wanyu/IteraTeR_v2 huggingface dataset:
Original:  Here we provide a systematic study on a nonlinear dynamical model of acute cell injury and characterize the global dynamics of a nonautonomous version of the theory.
Revised:  In this work we develop a modeling approach by extending an autonomous nonlinear dynamic theory of acute cell injury and characterize the global dynamics of a nonautonomous version of the theory.
Edit intent: clarity
Original:  This nonautonomous theory of acute cell injury thus provides a quantitative framework for understanding cell death and recovery and  developing effective therapeutics for acute injury.
Revised:  The nonautonomous theory of acute cell injury thus provides a quantitative framework for understanding cell death and recovery and developing effective therapeutics for acute injury.
Edit intent: fluency
Original:  This nonautonomous theory of acute cell injury thus provides a quantitative framework for understanding cell death and recovery and  developing effective therapeutics for acute injury.
Revised:  This nonautonomous theory of acute cell injury provides an improved quantitative framework for understanding cell death and recovery and developing effective therapeutics for acute injury.
Edit intent: clarity
Original:  This nonautonomous theory of acute cell injury thus provides a quantitative framework for understanding cell death and recovery and  developing effective therapeutics for acute injury.
Revised:  This nonautonomous theory of acute cell injury thus provides a quantitative framework for understanding cell death and recovery and lays a foundation for developing effective therapeutics for acute injury.
Edit intent: clarity
Original:  The Schmidt-rank width is an entanglement measure of quantum state , meaning this algorithm is efficient if the original state has little entanglement.
Revised:  The Schmidt-rank width is an entanglement measure of quantum states , meaning this algorithm is efficient if the original state has little entanglement.
Edit intent: fluency
Original:  Thus, it outperforms existing approaches in mixed traffic scenarios, as we show in our simulationenvironment .
Revised:  Thus, it outperforms existing approaches in mixed traffic scenarios, as we show in simulation .
Edit intent: clarity
Original: We propose a combination of the Eulerian Lagrangian Localised Adjoint Method (ELLAM) and the Modified Method of Characteristics (MMOC) for time-dependent advection-dominated PDEs.
Revised: We propose a combination of the Eulerian Lagrangian Localised Adjoint Method (ELLAM) and the Modified Method of Characteristics (MMOC) for time-dependent advection-domina\-ted PDEs.
Edit intent: fluency
Original:  A convergence analysis of the MMOC scheme, motivated by our previous work 5 , is provided  which can be extended to obtain the convergence of GEM scheme.
Revised:  A convergence result of the MMOC scheme, motivated by our previous work 5 , is provided which can be extended to obtain the convergence of GEM scheme.
Edit intent: clarity
Original:  A convergence analysis of the MMOC scheme, motivated by our previous work 5 , is provided  which can be extended to obtain the convergence of GEM scheme.
Revised:  A convergence analysis of the MMOC scheme, motivated by our previous work 5 , is provided , which can be extended to obtain the convergence of GEM scheme.
Edit intent: fluency
Original: In the process of collectively inventing new words for new con- cepts in a population, conflicts can quickly become numerous, in the form of synonymy and homonymy.
Revised: In the process of collectively inventing new words for new concepts in a population, conflicts can quickly become numerous, in the form of synonymy and homonymy.
Edit intent: fluency
Original:  Is there an efficient be- havior that could help balance the two?
Revised:  Is there an efficient behavior that could help balance the two?
Edit intent: fluency
Original:  The Naming Game is a multi-agent computational model for the emergence of lan- guage , focusing on the negotiation of new lexical conventions, where a common lexicon self-organizes but going through a phase of high complexity.
Revised:  The Naming Game is a multi-agent computational model for the emergence of language , focusing on the negotiation of new lexical conventions, where a common lexicon self-organizes but going through a phase of high complexity.
Edit intent: fluency
Original:  How- ever , those strategies were relying on ad hoc heuristics highly dependent on fine-tuning of parameters.
Revised:  However , those strategies were relying on ad hoc heuristics highly dependent on fine-tuning of parameters.
Edit intent: coherence
Original:  The mea- sure does not rely on heavy computation, and is cognitively plausible.
Revised:  The measure does not rely on heavy computation, and is cognitively plausible.
Edit intent: fluency
Original:  The new strategy yields an efficient control of com- plexity growth, along with a faster agreement process.
Revised:  The new strategy yields an efficient control of complexity growth, along with a faster agreement process.
