wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To compare the impact of the uniform random distribution of DCs (R-system) and the non-uniform distribution in  form of random Gaussian-clusters (G-system), we chose inhibitor as the control parameter.
Revised:  To compare the impact of the uniform random distribution of DCs (R-system) and the non-uniform distribution in the form of random Gaussian-clusters (G-system), we chose inhibitor as the control parameter.
Edit intent: fluency
Original:  In turn, when we consider the favourable environment, the relatively bigger shift toward higher final concentrations of DCs appears in the G. Thus, in the suitably favourable environment  this order can be reversed.
Revised:  In turn, when we consider the favourable environment, the relatively bigger shift toward higher final concentrations of DCs appears in the G. Thus, in the suitably favourable environment , this order can be reversed.
Edit intent: fluency
Original:  Furthermore, for various fictitious present analysis times t_2, positioned in advance to bubble crashes, we employ a clustering method to group  LPPLS fits over different time scales and the predicted critical times t_c (the most probable time for the start of the crash ending the bubble) .
Revised:  Furthermore, for various fictitious `present' times t2 before the crashes, we employ a clustering method to group LPPLS fits over different time scales and the predicted critical times t_c (the most probable time for the start of the crash ending the bubble) .
Edit intent: clarity
Original:  Furthermore, for various fictitious present analysis times t_2, positioned in advance to bubble crashes, we employ a clustering method to group  LPPLS fits over different time scales and the predicted critical times t_c (the most probable time for the start of the crash ending the bubble) .
Revised:  Furthermore, for various fictitious present analysis times t_2, positioned in advance to bubble crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales and the predicted critical times t_c (the most probable time for the start of the crash ending the bubble) .
Edit intent: meaning-changed
Original:  Furthermore, for various fictitious present analysis times t_2, positioned in advance to bubble crashes, we employ a clustering method to group  LPPLS fits over different time scales and the predicted critical times t_c (the most probable time for the start of the crash ending the bubble) .
Revised:  Furthermore, for various fictitious present analysis times t_2, positioned in advance to bubble crashes, we employ a clustering method to group LPPLS fits over different time scales , where tc is the most probable time for the start of the crash ending the bubble) .
Edit intent: clarity
Original:  Furthermore, for various fictitious present analysis times t_2, positioned in advance to bubble crashes, we employ a clustering method to group  LPPLS fits over different time scales and the predicted critical times t_c (the most probable time for the start of the crash ending the bubble) .
Revised:  Furthermore, for various fictitious present analysis times t_2, positioned in advance to bubble crashes, we employ a clustering method to group LPPLS fits over different time scales and the predicted critical times t_c (the most probable time for the ending of the bubble .
Edit intent: clarity
Original:  Furthermore, for various fictitious ` present' times t2 before the crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales, where tc is the most probable time for the ending of the bubble.
Revised:  Furthermore, for various fictitious ' present' times t2 before the crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales, where tc is the most probable time for the ending of the bubble.
Edit intent: fluency
Original:  Furthermore, for various fictitious ` present' times t2 before the crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales, where tc is the most probable time for the ending of the bubble.
Revised:  Furthermore, for various fictitious ` present' times t_2 before the crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales, where tc is the most probable time for the ending of the bubble.
Edit intent: clarity
Original:  Furthermore, for various fictitious ` present' times t2 before the crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales, where tc is the most probable time for the ending of the bubble.
Revised:  Furthermore, for various fictitious ` present' times t2 before the crashes, we employ a clustering method to group the predicted critical times t_c of the LPPLS fits over different time scales, where tc is the most probable time for the ending of the bubble.
Edit intent: meaning-changed
Original:  Furthermore, for various fictitious ` present' times t2 before the crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales, where tc is the most probable time for the ending of the bubble.
Revised:  Furthermore, for various fictitious ` present' times t2 before the crashes, we employ a clustering method to group the predicted critical times tc of the LPPLS fits over different time scales, where t_c is the most probable time for the ending of the bubble.
Edit intent: clarity
Original:  We next characterize the points at which the measure concentrates, and exhibit a particular solution corresponding to a Dirac measure at a time satisfy an ordinary differential equation identical to the original FitzHugh-Nagumo system.
Revised:  We next characterize the points at which this measure concentrates, and exhibit a particular solution corresponding to a Dirac measure at a time satisfy an ordinary differential equation identical to the original FitzHugh-Nagumo system.
