wanyu/IteraTeR_v2 huggingface dataset:
Original:  For the classes of the hierarchy, we investigate structural conditions on both the network and the  corresponding adjacency matrix that are necessary and sufficient for the inclusion of a network in the class . Moreover, we provide an algorithmic solution for the problem of positioning a network in the hierarchy.
Revised:  For the classes of the hierarchy, we investigate structural conditions on both the network and the corresponding adjacency matrix . Moreover, we provide an algorithmic solution for the problem of positioning a network in the hierarchy.
Edit intent: clarity
Original:  Oscillatory dynamics are thought to be widespread due to their superior information encoding capabilities, and single cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks.
Revised:  Oscillatory dynamics are thought to be widespread , and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks.
Edit intent: clarity
Original:  Here we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of Bayesian non-parametric regression  .
Revised:  Here we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of non-parametric regression .
Edit intent: clarity
Original:  Our method can distinguish oscillatory  expression from random fluctuations of non-oscillatory gene expression in single cell data , despite peak-to-peak variability in period and amplitude of single cell oscillations.
Revised:  Our method can distinguish oscillatory gene expression from random fluctuations of non-oscillatory gene expression in single cell data , despite peak-to-peak variability in period and amplitude of single cell oscillations.
Edit intent: fluency
Original:  Our method can distinguish oscillatory  expression from random fluctuations of non-oscillatory gene expression in single cell data , despite peak-to-peak variability in period and amplitude of single cell oscillations.
Revised:  Our method can distinguish oscillatory expression from random fluctuations of non-oscillatory expression in single-cell time series , despite peak-to-peak variability in period and amplitude of single cell oscillations.
Edit intent: clarity
Original:  Our method can distinguish oscillatory  expression from random fluctuations of non-oscillatory gene expression in single cell data , despite peak-to-peak variability in period and amplitude of single cell oscillations.
Revised:  Our method can distinguish oscillatory expression from random fluctuations of non-oscillatory gene expression in single cell data , despite peak-to-peak variability in period and amplitude of single-cell oscillations.
Edit intent: fluency
Original: Dijkstra monads are a means by which a dependent type theory can be enhanced with support for reasoning about effectful code .
Revised: Dijkstra monads enable a dependent type theory can be enhanced with support for reasoning about effectful code .
Edit intent: clarity
Original: Dijkstra monads are a means by which a dependent type theory can be enhanced with support for reasoning about effectful code .
Revised: Dijkstra monads are a means by which a dependent type theory to be enhanced with support for reasoning about effectful code .
Edit intent: clarity
Original:  These specification-level monads computing weakest preconditions, and their closely related counterparts, Hoare monads,  provide the basis on which verification tools like F*, Hoare Type Theory (HTT), and Ynot are built.
Revised:  These specification-level monads computing weakest preconditions, and their closely related counterparts, Hoare monads, they provide the basis on which verification tools like F*, Hoare Type Theory (HTT), and Ynot are built.
Edit intent: coherence
Original:  In this paper we show that Dijkstra monads can be derived "for free" by applying a continuation-passing style (CPS) translation to the standard monadic definitions of the underlying computational effects.
Revised:  We show that Dijkstra monads can be derived "for free" by applying a continuation-passing style (CPS) translation to the standard monadic definitions of the underlying computational effects.
Edit intent: coherence
Original:  We demonstrate these ideas in EMF*, a new dependently typed calculus, validating it both by formal proof and via a prototype implementation within F*.
Revised:  We demonstrate these ideas in EMF*, a new dependently typed calculus, validating it via both formal proof and via a prototype implementation within F*.
Edit intent: clarity
Original:  We demonstrate these ideas in EMF*, a new dependently typed calculus, validating it both by formal proof and via a prototype implementation within F*.
Revised:  We demonstrate these ideas in EMF*, a new dependently typed calculus, validating it both by formal proof and a prototype implementation within F*.
Edit intent: clarity
Original:  Besides equipping F* with a more uniform and extensible effect system, EMF* enables within F* a mixture of intrinsic and extrinsic proofs that was previously impossible .
Revised:  Besides equipping F* with a more uniform and extensible effect system, EMF* enables a novel mixture of intrinsic and extrinsic proofs that was previously impossible .
Edit intent: clarity
Original:  Besides equipping F* with a more uniform and extensible effect system, EMF* enables within F* a mixture of intrinsic and extrinsic proofs that was previously impossible .
Revised:  Besides equipping F* with a more uniform and extensible effect system, EMF* enables within F* a mixture of intrinsic and extrinsic proofs within F* .
Edit intent: clarity
Original:  In the dynamic maximum flow problem, the goal is to maximize the flow reaching the sink within a given time horizon T, while flow requires a certain travel time to traverse an arc .
