wanyu/IteraTeR_v2 huggingface dataset:
Original:  Furthermore, when ODM is articulated with three heuristics, competitive performance can be achieved on benchmark problems with search dimensions up to 10000.
Revised:  Furthermore, when ODM is articulated with three heuristics, competitive performance is reported across different sets of benchmark problems with search dimensions up to 10000.
Edit intent: clarity
Original:  One method to overcome this difficulty is to coarse-grain the model by decomposing the domain into a number of smaller sub-domains and homogenizing the heterogeneous medium within each sub-domain.
Revised:  One method to overcome this difficulty is to homogenize the model by decomposing the domain into a number of smaller sub-domains and homogenizing the heterogeneous medium within each sub-domain.
Edit intent: clarity
Original:  In the resulting coarse-grained model, the fine-scale diffusivity on each sub-domain is replaced by an effective diffusivity  , calculated from the solution of an appropriate boundary value problem over the sub-domain.
Revised:  In the resulting coarse-grained model, the fine-scale diffusivity with an effective diffusivity , calculated from the solution of an appropriate boundary value problem over the sub-domain.
Edit intent: clarity
Original:  We compare the new method to a standard finite volume method and show that the equivalent accuracy can be achieved in less computational time for several standard test cases.
Revised:  We compare our new method to a standard finite volume method and show that the equivalent accuracy can be achieved in less computational time for several standard test cases.
Edit intent: fluency
Original:  We compare the new method to a standard finite volume method and show that the equivalent accuracy can be achieved in less computational time for several standard test cases.
Revised:  We compare the new method to a standard finite volume method and show that equivalent accuracy can be achieved in less computational time for several standard test cases.
Edit intent: fluency
Original:  We also demonstrate how the new method can be used to homogenize complex heterogeneous geometries represented by a grid of blocks.
Revised:  We also demonstrate how the new method can be applied to complex heterogeneous geometries represented by a grid of blocks.
Edit intent: clarity
Original:  These results indicate that our new semi-analytical method has the potential to significantly speed up coarse-grain simulations of flow in heterogeneous media.
Revised:  These results indicate that our new semi-analytical method has the potential to significantly speed up simulations of flow in heterogeneous media.
Edit intent: clarity
Original:  In this paper, we present a new semi-analytical method for solving the boundary value problem and computing the effective diffusivity for pixellated , locally-isotropic, heterogeneous media.
Revised:  In this paper, we present a new semi-analytical method for solving this boundary value problem and computing the effective diffusivity for pixellated , locally-isotropic, heterogeneous media.
Edit intent: fluency
Original:  In this paper, we present a new semi-analytical method for solving the boundary value problem and computing the effective diffusivity for pixellated , locally-isotropic, heterogeneous media.
Revised:  In this paper, we present a new semi-analytical method for solving the boundary value problem and computing the effective diffusivity for pixelated , locally-isotropic, heterogeneous media.
Edit intent: fluency
Original:  We compare our new  method to a standard finite volume method and show that equivalent accuracy can be achieved in less computational time for several standard test cases.
Revised:  We compare our new solution method to a standard finite volume method and show that equivalent accuracy can be achieved in less computational time for several standard test cases.
Edit intent: clarity
Original: Direct numerical simulation of flow through heterogeneous media can be difficult due to the computational cost of resolving fine-scale heterogeneities.
Revised: Direct numerical simulation of diffusion through heterogeneous media can be difficult due to the computational cost of resolving fine-scale heterogeneities.
Edit intent: clarity
Original:  One method to overcome this difficulty is to homogenize the model by replacing the spatially-varying fine-scale diffusivity with an effective diffusivity valid across the entire domain or several non-overlapping sub-domains, calculated from the solution of an appropriate boundary value problem.
Revised:  One method to overcome this difficulty is to homogenize the model by replacing the spatially-varying fine-scale diffusivity with an effective diffusivity calculated from the solution of an appropriate boundary value problem.
