wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our framework composes standard techniques such as a stopping time, a martingale concentration and a closed-from solution to give a streamlined three-step recipe with a general and flexible principle to implement it. To demonstrate the power and the flexibility of our framework , we apply the framework on three very different learning problems : stochastic gradient descent for strongly convex functions, streaming principal component analysis and linear bandit with stochastic gradient descent updates.
Revised:  Our framework composes standard techniques such as a stopping time, a martingale concentration and a closed-from solution to give a streamlined three-step recipe with a general and flexible principle to tackle the "chicken and egg" problem. We demonstrate the power and the flexibility of our framework , we apply the framework on three very different learning problems : stochastic gradient descent for strongly convex functions, streaming principal component analysis and linear bandit with stochastic gradient descent updates.
Edit intent: clarity
Original:  To demonstrate the power and the flexibility of our framework , we apply the framework on three very different learning problems : stochastic gradient descent for strongly convex functions, streaming principal component analysis and linear bandit with stochastic gradient descent updates.
Revised:  To demonstrate the power and the flexibility of our framework by giving unifying analysis for three very different learning problems : stochastic gradient descent for strongly convex functions, streaming principal component analysis and linear bandit with stochastic gradient descent updates.
Edit intent: meaning-changed
Original:  To demonstrate the power and the flexibility of our framework , we apply the framework on three very different learning problems : stochastic gradient descent for strongly convex functions, streaming principal component analysis and linear bandit with stochastic gradient descent updates.
Revised:  To demonstrate the power and the flexibility of our framework , we apply the framework on three very different learning problems with both the last iterate and the strong uniform high probability convergence guarantee. The problems are stochastic gradient descent for strongly convex functions, streaming principal component analysis and linear bandit with stochastic gradient descent updates.
Edit intent: meaning-changed
Original: In this work we introduce a novel kinetic model for the study of tumour growths which highlights the role of microscopic transitions in determining a variety of equilibrium distributions.
Revised: The mathematical modeling of tumor growth has a long history, and has been mathematically formulated in several different ways. Here we tackle the problem in the case of a continuous distribution using mathematical tools from statistical physics. To this extent, we introduce a novel kinetic model for the study of tumour growths which highlights the role of microscopic transitions in determining a variety of equilibrium distributions.
Edit intent: meaning-changed
Original:  Microscopic feedback control therapies are designed to influence the natural tumour growth and to mitigate the risk factors involved in big sized tumours. Several numerical examples illustrate the effectiveness of the approach .
Revised:  Microscopic feedback control therapies are designed to influence the natural tumour growth and to mitigate the risk factors involved in big sized tumors. We further show that under a suitable scaling both the free and controlled growth models correspond to Fokker--Planck type equations for the growth distribution with variable coefficients of diffusion and drift, whose steady solutions in the free case are given by a class of generalized Gamma densities which can be characterized by fat tails. In this scaling the feedback control produces an explicit modification of the drift operator, which is shown to strongly modify the emerging distribution for the tumor size. In particular, the size distributions in presence of therapies manifest slim tails in all growth models, which corresponds to a marked mitigation of the risk factors. Numerical results confirming the theoretical analysis are also presented .
Edit intent: meaning-changed
Original:  We demonstrate how linguistic annotations from separate corpora can be merged through a similar format to thereby make annotation content simultaneously accessible. The process for developing the nanopublications is described, and SPARQL queries are performed to extract interesting content from the new representations.
Revised:  We demonstrate how linguistic annotations from separate corpora can be reliably linked from the start, and thereby be accessed and queried as if they were a single dataset. We describe how such nanopublications can be created and demonstrate how SPARQL queries can be performed to extract interesting content from the new representations.
Edit intent: clarity
Original:  In this paper, we introduce and analyze a new class of preconditioners for SGFEM that we call truncation preconditioners . Extending the idea of the mean-based preconditioner, the new preconditioners capture additional significant components of the stochastic Galerkin matrix.