Revised:  In the dynamic maximum flow problem, the goal is to maximize the flow reaching the sink within a given time horizon T, while flow requires a certain travel time to traverse an edge .
Edit intent: clarity
Original:  Furthermore, we investigate temporally repeated flows and show that in contrast to the non-robust case ( i.e. , without uncertainties) they no longer provide optimal solutions for the robust problem, but rather yield a worst case optimality gap of at least T.
Revised:  Furthermore, we investigate temporally repeated flows and show that in contrast to the non-robust case ( that is , without uncertainties) they no longer provide optimal solutions for the robust problem, but rather yield a worst case optimality gap of at least T.
Edit intent: clarity
Original:  We finally show that for infinite delays, the optimality gap is at most O(  k \log T), where k is a newly introduced instance characteristic.
Revised:  We finally show that the optimality gap is at most O( k \log T), where k is a newly introduced instance characteristic.
Edit intent: clarity
Original:  First, we motivate and introduce deterministic and stochastic models for chemical networks, and give an overview of simulation and exact solution methods.
Revised:  First, we motivate and introduce deterministic and stochastic methods for modelling chemical networks, and give an overview of simulation and exact solution methods.
Edit intent: clarity
Original:  We present a comparison of several of these methods by means of a numerical case study and highlight various of their respective advantages and disadvantages.
Revised:  We present a comparison of several of these methods by means of a numerical case study and highlight some of their respective advantages and disadvantages.
Edit intent: fluency
Original:  Experimental results show that using our VRCNN as post-processing leads to on average 4.6 %DIFDELCMD < \\%%% %DIF < bit-rate reduction compared  to HEVC baseline.
Revised:  Experimental results show that using our VRCNN as post-processing leads to on average 4.6 %DIFDELCMD < \\%%% %DIF < bit-rate reduction compared \% bit-rate reduction compared to HEVC baseline.
Edit intent: fluency
Original:  However, calculating the likelihood for discretely observed data from even simple models - such as the ubiquitous susceptible-infectious-removed (SIR) model - has been considered computationally intractable, since its formulation almost a century ago.
Revised:  However, calculating the likelihood for discretely observed data from even simple models -- such as the ubiquitous susceptible-infectious-removed (SIR) model - has been considered computationally intractable, since its formulation almost a century ago.
Edit intent: fluency
Original:  However, calculating the likelihood for discretely observed data from even simple models - such as the ubiquitous susceptible-infectious-removed (SIR) model - has been considered computationally intractable, since its formulation almost a century ago.
Revised:  However, calculating the likelihood for discretely observed data from even simple models - such as the ubiquitous susceptible-infectious-removed (SIR) model -- has been considered computationally intractable, since its formulation almost a century ago.
Edit intent: fluency
Original:  We also apply our direct likelihood evaluation to perform Bayesian inference for the 2014-2015 Ebola outbreak in Guinea under a hierarchical and time-inhomogeneous extension of the SIR model .
Revised:  We also apply our direct likelihood evaluation to perform Bayesian inference for the 2014-2015 Ebola outbreak in Guinea .
Edit intent: clarity
Original: We propose a framework for data-driven pricing in shared vehicle systems ( such as bikesharing and carsharing) in which customers can pick up and drop off vehicles in different locations.
Revised: Designing and operating shared vehicle systems ( such as bikesharing and carsharing) in which customers can pick up and drop off vehicles in different locations.
Edit intent: clarity
Original:  This framework provides\emph{  efficient algorithms with rigorous approximation guarantees for a wide class of objective functions ( including welfare and revenue), and under a variety of constraints on the prices.
Revised:  This framework provides\emph{ efficient algorithms with rigorous approximation guarantees for a wide class of objective functions ( including welfare and revenue), and under a variety of constraints on the prices.
Edit intent: fluency
Original:  This framework provides\emph{  efficient algorithms with rigorous approximation guarantees for a wide class of objective functions ( including welfare and revenue), and under a variety of constraints on the prices.
Revised:  This framework provides\emph{ efficient algorithms with rigorous approximation guarantees for a wide range of objective functions ( including welfare and revenue), and under a variety of constraints on the prices.
Edit intent: clarity
Original: Designing and operating shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to other resource allocation settings due to complex network externalities: in such systems, altering prices in any location affects  future supply throughout the system within very short timescales.
Revised: Optimizing shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to other resource allocation settings due to complex network externalities: in such systems, altering prices in any location affects future supply throughout the system within very short timescales.
Edit intent: clarity
Original: Designing and operating shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to other resource allocation settings due to complex network externalities: in such systems, altering prices in any location affects  future supply throughout the system within very short timescales.