Edit intent: fluency
Original:  We next characterize the points at which the measure concentrates, and exhibit a particular solution corresponding to a Dirac measure at a time satisfy an ordinary differential equation identical to the original FitzHugh-Nagumo system.
Revised:  We next characterize the points at which the measure concentrates, and exhibit a particular solution corresponding to a Dirac measure concentrated on a time-dependent point satisfying an ordinary differential equation identical to the original FitzHugh-Nagumo system.
Edit intent: meaning-changed
Original:  We exhibit a truthful  envy-free mechanism for cake cutting and chore division for two agents with piecewise uniform valuations, and we complement our result by showing that such a mechanism does not exist when certain additional assumptions are made .
Revised:  We exhibit a truthful and envy-free mechanism for cake cutting and chore division for two agents with piecewise uniform valuations, and we complement our result by showing that such a mechanism does not exist when certain additional assumptions are made .
Edit intent: coherence
Original:  We exhibit a truthful  envy-free mechanism for cake cutting and chore division for two agents with piecewise uniform valuations, and we complement our result by showing that such a mechanism does not exist when certain additional assumptions are made .
Revised:  We exhibit a truthful envy-free mechanism for cake cutting and chore division for two agents with piecewise uniform valuations, and we complement our result by showing that such a mechanism does not exist when certain additional constraints are imposed on the mechanisms .
Edit intent: clarity
Original: A nine state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state, may be reduced to a system of kinetic equations for a six state system.
Revised: A thirteen-state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state, may be reduced to a system of kinetic equations for a six state system.
Edit intent: meaning-changed
Original: A nine state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state, may be reduced to a system of kinetic equations for a six state system.
Revised: A nine state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state, may be reduced to kinetic equations for a six state system.
Edit intent: clarity
Original:  Assuming  the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the  kinetic equations may be  reduced to rate equations for activation, and  fast and slow inactivation that describe spike frequency adaptation  in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Revised:  Assuming that the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the kinetic equations may be reduced to rate equations for activation, and fast and slow inactivation that describe spike frequency adaptation in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Edit intent: fluency
Original:  Assuming  the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the  kinetic equations may be  reduced to rate equations for activation, and  fast and slow inactivation that describe spike frequency adaptation  in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Revised:  Assuming the activation sensors are independent and that activation has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the kinetic equations may be reduced to rate equations for activation, and fast and slow inactivation that describe spike frequency adaptation in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Edit intent: clarity
Original:  Assuming  the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the  kinetic equations may be  reduced to rate equations for activation, and  fast and slow inactivation that describe spike frequency adaptation  in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Revised:  Assuming the activation sensors are independent and the activation variable m has a faster relaxation than inactivation, the fast inactivation rate function is, in general, dependent on the activation variable m(t) ^3 h(t) , and the kinetic equations may be reduced to rate equations for activation, and fast and slow inactivation that describe spike frequency adaptation in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Edit intent: meaning-changed
Original:  Assuming  the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the  kinetic equations may be  reduced to rate equations for activation, and  fast and slow inactivation that describe spike frequency adaptation  in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Revised:  Assuming the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) but may be approximated by a voltage-dependent function , and the kinetic equations may be reduced to rate equations for activation, and fast and slow inactivation that describe spike frequency adaptation in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Edit intent: meaning-changed
Original:  Assuming  the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the  kinetic equations may be  reduced to rate equations for activation, and  fast and slow inactivation that describe spike frequency adaptation  in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Revised:  Assuming the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the rate function for entry into the slow inactivated state is dependent on the fast inactivation variable. The kinetic equations may be reduced to rate equations for activation, and fast and slow inactivation that describe spike frequency adaptation in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Edit intent: meaning-changed
Original:  Assuming  the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the  kinetic equations may be  reduced to rate equations for activation, and  fast and slow inactivation that describe spike frequency adaptation  in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Revised:  Assuming the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the kinetic equations may be further reduced to rate equations for activation, and fast and slow inactivation that describe spike frequency adaptation in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Edit intent: clarity
Original:  Assuming  the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the  kinetic equations may be  reduced to rate equations for activation, and  fast and slow inactivation that describe spike frequency adaptation  in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Revised:  Assuming the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the kinetic equations may be reduced to rate equations for activation, and coupled fast and slow inactivation that describe spike frequency adaptation in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Edit intent: meaning-changed
Original:  Assuming  the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the  kinetic equations may be  reduced to rate equations for activation, and  fast and slow inactivation that describe spike frequency adaptation  in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Revised:  Assuming the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the kinetic equations may be reduced to rate equations for activation, and fast and slow inactivation that describe spike frequency adaptation and a repetitive bursting oscillation in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Edit intent: fluency
Original:  Assuming  the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the  kinetic equations may be  reduced to rate equations for activation, and  fast and slow inactivation that describe spike frequency adaptation  in the neural membrane, a repetitive bursting oscillation and a cardiac action potential with a plateau oscillation.