Revised:  In this paper, we analyze a class of truncation preconditioners for SGFEM that we call truncation preconditioners . Extending the idea of the mean-based preconditioner, the new preconditioners capture additional significant components of the stochastic Galerkin matrix.
Edit intent: coherence
Original:  In this paper, we introduce and analyze a new class of preconditioners for SGFEM that we call truncation preconditioners . Extending the idea of the mean-based preconditioner, the new preconditioners capture additional significant components of the stochastic Galerkin matrix.
Revised:  In this paper, we introduce and analyze a new class of preconditioners for SGFEM . Extending the idea of the mean-based preconditioner, the new preconditioners capture additional significant components of the stochastic Galerkin matrix.
Edit intent: coherence
Original:  In this paper, we introduce and analyze a new class of preconditioners for SGFEM that we call truncation preconditioners . Extending the idea of the mean-based preconditioner, the new preconditioners capture additional significant components of the stochastic Galerkin matrix.
Revised:  In this paper, we introduce and analyze a new class of preconditioners for SGFEM that we call truncation preconditioners . Extending the idea of the mean-based preconditioner, these preconditioners capture additional significant components of the stochastic Galerkin matrix.
Edit intent: clarity
Original:  In particular, we look at the efficiency of the solver (in terms of iteration counts for solving the underlying linear systems) and compare our preconditioners with other existing preconditioners for stochastic Galerkin matrices, such as the mean-based and the Kronecker product ones.
Revised:  In particular, we look at the efficiency of the solver (in terms of iteration counts for solving the underlying linear systems) and compare truncation preconditioners with other existing preconditioners for stochastic Galerkin matrices, such as the mean-based and the Kronecker product ones.
Edit intent: meaning-changed
Original:  We achieve near perfect estimates of qualitative characteristics of the systems , and good approximations of numerical quantities, demonstrating that neural networks can learn advanced theorems and complex computations without built-in mathematical knowledge.
Revised:  We achieve near perfect estimates of qualitative characteristics of the systems , and good approximations of numerical quantities, demonstrating that neural networks can learn to perform complex computations, grounded in advanced theory, from examples, without built-in mathematical knowledge.
Edit intent: clarity
Original:  In all setups tested, our method boosts the average score of base models by a large margin of 4 points, as well as consistently outperforms the state-of-the-art.
Revised:  In all setups tested, our method boosts the average score of base models by a large margin of 4.7 points, as well as consistently outperforms the state-of-the-art.
Edit intent: meaning-changed
Original:  Here, we develop a two-step inference strategy that allows us to train robust generalized linear models of interacting neurons, by explicitly separating the effects of stimulus correlations and noise correlations in each training step.
Revised:  Here, we develop a two-step inference strategy that allows us to train robust generalized linear models of interacting neurons, by explicitly separating the effects of correlations in the stimulus from network interactions in each training step.
Edit intent: clarity
Original: We study the role of multivalent actin-binding proteins in remodeling actomyosin networks , using a numerical model of semiflexible filaments.
Revised: Quantifying the influence of microscopic details on the dynamics of development of the overall structure of a filamentous network is important in a number of biologically relevant contexts, but it is not obvious what order parameters can be used to adequately describe this complex process. In this paper, we investigated the role of multivalent actin-binding proteins in remodeling actomyosin networks , using a numerical model of semiflexible filaments.
Edit intent: meaning-changed
Original:  The rich variety of emergent architectures and morphologies inspired us to develop order parametersbased on network theory, to quantitatively characterize the URLanization in the simulated network. First, actin-binding proteins with a valency greater than two promote filament bundles and large filament clusters to a much greater extent than bivalent multilinkers.
Revised:  We first map the networks into local graph representations and then, using principles from network-theory order parameters, combine properties from these representations to gain insight on the heterogeneous morphologies of actomyosin networks at a global level. We find that ABPs with a valency greater than two promote filament bundles and large filament clusters to a much greater extent than bivalent multilinkers.
