wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In an MMD, each point of one set must be matched to a given number of the points of the other set , and the cost of matching a point to another point is equal to the distance between the two points. We present an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) , where the input point sets S and T lie on the line and |S|+|T| = n . Then, we study a generalized version of MMD problem, that is the many-to-many matching with demands and capacities problem (MMDC), that in which each point has a limited capacity in addition to a demand.
Revised:  In an MMD, each point of one set must be matched to a given number of the points of the other set . We propose an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) , where the input point sets S and T lie on the line and |S|+|T| = n . Then, we study a generalized version of MMD problem, that is the many-to-many matching with demands and capacities problem (MMDC), that in which each point has a limited capacity in addition to a demand.
Edit intent: coherence
Original:  We present an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) , where the input point sets S and T lie on the line and |S|+|T| = n . Then, we study a generalized version of MMD problem, that is the many-to-many matching with demands and capacities problem (MMDC), that in which each point has a limited capacity in addition to a demand.
Revised:  We present an O(n^2) time algorithm for computing a one-dimensional MMD (OMMD) , where the input point sets S and T lie on the line and |S|+|T| = n . Then, we study a generalized version of MMD problem, that is the many-to-many matching with demands and capacities problem (MMDC), that in which each point has a limited capacity in addition to a demand.
Edit intent: fluency
Original:  We present an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) , where the input point sets S and T lie on the line and |S|+|T| = n . Then, we study a generalized version of MMD problem, that is the many-to-many matching with demands and capacities problem (MMDC), that in which each point has a limited capacity in addition to a demand.
Revised:  We present an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) of minimum cost where |S| + |T| = n. In an OMMD the input point sets S and T lie on the line and |S|+|T| = n . Then, we study a generalized version of MMD problem, that is the many-to-many matching with demands and capacities problem (MMDC), that in which each point has a limited capacity in addition to a demand.
Edit intent: meaning-changed
Original:  We present an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) , where the input point sets S and T lie on the line and |S|+|T| = n . Then, we study a generalized version of MMD problem, that is the many-to-many matching with demands and capacities problem (MMDC), that in which each point has a limited capacity in addition to a demand.
Revised:  We present an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) , where the input point sets S and T lie on the line and the cost of matching a point to another point is equal to the distance between the two points . Then, we study a generalized version of MMD problem, that is the many-to-many matching with demands and capacities problem (MMDC), that in which each point has a limited capacity in addition to a demand.
Edit intent: meaning-changed
Original:  We present an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) , where the input point sets S and T lie on the line and |S|+|T| = n . Then, we study a generalized version of MMD problem, that is the many-to-many matching with demands and capacities problem (MMDC), that in which each point has a limited capacity in addition to a demand.
Revised:  We present an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) , where the input point sets S and T lie on the line and |S|+|T| = n . Then, we study a generalized version of MMD problem, the many-to-many matching with demands and capacities problem (MMDC), that in which each point has a limited capacity in addition to a demand.
Edit intent: coherence
Original: Given two point sets S and T, we first study the many-to-many matching with demands problem (MMDproblem) where each point of one set must be matched to  a given number of the points of the other set.
Revised: Given two point sets S and T, we first study the many-to-many matching with demands (MMD) problem, where each point of one set must be matched to a given number of the points of the other set.
Edit intent: clarity
Original: Given two point sets S and T, we first study the many-to-many matching with demands problem (MMDproblem) where each point of one set must be matched to  a given number of the points of the other set.
Revised: Given two point sets S and T, we first study the many-to-many matching with demands problem (MMDproblem) where each point of one set must be matched to at least a given number of the points of the other set.
Edit intent: clarity
Original:  We propose an O(n^2) time algorithm for computing a one-dimensional MMD (OMMD) of minimum cost  where |S|+|T| = n. In an OMMD  the input point sets S and T lie on the  line and the cost of matching a point to another point is equal to the distance between the two points.
Revised:  We propose an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) of minimum cost where |S|+|T| = n. In an OMMD the input point sets S and T lie on the line and the cost of matching a point to another point is equal to the distance between the two points.
Edit intent: fluency
Original:  We propose an O(n^2) time algorithm for computing a one-dimensional MMD (OMMD) of minimum cost  where |S|+|T| = n. In an OMMD  the input point sets S and T lie on the  line and the cost of matching a point to another point is equal to the distance between the two points.
Revised:  We propose an O(n^2) time algorithm for computing a one-dimensional MMD (OMMD) of minimum cost , where |S|+|T| = n. In an OMMD the input point sets S and T lie on the line and the cost of matching a point to another point is equal to the distance between the two points.