Edit intent: clarity
Original:  In this paper, we present a new semi-analytical method for solving this boundary value problem and computing the effective diffusivity for pixelated , locally-isotropic, heterogeneous media.
Revised:  In this paper, we present a new semi-analytical method for solving this boundary value problem and computing the effective diffusivity for pixellated , locally-isotropic, heterogeneous media.
Edit intent: clarity
Original:  These results indicate that our new semi-analytical method has the potential to significantly speed up simulations of flow in heterogeneous media.
Revised:  These results indicate that our new semi-analytical method has the potential to significantly speed up simulations of diffusion in heterogeneous media.
Edit intent: clarity
Original:  Also, we demonstrate that seven biological systems are antifragile.
Revised:  Also, we demonstrated that seven biological systems are antifragile.
Edit intent: fluency
Original:  We retain the advantages of the previous versions such as TCP/IP layers support, software programmability and low-cost front-end and increase the throughput at the transport layer to 400 kb/s (a factor of 4 with respect to the previous version) and  the distance by a factor of 3.5.
Revised:  We retain the advantages of the previous versions such as TCP/IP layers support, software programmability and low-cost front-end and increase the throughput at the transport layer to 400 kb/s (a factor of 4 with respect to the previous version) and increase the distance by a factor of 3.5.
Edit intent: fluency
Original:  We further improve the software robustness of the system and reduce the size at similar hardware cost.
Revised:  We further improve the software robustness of the system and reduce the form factor at similar hardware cost.
Edit intent: clarity
Original: This paper deals with subsampled Spectral gradient methods for minimizing finite sum.
Revised: This paper deals with subsampled spectral gradient methods for minimizing finite sum.
Edit intent: fluency
Original:  Numerical results on well known binary classification problems are given showing the effectiveness of the subsampling approach .
Revised:  Numerical results on well known binary classification problems are given to show the effectiveness of the subsampling approach .
Edit intent: fluency
Original:  The immersed boundary  method is a widely recognized and effective modeling paradigm for simulating fluid-structure interaction  in such systems, but a difficulty of the IB formulation of these problems is that the pressure and viscous stress are generally discontinuous at fluid-solid interfaces .
Revised:  The immersed boundary (IB) method is a widely recognized and effective modeling paradigm for simulating fluid-structure interaction in such systems, but a difficulty of the IB formulation of these problems is that the pressure and viscous stress are generally discontinuous at fluid-solid interfaces .
Edit intent: clarity
Original:  The immersed boundary  method is a widely recognized and effective modeling paradigm for simulating fluid-structure interaction  in such systems, but a difficulty of the IB formulation of these problems is that the pressure and viscous stress are generally discontinuous at fluid-solid interfaces .
Revised:  The immersed boundary method is a widely recognized and effective modeling paradigm for simulating fluid-structure interaction in such systems, but a difficulty of the IB formulation is that the pressure and viscous stress are generally discontinuous at fluid-solid interfaces .
Edit intent: clarity
Original:  The immersed boundary  method is a widely recognized and effective modeling paradigm for simulating fluid-structure interaction  in such systems, but a difficulty of the IB formulation of these problems is that the pressure and viscous stress are generally discontinuous at fluid-solid interfaces .
Revised:  The immersed boundary method is a widely recognized and effective modeling paradigm for simulating fluid-structure interaction in such systems, but a difficulty of the IB formulation of these problems is that the pressure and viscous stress are generally discontinuous at the interface .
Edit intent: clarity
Original:  The immersed interface method  is an IB-like approach to FSI that sharply imposes stress jump conditions, enabling higher-order accuracy, but prior applications of the IIM have been largely restricted to cases with smooth geometries .
Revised:  The immersed interface method (IIM) is an IB-like approach to FSI that sharply imposes stress jump conditions, enabling higher-order accuracy, but prior applications of the IIM have been largely restricted to cases with smooth geometries .
Edit intent: clarity
Original:  The immersed interface method  is an IB-like approach to FSI that sharply imposes stress jump conditions, enabling higher-order accuracy, but prior applications of the IIM have been largely restricted to cases with smooth geometries .