Edit intent: clarity
Original:  Our work provides a framework to describe the dendritic structures of actomyosin networks mediated by ABP with the ability of binding multiple filaments, such as Ca2+/Calmodulin-dependent Kinase II (CaMKII). We expect this work to motivate future directions to embrace network theory as a tool to analyze experimentsas well as to establish a quantitative understanding of the role of dendritic spine growth in synaptic plasticity .
Revised:  Our work motivates future studies to embrace network theory as a tool to analyze experimentsas well as to establish a quantitative understanding of the role of dendritic spine growth in synaptic plasticity .
Edit intent: clarity
Original:  We expect this work to motivate future directions to embrace network theory as a tool to analyze experimentsas well as to establish a quantitative understanding of the role of dendritic spine growth in synaptic plasticity .
Revised:  We expect this work to motivate future directions to embrace network theory as a tool to characterize complex morphologies of actomyosin detected by experiments, leading to a quantitative understanding of the role of dendritic spine growth in synaptic plasticity .
Edit intent: meaning-changed
Original:  We expect this work to motivate future directions to embrace network theory as a tool to analyze experimentsas well as to establish a quantitative understanding of the role of dendritic spine growth in synaptic plasticity .
Revised:  We expect this work to motivate future directions to embrace network theory as a tool to analyze experimentsas well as to establish a quantitative understanding of the role of ABPs in manipulating the self-assembly of actin filaments into unique architectures that underlie the structural scaffold of a cell relating to its mobility and shape .
Edit intent: meaning-changed
Original:  Practically, the research outputs interpretable insights for governmental stakeholders to evaluate the impact of information flow on their disaster response processes, so that critical stakeholders can be identified and targeted proactive actions can be taken for enhanced disaster response.
Revised:  Practically, the research provides governmental stakeholders interpretable insights for governmental stakeholders to evaluate the impact of information flow on their disaster response processes, so that critical stakeholders can be identified and targeted proactive actions can be taken for enhanced disaster response.
Edit intent: clarity
Original:  Practically, the research outputs interpretable insights for governmental stakeholders to evaluate the impact of information flow on their disaster response processes, so that critical stakeholders can be identified and targeted proactive actions can be taken for enhanced disaster response.
Revised:  Practically, the research outputs interpretable insights for evaluating the impact of information flow on their disaster response processes, so that critical stakeholders can be identified and targeted proactive actions can be taken for enhanced disaster response.
Edit intent: clarity
Original:  Practically, the research outputs interpretable insights for governmental stakeholders to evaluate the impact of information flow on their disaster response processes, so that critical stakeholders can be identified and targeted proactive actions can be taken for enhanced disaster response.
Revised:  Practically, the research outputs interpretable insights for governmental stakeholders to evaluate the impact of information flow on their disaster response effectiveness so that critical stakeholders can be identified and targeted proactive actions can be taken for enhanced disaster response.
Edit intent: clarity
Original:  Practically, the research outputs interpretable insights for governmental stakeholders to evaluate the impact of information flow on their disaster response processes, so that critical stakeholders can be identified and targeted proactive actions can be taken for enhanced disaster response.
Revised:  Practically, the research outputs interpretable insights for governmental stakeholders to evaluate the impact of information flow on their disaster response processes, so that critical stakeholders can be targeted proactive actions can be taken for enhanced disaster response.
Edit intent: clarity
Original:  Practically, the research outputs interpretable insights for governmental stakeholders to evaluate the impact of information flow on their disaster response processes, so that critical stakeholders can be identified and targeted proactive actions can be taken for enhanced disaster response.
Revised:  Practically, the research outputs interpretable insights for governmental stakeholders to evaluate the impact of information flow on their disaster response processes, so that critical stakeholders can be identified and targeted proactive actions for enhanced disaster response.
Edit intent: coherence
Original:  The modified SRPT algorithm is shown to achieve an competitive ratio of \Theta(\log \alpha +\beta) for minimizing  flow time, where \alpha denotes the ratio of maximum job workload and minimum job workload, \beta represents the ratio between maximum non-preemptive task workload and minimum job workload.
