wanyu/IteraTeR_v2 huggingface dataset:
Original:  To do this, we use the  Kokkos framework, which is designed to enable high performance across multiple architectures with a single code even though these architectures handle fine-grained parallelism in varying ways.
Revised:  To do this, we use the Kokkos framework, which enables a single code to achieve high performance across multiple architectures with a single code even though these architectures handle fine-grained parallelism in varying ways.
Edit intent: clarity
Original:  To do this, we use the  Kokkos framework, which is designed to enable high performance across multiple architectures with a single code even though these architectures handle fine-grained parallelism in varying ways.
Revised:  To do this, we use the Kokkos framework, which is designed to enable high performance across multiple architectures that differ in how they approach fine-grained parallelism in varying ways.
Edit intent: clarity
Original:  Not only are the specifics of the implementation interesting to persons tuning tensor computations, but also potentially to other high-performance software developers looking to understand ways to make their codesportable across architectures without supporting multiple code bases.
Revised:  Not only are the specifics of our approaches and implementation interesting for tuning tensor computations, but also potentially to other high-performance software developers looking to understand ways to make their codesportable across architectures without supporting multiple code bases.
Edit intent: clarity
Original:  Not only are the specifics of the implementation interesting to persons tuning tensor computations, but also potentially to other high-performance software developers looking to understand ways to make their codesportable across architectures without supporting multiple code bases.
Revised:  Not only are the specifics of the implementation interesting to persons tuning tensor computations, but they also provide a roadmap for developing other portable high-performance software developers looking to understand ways to make their codesportable across architectures without supporting multiple code bases.
Edit intent: clarity
Original:  This format reorders the traversal of tensor nonzeros in the MTTKRP to reduce atomic-write contention ;
Revised:  This format reorders the traversal of tensor nonzeros to reduce atomic-write contention ;
Edit intent: clarity
Original:  We show that we are competitive with state-of-the-art approaches available in the literature while having the advantage of being able to run on a wider of variety of architectures with a single code .
Revised:  We show that we are competitive with state-of-the-art approaches available in the literature while having the advantage of being able to run on a variety of architectures with a single code .
Edit intent: clarity
Original:  We show that we are competitive with state-of-the-art approaches available in the literature while having the advantage of being able to run on a wider of variety of architectures with a single code .
Revised:  We show that we are competitive with state-of-the-art approaches available in the literature while having the advantage of being able to run on a wider of variety of architectures .
Edit intent: clarity
Original:  In most of these models it is assumed that the attacks by the exploiters are random, so that the effect of victim selection by exploiters on coevolutionary patterns remains unexplored.
Revised:  These models usually assume that the attacks by the exploiters are random, so that the effect of victim selection by exploiters on coevolutionary patterns remains unexplored.
Edit intent: clarity
Original:  In most of these models it is assumed that the attacks by the exploiters are random, so that the effect of victim selection by exploiters on coevolutionary patterns remains unexplored.
Revised:  In most of these models it is assumed that the attacks by the exploiters are random, so the effect of victim selection by exploiters on coevolutionary patterns remains unexplored.
Edit intent: fluency
Original:  Here we study an individual-based model for the coevolution of predators and prey in which every individual predator can attack only a small number of prey any given time .
Revised:  Here we analytically studied the payoff for predators and prey in which every individual predator can attack only a small number of prey any given time .
Edit intent: clarity
Original:  However, for small population sizes  prey selection can cause other coevolutionary patternsthat have not yet been reported .
Revised:  However, for small population sizes , prey selection can cause other coevolutionary patternsthat have not yet been reported .
Edit intent: fluency
Original:  However, for small population sizes  prey selection can cause other coevolutionary patternsthat have not yet been reported .
Revised:  However, for small population sizes prey selection can cause unexpected coevolutionary patterns .
Edit intent: clarity
Original:  Depending on the underlying objectives, we develop a general methodology for determining whether a given risk measurement related optimization problem is robust .
Revised:  We develop a general methodology for determining whether a given risk measurement related optimization problem is robust .