Revised: Designing and operating shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to traditional resource allocation settings due to complex network externalities: in such systems, altering prices in any location affects future supply throughout the system within very short timescales.
Edit intent: clarity
Original: Designing and operating shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to other resource allocation settings due to complex network externalities: in such systems, altering prices in any location affects  future supply throughout the system within very short timescales.
Revised: Designing and operating shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to other resource allocation settings due to the presence of future supply throughout the system within very short timescales.
Edit intent: clarity
Original: Designing and operating shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to other resource allocation settings due to complex network externalities: in such systems, altering prices in any location affects  future supply throughout the system within very short timescales.
Revised: Designing and operating shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to other resource allocation settings due to complex network externalities: in such systems, altering prices in any location affectscomplex network externalities future supply throughout the system within very short timescales.
Edit intent: fluency
Original: Designing and operating shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to other resource allocation settings due to complex network externalities: in such systems, altering prices in any location affects  future supply throughout the system within very short timescales.
Revised: Designing and operating shared vehicle systems (bike-sharing/car-sharing/ride-sharing) is more challenging compared to other resource allocation settings due to complex network externalities: in such systems, altering prices in any location affects future supply throughout the system within short timescales.
Edit intent: clarity
Original:  Such externalities are well captured by steady-state Markov chain models, and hence these are widely used for shared vehicle systems.
Revised:  These externalities are well captured by steady-state Markov chain models, and hence these are widely used for shared vehicle systems.
Edit intent: fluency
Original:  Such externalities are well captured by steady-state Markov chain models, and hence these are widely used for shared vehicle systems.
Revised:  Such externalities are well captured by steady-state Markovian models, which are therefore widely used to analyze such systems.
Edit intent: clarity
Original:  However, using such models to design pricing  policies is computationally difficult since the resulting optimization problem is high-dimensional and non-convex.
Revised:  However, using such models to design pricing policies is computationally difficult since the resulting optimization problems are high-dimensional and non-convex.
Edit intent: fluency
Original:  We develop a general  framework for designing pricing policies in such systems, based on a novel convex relaxation which we term elevated flow relaxation.
Revised:  To this end, we develop a general framework for designing pricing policies in such systems, based on a novel convex relaxation which we term elevated flow relaxation.
Edit intent: coherence
Original:  We develop a general  framework for designing pricing policies in such systems, based on a novel convex relaxation which we term elevated flow relaxation.
Revised:  We develop a general framework for designing pricing policies in shared vehicle systems, based on a novel convex relaxation which we term elevated flow relaxation.
Edit intent: clarity
Original:  For any shared vehicle system with n stations and m vehicles, we show that a simple pricing policy with an approximation ratio of 1+(n-1)/m can be efficiently computed via our relaxation .
Revised:  For any shared vehicle system with n stations and m vehicles, our framework provides a pricing policy with an approximation ratio of 1+(n-1)/m can be efficiently computed via our relaxation .
Edit intent: clarity
Original:  For any shared vehicle system with n stations and m vehicles, we show that a simple pricing policy with an approximation ratio of 1+(n-1)/m can be efficiently computed via our relaxation .
Revised:  For any shared vehicle system with n stations and m vehicles, we show that a simple pricing policy with an approximation ratio of 1+(n-1)/m .
Edit intent: clarity
Original:  The simplicity of our approach also allows us to extend it to more complex settings . Apart from pricing, shared vehicle systems enable other control levers for modulating demand and supply : rebalancing empty vehicles, redirecting riders to nearby vehicles,  etc.
Revised:  Further, the simplicity of our approach also allows us to extend it to more complex settings . Apart from pricing, shared vehicle systems enable other control levers for modulating demand and supply : rebalancing empty vehicles, redirecting riders to nearby vehicles, etc.
Edit intent: coherence
Original:  The simplicity of our approach also allows us to extend it to more complex settings . Apart from pricing, shared vehicle systems enable other control levers for modulating demand and supply : rebalancing empty vehicles, redirecting riders to nearby vehicles,  etc.
Revised:  The simplicity of our approach allows us to extend it to more complex settings . Apart from pricing, shared vehicle systems enable other control levers for modulating demand and supply : rebalancing empty vehicles, redirecting riders to nearby vehicles, etc.
Edit intent: clarity
Original:  The simplicity of our approach also allows us to extend it to more complex settings . Apart from pricing, shared vehicle systems enable other control levers for modulating demand and supply : rebalancing empty vehicles, redirecting riders to nearby vehicles,  etc.
Revised:  The simplicity of our approach also allows us to extend it to more complex settings : rebalancing empty vehicles, redirecting riders to nearby vehicles, etc.