Edit intent: fluency
Original:  We propose an O(n^2) time algorithm for computing a one-dimensional MMD (OMMD) of minimum cost  where |S|+|T| = n. In an OMMD  the input point sets S and T lie on the  line and the cost of matching a point to another point is equal to the distance between the two points.
Revised:  We propose an O(n^2) time algorithm for computing a one-dimensional MMD (OMMD) of minimum cost where |S|+|T| = n. In an OMMD problem, the input point sets S and T lie on the line and the cost of matching a point to another point is equal to the distance between the two points.
Edit intent: clarity
Original:  We propose an O(n^2) time algorithm for computing a one-dimensional MMD (OMMD) of minimum cost  where |S|+|T| = n. In an OMMD  the input point sets S and T lie on the  line and the cost of matching a point to another point is equal to the distance between the two points.
Revised:  We propose an O(n^2) time algorithm for computing a one-dimensional MMD (OMMD) of minimum cost where |S|+|T| = n. In an OMMD the input point sets S and T lie on the real line and the cost of matching a point to another point is equal to the distance between the two points.
Edit intent: meaning-changed
Original:  Then, we study a generalized version of  MMD problem, the many-to-many matching with demands and capacities problem (MMDC)  , that in which each point has a limited capacity in addition to a demand.
Revised:  We also study a generalized version of MMD problem, the many-to-many matching with demands and capacities problem (MMDC) , that in which each point has a limited capacity in addition to a demand.
Edit intent: coherence
Original:  Then, we study a generalized version of  MMD problem, the many-to-many matching with demands and capacities problem (MMDC)  , that in which each point has a limited capacity in addition to a demand.
Revised:  Then, we study a generalized version of the MMD problem, the many-to-many matching with demands and capacities problem (MMDC) , that in which each point has a limited capacity in addition to a demand.
Edit intent: fluency
Original:  Then, we study a generalized version of  MMD problem, the many-to-many matching with demands and capacities problem (MMDC)  , that in which each point has a limited capacity in addition to a demand.
Revised:  Then, we study a generalized version of MMD problem, the many-to-many matching with demands and capacities (MMDC) , that in which each point has a limited capacity in addition to a demand.
Edit intent: fluency
Original:  Then, we study a generalized version of  MMD problem, the many-to-many matching with demands and capacities problem (MMDC)  , that in which each point has a limited capacity in addition to a demand.
Revised:  Then, we study a generalized version of MMD problem, the many-to-many matching with demands and capacities problem (MMDC) problem , that in which each point has a limited capacity in addition to a demand.
Edit intent: coherence
Original: Given two point sets S and T, we first study the many-to-many matching with demands (MMD) problem , where each point of one set must be matched to at least a given number of the points of the other set .
Revised: Given two point sets S and T, a many-to-many matching with demands (MMD) problem , where each point of one set must be matched to at least a given number of the points of the other set .
Edit intent: clarity
Original: Given two point sets S and T, we first study the many-to-many matching with demands (MMD) problem , where each point of one set must be matched to at least a given number of the points of the other set .
Revised: Given two point sets S and T, we first study the many-to-many matching with demands (MMD) problem is the problem of finding a minimum-cost many-to-many matching between S and T such that each point of one set must be matched to at least a given number of the points of the other set .
Edit intent: meaning-changed
Original: Given two point sets S and T, we first study the many-to-many matching with demands (MMD) problem , where each point of one set must be matched to at least a given number of the points of the other set .
Revised: Given two point sets S and T, we first study the many-to-many matching with demands (MMD) problem , where each point of S (respectively T) is matched to at least a given number of the points of the other set .
Edit intent: clarity
Original: Given two point sets S and T, we first study the many-to-many matching with demands (MMD) problem , where each point of one set must be matched to at least a given number of the points of the other set .
Revised: Given two point sets S and T, we first study the many-to-many matching with demands (MMD) problem , where each point of one set must be matched to at least a given number of the points of T (respectively S) .
Edit intent: meaning-changed
Original:  We propose an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) of minimum cost  , where |S|+|T| = n. In an OMMD problem, the input point sets S and T lie on the real line and the cost of matching a point to another point is equal to the distance between the two points.
Revised:  We propose the first O(n^2) time algorithm for computing a one dimensional MMD (OMMD) of minimum cost , where |S|+|T| = n. In an OMMD problem, the input point sets S and T lie on the real line and the cost of matching a point to another point is equal to the distance between the two points.
Edit intent: meaning-changed
Original:  We propose an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) of minimum cost  , where |S|+|T| = n. In an OMMD problem, the input point sets S and T lie on the real line and the cost of matching a point to another point is equal to the distance between the two points.