Edit intent: fluency
Original: We present a circuit synthesis methodology to emulate in hardware the electrical behavior of a single bursting neuron.
Revised: We present a novel methodology to enable control of a neuromorphic circuit in close analogy with the physiological neuromodulation of a single bursting neuron.
Edit intent: clarity
Original: We present a circuit synthesis methodology to emulate in hardware the electrical behavior of a single bursting neuron.
Revised: We present a circuit synthesis methodology to emulate in hardware the electrical behavior of a single neuron.
Edit intent: clarity
Original:  As an input-output approach, I-V curve shaping proves effective in enabling robust and accurate control of the circuit behavior , suggesting a close analogy to the biophysical mechanisms of neuromodulation  .
Revised:  As an input-output approach, I-V curve shaping proves effective in enabling robust and accurate control of the circuit behavior and resembles the biophysical mechanisms of neuromodulation .
Edit intent: coherence
Original:  The data concerning the virtual spreading are easily obtainable, in particular the diffusion of information during the announcement for the discovery of the Higgs Boson on Twitter \textsuperscript{TM was recorded and investigated in detail.
Revised:  The data concerning the virtual spreading are easily obtainable, in particular the diffusion of information during the announcement for the discovery of the Higgs Boson on Twitter was recorded and investigated in detail.
Edit intent: clarity
Original:  Here we show that by forming chasing triplets in the predation process among the consumers and resources  , the number of coexisting species of consumers can exceed that of resources at steady state, naturally breaking the competitive exclusion principle.
Revised:  Here we show that by forming chasing triplets among the consumers and resources , the number of coexisting species of consumers can exceed that of resources at steady state, naturally breaking the competitive exclusion principle.
Edit intent: clarity
Original:  Our model can be broadly applicable to explain the biodiversity of many consumer-resource ecosystems and  deepen our understanding of biodiversity in nature.
Revised:  Our model can be broadly applicable to explain the biodiversity of many consumer-resource ecosystems and hence deepen our understanding of biodiversity in nature.
Edit intent: coherence
Original:  One great challenge is embodied in the so-called competitive exclusion principle: the number of  species in steady coexistence cannot exceed the number of resources.
Revised:  An outstanding challenge is embodied in the so-called competitive exclusion principle: the number of species in steady coexistence cannot exceed the number of resources.
Edit intent: clarity
Original:  One great challenge is embodied in the so-called competitive exclusion principle: the number of  species in steady coexistence cannot exceed the number of resources.
Revised:  One great challenge is embodied in the so-called competitive exclusion principle: the number of species in steady coexistence cannot exceed that of resources.
Edit intent: clarity
Original:   In the past five decades, various mechanisms have been proposed to overcome the limit on diversity set by the competitive exclusion principle .
Revised:  In the past five decades, various mechanisms have been proposed to explain the violation of the Competitive Exclusion Principle .
Edit intent: clarity
Original:  Here we show that by forming chasing triplets among the consumers and resources in the predation process, the number of coexisting species of consumers can exceed that of resources at steady state, naturally breaking the competitive exclusion principle .
Revised:  Here we show that by forming chasing triplets among the consumers and resources in the predation process, the Competitive Exclusion Principle can be naturally violated .
Edit intent: clarity
Original:  Our model can be broadly applicable to explain the biodiversity of many consumer-resource ecosystems and hence deepen our understanding of biodiversity in nature.
Revised:  Our model can be broadly applicable to explain the biodiversity of many consumer-resource ecosystems and deepen our understanding of biodiversity in nature.
Edit intent: coherence
Original:  Here we show that by forming chasing triplets among the consumers and resources in the predation process, the Competitive Exclusion Principle can be naturally violated.
Revised:  Here we show that by forming chasing triplets among the consumers and resources in the consumption process, the Competitive Exclusion Principle can be naturally violated.
Edit intent: clarity
Original:  Our model can be broadly applicable to explain the biodiversity of many consumer-resource ecosystems and deepen our understanding of biodiversity in nature.
Revised:  The modeling framework developed here is broadly applicable and can be used to explain the biodiversity of many consumer-resource ecosystems and deepen our understanding of biodiversity in nature.
Edit intent: clarity
Original:  Our model can be broadly applicable to explain the biodiversity of many consumer-resource ecosystems and deepen our understanding of biodiversity in nature.
Revised:  Our model can be broadly applicable to explain the biodiversity of many consumer-resource ecosystems and hence deepens our understanding of biodiversity in nature.