Revised:  Assuming the activation sensors are independent and the activation variable m has a faster relaxation than the inactivation variable h, the open state probability during the action potential may be expressed as m(t) ^3 h(t) , and the kinetic equations may be reduced to rate equations for activation, and fast and slow inactivation that describe spike frequency adaptation in the neural membrane, and a cardiac action potential with a plateau oscillation.
Edit intent: clarity
Original: A thirteen-state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state , may be reduced to kinetic equations for a six state system .
Revised: A fifteen state kinetic model for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state , may be reduced to kinetic equations for a six state system .
Edit intent: clarity
Original: A thirteen-state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state , may be reduced to kinetic equations for a six state system .
Revised: A thirteen-state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and slow inactivated states , may be reduced to kinetic equations for a six state system .
Edit intent: fluency
Original: A thirteen-state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state , may be reduced to kinetic equations for a six state system .
Revised: A thirteen-state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state , may be reduced to equations for a six state system .
Edit intent: clarity
Original: A thirteen-state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state , may be reduced to kinetic equations for a six state system . Assuming that the activation sensors are independent and that activation has a faster relaxation than inactivation , the fast inactivation rate function is, in general, dependent on the activation variable m(t) but may be approximated by a voltage-dependent function, and the rate function for entry into the slow inactivated state is dependent on the fast inactivation variable .
Revised: A thirteen-state master equation for Na+ channel gating that describes the coupling between three activation sensors, a two-stage fast inactivation process and a slow inactivated state , may be reduced to kinetic equations for a six state system by application of the method of multiple scales. By expressing the occupation probabilities for closed states and the open state in terms of activation and fast inactivation variables, and assuming that activation has a faster relaxation than inactivation , the fast inactivation rate function is, in general, dependent on the activation variable m(t) but may be approximated by a voltage-dependent function, and the rate function for entry into the slow inactivated state is dependent on the fast inactivation variable .
Edit intent: meaning-changed
Original:  Assuming that the activation sensors are independent and that activation has a faster relaxation than inactivation , the fast inactivation rate function is, in general, dependent on the activation variable m(t) but may be approximated by a voltage-dependent function, and the rate function for entry into the slow inactivated state is dependent on the fast inactivation variable .
Revised:  Assuming that the activation sensors are independent and that activation has a faster relaxation than inactivation and that the activation sensors are mutually independent, the kinetic equations may be further reduced to rate equations for activation, and coupled fast and slow inactivation that describe spike frequency adaptation, a repetitive bursting oscillation in the neural membrane, and a cardiac action potential with a plateau oscillation. The fast inactivation rate function is, in general, dependent on the activation variable m(t) but may be approximated by a voltage-dependent function, and the rate function for entry into the slow inactivated state is dependent on the fast inactivation variable .
Edit intent: meaning-changed
Original:  Assuming that the activation sensors are independent and that activation has a faster relaxation than inactivation , the fast inactivation rate function is, in general, dependent on the activation variable m(t) but may be approximated by a voltage-dependent function, and the rate function for entry into the slow inactivated state is dependent on the fast inactivation variable . The kinetic equations may be further reduced to rate equations for activation, and coupled fast and slow inactivation that describe spike frequency adaptation and a repetitive bursting oscillation in the neural membrane, and a cardiac action potential with a plateau oscillation .
Revised:  Assuming that the activation sensors are independent and that activation has a faster relaxation than inactivation , the fast inactivation rate function is, in general, dependent on the activation variable m(t) but may be approximated by a voltage-dependent function, and the rate function for entry into the slow inactivated state is dependent on the fast inactivation variable .
Edit intent: meaning-changed
Original:  Currently, this is often done using simple threshold policies to reserve large parts of each cluster, which leads to low average utilizationof the cluster. In this paper, we propose more sophisticated policies for controlling admission to a cluster and demonstrate that they significantly increase cluster utilization.
Revised:  Currently, this is often done using simple threshold policies to reserve large parts of each cluster, which leads to low utilization. We propose more sophisticated policies for controlling admission to a cluster and demonstrate that they significantly increase cluster utilization.
Edit intent: clarity