Revised:  We propose an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) of minimum cost between S and T , where |S|+|T| = n. In an OMMD problem, the input point sets S and T lie on the real line and the cost of matching a point to another point is equal to the distance between the two points.
Edit intent: meaning-changed
Original:  We propose an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) of minimum cost  , where |S|+|T| = n. In an OMMD problem, the input point sets S and T lie on the real line and the cost of matching a point to another point is equal to the distance between the two points.
Revised:  We propose an O(n^2) time algorithm for computing a one dimensional MMD (OMMD) of minimum cost , where |S|+|T| = n. In an OMMD problem, the input point sets S and T lie on the real line and the cost of matching a point to another point equals the distance between the two points.
Edit intent: fluency
Original:  A carefully designed sequential Monte Carlo algorithm provides out-of-sample empirical evidence that our suggested model outperforms, with respect to predictive Bayes factors, modelsthat do not exploit the panel structure of stocks .
Revised:  To evaluate the proposed modelling approach we conduct out-of-sample empirical evidence that our suggested model outperforms, with respect to predictive Bayes factors, modelsthat do not exploit the panel structure of stocks .
Edit intent: clarity
Original:  A carefully designed sequential Monte Carlo algorithm provides out-of-sample empirical evidence that our suggested model outperforms, with respect to predictive Bayes factors, modelsthat do not exploit the panel structure of stocks .
Revised:  A carefully designed sequential Monte Carlo algorithm provides out-of-sample forecasts and we compare the posterior predictive distributions obtained from the different models. We provide evidence that joint modelling of jumps improves the predictive ability of the stochastic volatility models .
Edit intent: meaning-changed
Original:  Such a system will have the dual advantage of suggesting food items that the user is more likely to consume while also achieving the goal of ensuring the food intake leans more towards healthier alternatives. This solves the problem of suggesting food items that are commonly accepted as healthy but are seldom incorporated into one's diet .
Revised:  Such a system will have the dual advantage of suggesting food items that the user is more likely to enjoy based upon matching with their flavour profile through use of the taste biological domain knowledge. This preliminary intends to spark more robust mechanisms by which flavour of food is taken into consideration as a major feature set into food recommendation systems. Our long term vision is to integrate this with health factors to recommend healthy and tasty food to users to enhance quality of life .
Edit intent: meaning-changed
Original:  Our proposed method shows improvements on independent test data relative to a recently published baseline method  .
Revised:  Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusion: As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data .
Edit intent: meaning-changed
Original:  The theory assumes that customers find a price unfair when it carries a high markup over cost, and that customers dislike unfair prices . Since markups are not observable , customers must extract them from prices.
Revised:  The theory assumes that customers find a price unfair when it carries a high markup over cost, and that customers dislike unfair prices , namely those marked up steeply over cost. Since costs are unobservable , customers must extract them from prices.
Edit intent: meaning-changed
Original:  Embedded in a New Keynesian model---as a replacement of Calvo pricing---our theory produces monetary nonneutrality. When monetary policy loosens and inflation rises, customers misperceive markups as higher and feel unfairly treated;
Revised:  Embedded in a New Keynesian model as a replacement for the usual pricing frictions, our theory produces monetary-policy nonneutrality: when monetary policy loosens and inflation rises, customers misperceive markups as higher and feel unfairly treated;
Edit intent: clarity
Original:  firms mitigate the perceived unfairness of prices by reducing their markups , which in general equilibrium leads to higher output .
Revised:  firms mitigate the perceived unfairness of prices by reducing their markups , which in general equilibrium , employment rises. The New Keynesian model also features a hybrid short-run Phillips curve, realistic impulse responses of output and employment to monetary and technology shocks, and an upward-sloping long-run Phillips curve .
Edit intent: meaning-changed
Original:  Embedded in a New Keynesian model as a replacement for the usual pricing frictions, our theory produces monetary-policy nonneutrality: when monetary policy loosens and inflation rises, customers misperceive markups as higher and feel unfairly treated;
Revised:  Embedded in a New Keynesian model as a replacement for the usual pricing frictions, our theory produces monetary nonneutrality: when monetary policy loosens and inflation rises, customers misperceive markups as higher and feel unfairly treated;
Edit intent: clarity
Original:  A major issue that has received little attention in the Raman literature is the fact that variations in instrumental responses and intrinsic spectral backgrounds over different days of measurements or devices creates strong inconsistency of Raman intensity spectra over the various experimental condition, thus making the use of Raman spectroscopy on a large scale and reproductive basis difficult.
Revised:  A major issue that has received a lot of attention in the Raman literature is the fact that variations in instrumental responses and intrinsic spectral backgrounds over different days of measurements or devices creates strong inconsistency of Raman intensity spectra over the various experimental condition, thus making the use of Raman spectroscopy on a large scale and reproductive basis difficult.