Edit intent: coherence
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: fluency
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V %DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: fluency
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: clarity
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M %DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: fluency
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: fluency
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O %DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: fluency
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: fluency
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R %DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: clarity
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: fluency
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H %DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: fluency
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: fluency
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P %DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: clarity
Original: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% E xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Revised: We propose a novel algorithmic framework of %DIFDELCMD < {\bf %%% V ariable%DIFDELCMD < {\bf %%% M etric%DIFDELCMD < {\bf %DIFDELCMD < %%% O ver-%DIFDELCMD < {\bf %%% R elaxed%DIFDELCMD < {\bf %%% H ybrid%DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %%% xtra-gradient (VMOR-HPE) method with a global convergence guarantee for the maximal monotone operator inclusion problem.
Edit intent: fluency
Original:  yielding a new algorithm, namely nonsymmetric %DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %DIFDELCMD < %%% A lternating%DIFDELCMD < {\bf %%% D irection%DIFDELCMD < {\bf %%% M ethod of%DIFDELCMD < {\bf %%% M ultipliers with a preconditioned %DIFDELCMD < {\bf %%% E xtra-gradient step in which the preconditioned metric is generated by a blockwise %DIFDELCMD < {\bf %%% B arzilai-%DIFDELCMD < {\bf %%% B orwein line search technique (PADMM-EBB).
Revised:  yielding a new algorithm, namely nonsymmetric %DIFDELCMD < {\bf %%% roximal%DIFDELCMD < {\bf %DIFDELCMD < %%% A lternating%DIFDELCMD < {\bf %%% D irection%DIFDELCMD < {\bf %%% M ethod of%DIFDELCMD < {\bf %%% M ultipliers with a preconditioned %DIFDELCMD < {\bf %%% E xtra-gradient step in which the preconditioned metric is generated by a blockwise %DIFDELCMD < {\bf %%% B arzilai-%DIFDELCMD < {\bf %%% B orwein line search technique (PADMM-EBB).
Edit intent: fluency
Original:  yielding a new algorithm, namely nonsymmetric %DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %DIFDELCMD < %%% A lternating%DIFDELCMD < {\bf %%% D irection%DIFDELCMD < {\bf %%% M ethod of%DIFDELCMD < {\bf %%% M ultipliers with a preconditioned %DIFDELCMD < {\bf %%% E xtra-gradient step in which the preconditioned metric is generated by a blockwise %DIFDELCMD < {\bf %%% B arzilai-%DIFDELCMD < {\bf %%% B orwein line search technique (PADMM-EBB).
Revised:  yielding a new algorithm, namely nonsymmetric %DIFDELCMD < {\bf %%% P %DIFDELCMD < {\bf %DIFDELCMD < %%% A lternating%DIFDELCMD < {\bf %%% D irection%DIFDELCMD < {\bf %%% M ethod of%DIFDELCMD < {\bf %%% M ultipliers with a preconditioned %DIFDELCMD < {\bf %%% E xtra-gradient step in which the preconditioned metric is generated by a blockwise %DIFDELCMD < {\bf %%% B arzilai-%DIFDELCMD < {\bf %%% B orwein line search technique (PADMM-EBB).
Edit intent: fluency
Original:  yielding a new algorithm, namely nonsymmetric %DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %DIFDELCMD < %%% A lternating%DIFDELCMD < {\bf %%% D irection%DIFDELCMD < {\bf %%% M ethod of%DIFDELCMD < {\bf %%% M ultipliers with a preconditioned %DIFDELCMD < {\bf %%% E xtra-gradient step in which the preconditioned metric is generated by a blockwise %DIFDELCMD < {\bf %%% B arzilai-%DIFDELCMD < {\bf %%% B orwein line search technique (PADMM-EBB).
Revised:  yielding a new algorithm, namely nonsymmetric %DIFDELCMD < {\bf %%% P roximal%DIFDELCMD < {\bf %DIFDELCMD < %%% lternating%DIFDELCMD < {\bf %%% D irection%DIFDELCMD < {\bf %%% M ethod of%DIFDELCMD < {\bf %%% M ultipliers with a preconditioned %DIFDELCMD < {\bf %%% E xtra-gradient step in which the preconditioned metric is generated by a blockwise %DIFDELCMD < {\bf %%% B arzilai-%DIFDELCMD < {\bf %%% B orwein line search technique (PADMM-EBB).
Edit intent: fluency