Edit intent: clarity
Original:  Motivated by practical issues from financial regulation, we give special attention to the two most widely used risk measures in the industry, Value-at-Risk (VaR) and Expected Shortfall (ES).
Revised:  Motivated by practical issues from financial regulation, special attention is given to the two most widely used risk measures in the industry, Value-at-Risk (VaR) and Expected Shortfall (ES).
Edit intent: clarity
Original:  We discover that for many simple representative optimization problems, VaR generally leads to non-robust optimizers whereas ES generally leads to robust ones.
Revised:  We establish that for a class of general optimization problems, VaR generally leads to non-robust optimizers whereas ES generally leads to robust ones.
Edit intent: clarity
Original:  We discover that for many simple representative optimization problems, VaR generally leads to non-robust optimizers whereas ES generally leads to robust ones.
Revised:  We discover that for many simple representative optimization problems, VaR leads to non-robust optimizers whereas ES generally leads to robust ones.
Edit intent: fluency
Original:  Our results thus shed light from a new angle on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation.
Revised:  Our results offer extra insight on the ongoing discussion about the comparative advantages of VaR and ES in banking and insurance regulation.
Edit intent: clarity
Original:  Our notion of robustness is conceptually different from the field of robust optimization, to which some interesting links are discovered .
Revised:  Our notion of robustness is conceptually different from the field of robust optimization, to which some interesting links are derived .
Edit intent: clarity
Original:  Specifically , we focus on data sets arising from " scenes ", configurations of physical objects ;
Revised:  Specifically , we focus on data sets arising from " scene ", configurations of physical objects ;
Edit intent: fluency
Original:  Specifically , we focus on data sets arising from " scenes ", configurations of physical objects ;
Revised:  Specifically , we focus on data sets arising from " scenes ", a configuration of physical objects ;
Edit intent: fluency
Original:  The syntax of Scenic makes it easy to specify complex relationships between the positions and orientations of objects.
Edit intent: coherence
Original:  We implement an improviser for Scenicscenarios and apply it in a case study generating synthetic data sets for a convolutional neural network designed to detect cars in road images .
Revised:  We implement an improviser for Scenicscenarios and apply it in a case study on a convolutional neural network designed to detect cars in road images .
Edit intent: clarity
Original: Despite deep reinforcement learning has recently achieved great successes, however in multiagent environments, a number of challenges still remain.
Edit intent: coherence
Original:  Multiagent reinforcement learning (MARL) is commonly considered to suffer from the problem of non-stationary environments and exponentially increasing policy space.
Revised:  Multiagent reinforcement learning (MARL) is commonly considered to suffer from non-stationary environments and exponentially increasing policy space.
Edit intent: clarity
Original:  It would be even more challenging to learn effective policies in circumstances where the rewards are sparse and delayed over long trajectories.
Revised:  It would be even more challenging when rewards are sparse and delayed over long trajectories.
Edit intent: clarity
Original:  In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning ( hierarchical deep MARL ) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed.
Revised:  In this paper, we study hierarchical deep MARL ) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed.
Edit intent: coherence
Original:  In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning ( hierarchical deep MARL ) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed.
Revised:  In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning ( hierarchical deep MARL in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed.
Edit intent: fluency
Original:  Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER) . We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.
Revised:  Moreover, to alleviate the issue of the sparse transitions at the high level of abstraction and the non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER) . We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.
Edit intent: clarity
Original:  Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER) . We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.
Revised:  Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity of multiagent learning . We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.
Edit intent: clarity
Original:  Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER) . We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.
Revised:  Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER) . We empirically demonstrate the effectiveness of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.
Edit intent: clarity
Original: This paper presents the development of a vascularized breast tumor and healthy / tumorigenic liver microenvironments-on-a-chip connected in series.
Revised: This paper presents the development of a vascularized breast tumor and healthy or tumorigenic liver microenvironments-on-a-chip connected in series.
Edit intent: fluency
Original:  This  device enables the dynamic determination of vessel permeability, transport of nanoparticles/drugs and their associated efficacy , and toxicity to the liver.
Revised:  This device enables the dynamic determination of vessel permeability, the measurement of drug and nanoparticle transport, and the assessment of the associated efficacy and toxicity to the liver.