Edit intent: clarity
Original: Optimizing shared vehicle systems ( bike-sharing / car-sharing /  ride-sharing) is more challenging compared to traditional resource allocation settings due to the presence of complex network externalities -- changes in the demand/supply at any location affect future supply throughout the system within short timescales.
Revised: Optimizing shared vehicle systems ( bike / car-sharing / ride-sharing) is more challenging compared to traditional resource allocation settings due to the presence of complex network externalities -- changes in the demand/supply at any location affect future supply throughout the system within short timescales.
Edit intent: fluency
Original: Optimizing shared vehicle systems ( bike-sharing / car-sharing /  ride-sharing) is more challenging compared to traditional resource allocation settings due to the presence of complex network externalities -- changes in the demand/supply at any location affect future supply throughout the system within short timescales.
Revised: Optimizing shared vehicle systems ( bike-sharing / scooter / ride-sharing) is more challenging compared to traditional resource allocation settings due to the presence of complex network externalities -- changes in the demand/supply at any location affect future supply throughout the system within short timescales.
Edit intent: clarity
Original:  However, using such models to design pricing / control policies is computationally difficult since the resulting optimization problems are high-dimensional and non-convex.
Revised:  However, using such models to design pricing and other control policies is computationally difficult since the resulting optimization problems are high-dimensional and non-convex.
Edit intent: clarity
Original:  To this end, we develop a general approximation framework for designing pricing policies in  shared vehicle systems, based on a novel convex relaxation which we termelevated flow relaxation .
Revised:  To this end, we develop a rigorous approximation framework shared vehicle systems, based on a novel convex relaxation which we termelevated flow relaxation .
Edit intent: clarity
Original:  To this end, we develop a general approximation framework for designing pricing policies in  shared vehicle systems, based on a novel convex relaxation which we termelevated flow relaxation .
Revised:  To this end, we develop a general approximation framework for designing pricing policies in for shared vehicle systems, based on a novel convex relaxation which we termelevated flow relaxation .
Edit intent: fluency
Original:  To this end, we develop a general approximation framework for designing pricing policies in  shared vehicle systems, based on a novel convex relaxation which we termelevated flow relaxation .
Revised:  To this end, we develop a general approximation framework for designing pricing policies in shared vehicle systems, elevated flow relaxation .
Edit intent: clarity
Original:  To this end, we develop a general approximation framework for designing pricing policies in  shared vehicle systems, based on a novel convex relaxation which we termelevated flow relaxation .
Revised:  To this end, we develop a general approximation framework for designing pricing policies in shared vehicle systems, based on a novel convex relaxation which we term .
Edit intent: clarity
Original:  Our approach provides the first efficient algorithms withrigorous approximation guarantees for a wide range of  objective functions (throughput, revenue, welfare) .
Revised:  Our approach provides the first efficient algorithms withproviding a unified approach for a wide range of objective functions (throughput, revenue, welfare) .
Edit intent: clarity
Original:  For any shared vehicle system with n stations and m vehicles, our framework provides a pricing policy with an approximation ratio of 1+(n-1)/m . This guarantee is particularly meaningful when m/n, the average number of vehicles per station  is large, as is often the case in practice .
Revised:  For any shared vehicle system with n stations and m vehicles, our framework obtains an approximation ratio of 1+(n-1)/m . This guarantee is particularly meaningful when m/n, the average number of vehicles per station is large, as is often the case in practice .
Edit intent: clarity
Original:  For any shared vehicle system with n stations and m vehicles, our framework provides a pricing policy with an approximation ratio of 1+(n-1)/m . This guarantee is particularly meaningful when m/n, the average number of vehicles per station  is large, as is often the case in practice .
Revised:  For any shared vehicle system with n stations and m vehicles, our framework provides a pricing policy with an approximation ratio of 1+(n-1)/m , which is particularly meaningful when m/n, the average number of vehicles per station is large, as is often the case in practice .
Edit intent: clarity
Original:  For any shared vehicle system with n stations and m vehicles, our framework provides a pricing policy with an approximation ratio of 1+(n-1)/m . This guarantee is particularly meaningful when m/n, the average number of vehicles per station  is large, as is often the case in practice .
Revised:  For any shared vehicle system with n stations and m vehicles, our framework provides a pricing policy with an approximation ratio of 1+(n-1)/m . This guarantee is particularly meaningful when m/n, the average number of vehicles per station , is large, as is often the case in practice .
Edit intent: fluency
Original:  } This is in contrast to the best complexity of the known algorithms for the same problem in the non-symmetric situation, which is singly exponential.
Revised:  } This is in contrast to the best complexity of the known algorithms for the same problems in the non-symmetric situation, which is singly exponential.
Edit intent: fluency