , are similar to the responses of neuronsin recurrent neural networks trained in similar conditions.
Edit intent: clarity
Original:  Finally, we show that firing patterns displayed by neurons in the ventral pallidum  , a basal ganglia structure playing a crucial role in motivated behaviors , are similar to the responses of neuronsin recurrent neural networks trained in similar conditions.
Revised:  Finally, we show that firing patterns displayed by neurons in the ventral pallidum , a basal ganglia structure involved in motivated behaviors , are similar to the responses of neuronsin recurrent neural networks trained in similar conditions.
Edit intent: clarity
Original:  Finally, we show that firing patterns displayed by neurons in the ventral pallidum  , a basal ganglia structure playing a crucial role in motivated behaviors , are similar to the responses of neuronsin recurrent neural networks trained in similar conditions. Similarly to the pallidum neurons, artificial neural nets contain two different classes of neurons, tuned to reward and punishment.
Revised:  Finally, we show that firing patterns displayed by neurons in the ventral pallidum , a basal ganglia structure playing a crucial role in motivated behaviors . We show that, similarly to real neurons, recurrent networks with motivation are composed of two oppositely-tuned classes of neurons, tuned to reward and punishment.
Edit intent: clarity
Original:  We conclude that reinforcement learning networks can efficiently learn optimal behavior in conditions when reward values are modulated by external motivational processes with arbitrary dynamics. Motivational salience can be viewed as a general-purpose model-free method identifying and capturing changes in subjective or objective values of multiple rewards. Networks with motivationmay also be parts of a larger hierarchical reinforcement learning system in the brain.
Revised:  We conclude that networks with motivation can rapidly adapt their behavior to varying conditions without changes in synaptic strength when expected reward is modulated by motivation. Such networks may also provide a mechanism for how hierarchical reinforcement learning system in the brain.
Edit intent: clarity
Original:  It hasfor several decades, spurred the interest of many researchers to design stock price predictive models . In this paper , the URLanisms search algorithm, a new metaheuristic algorithm is employed as an efficient method for training feedforward neural networks (FFNN).
Revised:  It hasfor several decades, spurred the interest of many researchers to design stock price predictive models ; of which some have been utilized to predict the next day opening and closing prices of the stock indices. This paper proposes the design and implementation of a hybrid URLanisms search trained feedforward neural network model for effective and accurate stock price prediction. The URLanisms search algorithm is used as an efficient method for training feedforward neural networks (FFNN).
Edit intent: meaning-changed
Original:  In this paper , the URLanisms search algorithm, a new metaheuristic algorithm is employed as an efficient method for training feedforward neural networks (FFNN). The training process is used to build a better stock price predictive model.
Revised:  In this paper , the URLanisms search algorithm, a new metaheuristic algorithm is employed as an efficient method for training feedforward neural networks , while the resulting training process is used to build a better stock price predictive model.
Edit intent: clarity
Original:  The training process is used to build a better stock price predictive model. The Straits Times Index, Nikkei 225, NASDAQ Composite, S P 500, and Dow Jones Industrial Average indices were utilized as time series data sets for training and testing proposed predic-tive model.
Revised:  The training process is used to build a better stock price P 500, and Dow Jones Industrial Average indices were utilized as time series data sets for training and testing proposed predic-tive model.
Edit intent: clarity
Original:  The Straits Times Index, Nikkei 225, NASDAQ Composite, S P 500, and Dow Jones Industrial Average indices were utilized as time series data sets for training and testing proposed predic-tive model.
Revised:  The Straits Times Index, Nikkei 225, NASDAQ Composite, S prediction model. Furthermore, the study also presents a comparative performance evaluation of three different stock price forecasting models; namely, the particle swarm optimization trained feedforward neural network model, the genetic algorithm trained feedforward neural network model and the well-known ARIMA model. The system developed in support of this study utilizes sixteen stock indices as time series data sets for training and testing proposed predic-tive model.
Edit intent: meaning-changed
Original:  The Straits Times Index, Nikkei 225, NASDAQ Composite, S P 500, and Dow Jones Industrial Average indices were utilized as time series data sets for training and testing proposed predic-tive model.
Revised:  The Straits Times Index, Nikkei 225, NASDAQ Composite, S P 500, and Dow Jones Industrial Average indices were utilized as time series datasets for training and testing proposed predic-tive model.
Edit intent: clarity