Edit intent: clarity
Original:  The platform is utilized to determine the effect of particle size on the spatiotemporal diffusion of particles through each microenvironment  independently and in response to  circulation of particles in varying sequence of microenvironments.
Revised:  The platform is utilized to determine the effect of particle size on the spatiotemporal diffusion of particles through each microenvironment , both independently and in response to circulation of particles in varying sequence of microenvironments.
Edit intent: clarity
Original:  The platform is utilized to determine the effect of particle size on the spatiotemporal diffusion of particles through each microenvironment  independently and in response to  circulation of particles in varying sequence of microenvironments.
Revised:  The platform is utilized to determine the effect of particle size on the spatiotemporal diffusion of particles through each microenvironment independently and in response to the circulation of particles in varying sequence of microenvironments.
Edit intent: fluency
Original:  The platform is utilized to determine the effect of particle size on the spatiotemporal diffusion of particles through each microenvironment  independently and in response to  circulation of particles in varying sequence of microenvironments.
Revised:  The platform is utilized to determine the effect of particle size on the spatiotemporal diffusion of particles through each microenvironment independently and in response to circulation of particles in varying sequences of microenvironments.
Edit intent: fluency
Original:  The results show that when breast cancer cells were cultured in the microenvironments they had a 2.62 fold higher vessel porosity relative to vessels within healthy liver microenvironments.
Revised:  The results show that when breast cancer cells were cultured in the microenvironments they had a 2.62 -fold higher vessel porosity relative to vessels within healthy liver microenvironments.
Edit intent: fluency
Original:  Hence, the permeability of  tumor microenvironment increased by 2.35 to 2.77 fold compared to healthy liver for small and large particles, respectively.
Revised:  Hence, the permeability of the tumor microenvironment increased by 2.35 to 2.77 fold compared to healthy liver for small and large particles, respectively.
Edit intent: fluency
Original:  Hence, the permeability of  tumor microenvironment increased by 2.35 to 2.77 fold compared to healthy liver for small and large particles, respectively.
Revised:  Hence, the permeability of tumor microenvironment increased by 2.35 to 2.77 -fold compared to a healthy liver for small and large particles, respectively.
Edit intent: fluency
Original:  The ECM accumulation rate of larger particles was 2.57 fold lower than smaller particles in  healthy liver.
Revised:  The ECM accumulation rate of larger particles was 2.57 -fold lower than smaller particles in healthy liver.
Edit intent: fluency
Original:  The ECM accumulation rate of larger particles was 2.57 fold lower than smaller particles in  healthy liver.
Revised:  The ECM accumulation rate of larger particles was 2.57 fold lower than smaller particles in a healthy liver.
Edit intent: fluency
Original:  However, the accumulation rate was 5.57 fold greater in the breast tumor microenvironment.
Revised:  However, the accumulation rate was 5.57 -fold greater in the breast tumor microenvironment.
Edit intent: fluency
Original:   Ultimately, the platform could be utilized to determine the impact of the tissue /tumor microenvironmentor drug/nanoparticle properties on transport, efficacy, selectivity, and toxicity in a dynamic, and high throughput manner for use in treatment optimization.
Revised:  Ultimately, the platform could be utilized to determine the impact of the tissue or tumor microenvironment, or drug and nanoparticle properties, on transport, efficacy, selectivity, and toxicity in a dynamic, and high throughput manner for use in treatment optimization.
Edit intent: fluency
Original: The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a perturbed Metropolis-Hastings (MH) algorithm, provides a simple approach for approximate sampling when the target distribution is doubly-intractable or contains latent variables.
Revised: The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a perturbed Metropolis-Hastings (MH) algorithm, provides an approach for approximate sampling when the target distribution is doubly-intractable or contains latent variables.
Edit intent: clarity
Original:  We assume that the associated unperturbed Markov chain is geometrically ergodic and show explicit estimates of the difference between the n-th step distributions of the perturbed MCwM and the unperturbed MH chains.