Revised:  The immersed interface method is an IB-like approach to FSI that sharply imposes stress jump conditions, enabling higher-order accuracy, but prior applications of the IIM have been largely restricted to methods that rely on smooth representations of the interface geometry .
Edit intent: clarity
Original:  Our results also demonstrate  that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we demonstrate second-order global convergence rates along with nearly second-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure field .
Revised:  Our results also demonstrate that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global 2nd-order accuracy . Specifically,we demonstrate second-order global convergence rates along with nearly second-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure field .
Edit intent: clarity
Original:  Our results also demonstrate  that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we demonstrate second-order global convergence rates along with nearly second-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure field .
Revised:  Our results also demonstrate that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we show 2nd-order global convergence rate along with nearly second-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure field .
Edit intent: clarity
Original:  Our results also demonstrate  that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we demonstrate second-order global convergence rates along with nearly second-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure field .
Revised:  Our results also demonstrate that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we demonstrate second-order global convergence rates along with nearly 2nd-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure field .
Edit intent: fluency
Original:  Our results also demonstrate  that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we demonstrate second-order global convergence rates along with nearly second-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure field .
Revised:  Our results also demonstrate that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we demonstrate second-order global convergence rates along with nearly second-order local convergence in the Eulerian velocity , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure field .
Edit intent: fluency
Original:  Our results also demonstrate  that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we demonstrate second-order global convergence rates along with nearly second-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure field .
Revised:  Our results also demonstrate that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we demonstrate second-order global convergence rates along with nearly second-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with 1st-order local convergence for the Eulerian pressure field .
Edit intent: clarity
Original:  Our results also demonstrate  that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we demonstrate second-order global convergence rates along with nearly second-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure field .
Revised:  Our results also demonstrate that only the lowest-order jump conditions for the pressure and velocity gradient are required to realize global second-order accuracywith the IIM . Specifically,we demonstrate second-order global convergence rates along with nearly second-order local convergence in the Eulerian velocity field , and between first-~and second-order global convergence rates along with approximately first-order local convergence for the Eulerian pressure .
Edit intent: fluency
Original:  We also demonstrate approximately second-order local convergence in the interfacial displacement and velocity along with first-order local convergence in the fluid traction along the interface .
Revised:  We also show 2nd-order local convergence in the interfacial displacement and velocity along with first-order local convergence in the fluid traction along the interface .
Edit intent: clarity
Original:  We also demonstrate approximately second-order local convergence in the interfacial displacement and velocity along with first-order local convergence in the fluid traction along the interface .
Revised:  We also demonstrate approximately second-order local convergence in the interfacial displacement and velocity along with 1st-order local convergence in the fluid traction along the interface .
Edit intent: fluency
Original:  for dynamic resource management in wireless networks . Specifically, this work considers the case of distributed multi-user MIMO (D-MIMO) based Wi-Fi networks and studies how DRL can help improve the performance of these networks, particularly in dynamic scenarios . D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously.
Revised:  to enable dynamic resource management in wireless networks . Specifically, this work considers the case of distributed multi-user MIMO (D-MIMO) based Wi-Fi networks and studies how DRL can help improve the performance of these networks, particularly in dynamic scenarios . D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously.
Edit intent: clarity
Original:  for dynamic resource management in wireless networks . Specifically, this work considers the case of distributed multi-user MIMO (D-MIMO) based Wi-Fi networks and studies how DRL can help improve the performance of these networks, particularly in dynamic scenarios . D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously.
Revised:  for dynamic resource management in Wi-Fi networks implementing distributed multi-user MIMO (D-MIMO) based Wi-Fi networks and studies how DRL can help improve the performance of these networks, particularly in dynamic scenarios . D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously.
Edit intent: clarity
Original:  for dynamic resource management in wireless networks . Specifically, this work considers the case of distributed multi-user MIMO (D-MIMO) based Wi-Fi networks and studies how DRL can help improve the performance of these networks, particularly in dynamic scenarios . D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously.