Edit intent: clarity
Original:  Recently, it has been shown that the number of engaged torque-generating 'stator ' units in the motor varies depending on the external load the motor experiences , and suggested that mechanosensing in the flagellar motor is driven via a 'catch bond' mechanism in the motor's stator units.
Revised:  Recently, it has been shown that the number of functional torque-generating 'stator ' units in the motor varies depending on the external load the motor experiences , and suggested that mechanosensing in the flagellar motor is driven via a 'catch bond' mechanism in the motor's stator units.
Edit intent: clarity
Original:  Recently, it has been shown that the number of engaged torque-generating 'stator ' units in the motor varies depending on the external load the motor experiences , and suggested that mechanosensing in the flagellar motor is driven via a 'catch bond' mechanism in the motor's stator units.
Revised:  Recently, it has been shown that the number of engaged torque-generating stator units in the motor varies depending on the external load the motor experiences , and suggested that mechanosensing in the flagellar motor is driven via a 'catch bond' mechanism in the motor's stator units.
Edit intent: fluency
Original:  Recently, it has been shown that the number of engaged torque-generating 'stator ' units in the motor varies depending on the external load the motor experiences , and suggested that mechanosensing in the flagellar motor is driven via a 'catch bond' mechanism in the motor's stator units.
Revised:  Recently, it has been shown that the number of engaged torque-generating 'stator ' units in the motor depends on the external load the motor experiences , and suggested that mechanosensing in the flagellar motor is driven via a 'catch bond' mechanism in the motor's stator units.
Edit intent: clarity
Original:  Recently, it has been shown that the number of engaged torque-generating 'stator ' units in the motor varies depending on the external load the motor experiences , and suggested that mechanosensing in the flagellar motor is driven via a 'catch bond' mechanism in the motor's stator units.
Revised:  Recently, it has been shown that the number of engaged torque-generating 'stator ' units in the motor varies depending on the external load , and suggested that mechanosensing in the flagellar motor is driven via a 'catch bond' mechanism in the motor's stator units.
Edit intent: clarity
Original:  Here, we present a method that allows us to measure stator dynamics across a large range of external loads, including near the zero-torque limit : By attaching superparamagnetic beads to the flagellar hook, we can control the motor's speed via a rotating magnetic field.
Revised:  We present a method that allows us to measure stator dynamics across a large range of external loads, including near the zero-torque limit : By attaching superparamagnetic beads to the flagellar hook, we can control the motor's speed via a rotating magnetic field.
Edit intent: coherence
Original:  Here, we present a method that allows us to measure stator dynamics across a large range of external loads, including near the zero-torque limit : By attaching superparamagnetic beads to the flagellar hook, we can control the motor's speed via a rotating magnetic field.
Revised:  Here, we present a method that allows us to measure -- on a single motor -- stator unit dynamics across a large range of external loads, including near the zero-torque limit : By attaching superparamagnetic beads to the flagellar hook, we can control the motor's speed via a rotating magnetic field.
Edit intent: meaning-changed
Original:  Here, we present a method that allows us to measure stator dynamics across a large range of external loads, including near the zero-torque limit : By attaching superparamagnetic beads to the flagellar hook, we can control the motor's speed via a rotating magnetic field.
Revised:  Here, we present a method that allows us to measure stator dynamics across a large range of external loads, including near the zero-torque limit . By attaching superparamagnetic beads to the flagellar hook, we can control the motor's speed via a rotating magnetic field.
Edit intent: fluency
Original:  Our results suggest that stator dynamics are affected by the torque that each stator unit experiences, manifested as a function of the energy available to it in the form of IMF and the speed at which the rotor is driven .
Revised:  This framework allows for a deeper exploration into the mechanism behind load-dependent remodelling by separating out motor properties, such as rotation speed and energy availability in the form of IMF and the speed at which the rotor is driven .
Edit intent: meaning-changed
Original:  Our results suggest that stator dynamics are affected by the torque that each stator unit experiences, manifested as a function of the energy available to it in the form of IMF and the speed at which the rotor is driven .
Revised:  Our results suggest that stator dynamics are affected by the torque that each stator unit experiences, manifested as a function of the energy available to it in the form of IMF , that affect the motor torque .
Edit intent: meaning-changed
Original:  Considering these measures as stochastic processes in a stationary environment (defined by the arrival processes, message processing times and admission controls in bufferless systems)  we characterize their distributions using the Palm inversion formula.
Revised:  Considering these measures as stochastic processes in a stationary environment (defined by the arrival processes, message processing times and admission controls in bufferless systems) , we characterize their distributions using the Palm inversion formula.
Edit intent: fluency