Revised:  We assume that the associated unperturbed Markov chain is geometrically ergodic , we show explicit estimates of the difference between the n-th step distributions of the perturbed MCwM and the unperturbed MH chains.
Edit intent: fluency
Original:  Moreover, we need to verify stability of the perturbed chain , either through a Lyapunov condition, or by restricting it to the center of the state space .
Revised:  Moreover, we need to verify stability of the perturbed chain .
Edit intent: coherence
Original:  one such stochastic seeding strategy is to select random network neighbors of random individuals , thus exploiting a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual . Empirical evaluations of these strategies have demanded large field experiments designed specifically for this purpose , but these experiments have yielded relatively imprecise estimates of the relative efficacy of these seeding strategies.
Revised:  one such stochastic seeding strategy is to select random network neighbors of random individuals ; this exploits a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual . Empirical evaluations of these strategies have demanded large field experiments designed specifically for this purpose , but these experiments have yielded relatively imprecise estimates of the relative efficacy of these seeding strategies.
Edit intent: coherence
Original:  one such stochastic seeding strategy is to select random network neighbors of random individuals , thus exploiting a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual . Empirical evaluations of these strategies have demanded large field experiments designed specifically for this purpose , but these experiments have yielded relatively imprecise estimates of the relative efficacy of these seeding strategies.
Revised:  one such stochastic seeding strategy is to select random network neighbors of random individuals , thus exploiting a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual . Empirical evaluations of these strategies have demanded large field experiments designed specifically for this purpose and have yielded relatively imprecise estimates of the relative efficacy of these seeding strategies.
Edit intent: coherence
Original:  one such stochastic seeding strategy is to select random network neighbors of random individuals , thus exploiting a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual . Empirical evaluations of these strategies have demanded large field experiments designed specifically for this purpose , but these experiments have yielded relatively imprecise estimates of the relative efficacy of these seeding strategies.
Revised:  one such stochastic seeding strategy is to select random network neighbors of random individuals , thus exploiting a version of the friendship paradox, whereby the friend of a random individual is expected to have more friends than a random individual . Empirical evaluations of these strategies have demanded large field experiments designed specifically for this purpose , but these experiments have yielded relatively imprecise comparisons of strategies.
Edit intent: clarity
Original:  Here we show both how stochastic seeding strategies can be evaluated  using existing data arising from randomized experiments in networks designed for other purposes  and how to design much more efficient experiments for this specific evaluation .
Revised:  Here we show how stochastic seeding strategies can be evaluated using existing data arising from randomized experiments in networks designed for other purposes and how to design much more efficient experiments for this specific evaluation .
Edit intent: coherence
Original:  Here we show both how stochastic seeding strategies can be evaluated  using existing data arising from randomized experiments in networks designed for other purposes  and how to design much more efficient experiments for this specific evaluation .
Revised:  Here we show both how stochastic seeding strategies can be evaluated using existing data arising from experiments designed for other purposes and how to design much more efficient experiments for this specific evaluation .
Edit intent: clarity
Original:  Here we show both how stochastic seeding strategies can be evaluated  using existing data arising from randomized experiments in networks designed for other purposes  and how to design much more efficient experiments for this specific evaluation .
Revised:  Here we show both how stochastic seeding strategies can be evaluated using existing data arising from randomized experiments in networks designed for other purposes , and how to design much more efficient experiments for this specific evaluation .
Edit intent: fluency
Original:  Here we show both how stochastic seeding strategies can be evaluated  using existing data arising from randomized experiments in networks designed for other purposes  and how to design much more efficient experiments for this specific evaluation .
Revised:  Here we show both how stochastic seeding strategies can be evaluated using existing data arising from randomized experiments in networks designed for other purposes and how to design more efficient experiments for this specific evaluation .
Edit intent: clarity
Original:  Here we show both how stochastic seeding strategies can be evaluated  using existing data arising from randomized experiments in networks designed for other purposes  and how to design much more efficient experiments for this specific evaluation .
Revised:  Here we show both how stochastic seeding strategies can be evaluated using existing data arising from randomized experiments in networks designed for other purposes and how to design much more efficient experiments .
Edit intent: clarity