Revised:  for dynamic resource management in wireless networks . Specifically, this work considers the case of distributed multi-user MIMO (D-MIMO) . D-MIMO is a technique by which a set of wireless access points are synchronized and grouped together to jointly serve multiple users simultaneously.
Edit intent: coherence
Original:  This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks in detail -- channel assignment of D-MIMO groups, and  deciding how to cluster access points to form D-MIMO groups . A DRL framework is constructed through which an agent interacts with a D-MIMO  network, learns about the network environment, and is successful in converging to policies which augment the performance of the D-MIMO network in different scenarios .
Revised:  This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks : (i) channel assignment of D-MIMO groups, and deciding how to cluster access points to form D-MIMO groups . A DRL framework is constructed through which an agent interacts with a D-MIMO network, learns about the network environment, and is successful in converging to policies which augment the performance of the D-MIMO network in different scenarios .
Edit intent: clarity
Original:  This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks in detail -- channel assignment of D-MIMO groups, and  deciding how to cluster access points to form D-MIMO groups . A DRL framework is constructed through which an agent interacts with a D-MIMO  network, learns about the network environment, and is successful in converging to policies which augment the performance of the D-MIMO network in different scenarios .
Revised:  This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks in detail -- channel assignment of D-MIMO groups, and (ii) deciding how to cluster access points to form D-MIMO groups . A DRL framework is constructed through which an agent interacts with a D-MIMO network, learns about the network environment, and is successful in converging to policies which augment the performance of the D-MIMO network in different scenarios .
Edit intent: coherence
Original:  This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks in detail -- channel assignment of D-MIMO groups, and  deciding how to cluster access points to form D-MIMO groups . A DRL framework is constructed through which an agent interacts with a D-MIMO  network, learns about the network environment, and is successful in converging to policies which augment the performance of the D-MIMO network in different scenarios .
Revised:  This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks in detail -- channel assignment of D-MIMO groups, and deciding how to cluster access points to form D-MIMO groups . A DRL framework is constructed through which an agent interacts with a D-MIMO Wi-Fi network, learns about the network environment, and is successful in converging to policies which augment the performance of the D-MIMO network in different scenarios .
Edit intent: fluency
Original:  This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks in detail -- channel assignment of D-MIMO groups, and  deciding how to cluster access points to form D-MIMO groups . A DRL framework is constructed through which an agent interacts with a D-MIMO  network, learns about the network environment, and is successful in converging to policies which augment the performance of the D-MIMO network in different scenarios .
Revised:  This paper addresses two dynamic resource management problems pertaining to D-MIMO Wi-Fi networks in detail -- channel assignment of D-MIMO groups, and deciding how to cluster access points to form D-MIMO groups . A DRL framework is constructed through which an agent interacts with a D-MIMO network, learns about the network environment, and is successful in converging to policies which address the aforementioned problems .
Edit intent: clarity
Original:  Through extensive simulations and on-line training based on dense Wi-Fi deployments , this paper demonstrates the merits of using DRL by achieving up to a 20\% improvement in the performance of D-MIMO networks with DRL compared to when DRL is not used .
Revised:  Through extensive simulations and on-line training based on D-MIMO Wi-Fi deployments , this paper demonstrates the merits of using DRL by achieving up to a 20\% improvement in the performance of D-MIMO networks with DRL compared to when DRL is not used .
Edit intent: clarity
Original:  Through extensive simulations and on-line training based on dense Wi-Fi deployments , this paper demonstrates the merits of using DRL by achieving up to a 20\% improvement in the performance of D-MIMO networks with DRL compared to when DRL is not used .
Revised:  Through extensive simulations and on-line training based on dense Wi-Fi networks , this paper demonstrates the merits of using DRL by achieving up to a 20\% improvement in the performance of D-MIMO networks with DRL compared to when DRL is not used .
Edit intent: clarity