Revised:  The modified SRPT algorithm is shown to achieve a competitive ratio of \Theta(\log \alpha +\beta) for minimizing flow time, where \alpha denotes the ratio of maximum job workload and minimum job workload, \beta represents the ratio between maximum non-preemptive task workload and minimum job workload.
Edit intent: fluency
Original:  The modified SRPT algorithm is shown to achieve an competitive ratio of \Theta(\log \alpha +\beta) for minimizing  flow time, where \alpha denotes the ratio of maximum job workload and minimum job workload, \beta represents the ratio between maximum non-preemptive task workload and minimum job workload.
Revised:  The modified SRPT algorithm is shown to achieve an competitive ratio of \Theta(\log \alpha +\beta) for minimizing the flow time, where \alpha denotes the ratio of maximum job workload and minimum job workload, \beta represents the ratio between maximum non-preemptive task workload and minimum job workload.
Edit intent: fluency
Original:  We further consider the problem under poisson arrival and general workload distribution,  M-SRPT is proved to be asymptotic optimal when the traffic intensity \rho approaches 1, if the task size is upper bound by the derived upper bound \eta{1-\rho}, if the job size distribution has bounded support, which generalizes the result in }[ .
Revised:  We further consider the problem under Poisson arrival and general workload distribution, M-SRPT is proved to be asymptotic optimal when the traffic intensity \rho approaches 1, if the task size is upper bound by the derived upper bound \eta{1-\rho}, if the job size distribution has bounded support, which generalizes the result in }[ .
Edit intent: fluency
Original:  We further consider the problem under poisson arrival and general workload distribution,  M-SRPT is proved to be asymptotic optimal when the traffic intensity \rho approaches 1, if the task size is upper bound by the derived upper bound \eta{1-\rho}, if the job size distribution has bounded support, which generalizes the result in }[ .
Revised:  We further consider the problem under poisson arrival and general workload distribution, and show that M-SRPT is proved to be asymptotic optimal when the traffic intensity \rho approaches 1, if the task size is upper bound by the derived upper bound \eta{1-\rho}, if the job size distribution has bounded support, which generalizes the result in }[ .
Edit intent: coherence
Original:  We further consider the problem under poisson arrival and general workload distribution,  M-SRPT is proved to be asymptotic optimal when the traffic intensity \rho approaches 1, if the task size is upper bound by the derived upper bound \eta{1-\rho}, if the job size distribution has bounded support, which generalizes the result in }[ .
Revised:  We further consider the problem under poisson arrival and general workload distribution, M-SRPT is asymptotic optimal when the traffic intensity \rho approaches 1, if the task size is upper bound by the derived upper bound \eta{1-\rho}, if the job size distribution has bounded support, which generalizes the result in }[ .
Edit intent: coherence
Original:  We further consider the problem under poisson arrival and general workload distribution,  M-SRPT is proved to be asymptotic optimal when the traffic intensity \rho approaches 1, if the task size is upper bound by the derived upper bound \eta{1-\rho}, if the job size distribution has bounded support, which generalizes the result in }[ .
Revised:  We further consider the problem under poisson arrival and general workload distribution, M-SRPT is proved to be asymptotic optimal when the traffic intensity \rho approaches 1, if the job size distribution has bounded support. In addition, we also present a characterization of the heavy traffic behavior of work-conserving algorithms in single-task job scheduling, and prove that the average flow time in GI/GI/1 system scales with \frac{1{1-\rho}, if the job size distribution has bounded support, which generalizes the result in }[ .
Edit intent: meaning-changed
Original:  We further consider the problem under poisson arrival and general workload distribution,  M-SRPT is proved to be asymptotic optimal when the traffic intensity \rho approaches 1, if the task size is upper bound by the derived upper bound \eta{1-\rho}, if the job size distribution has bounded support, which generalizes the result in }[ .
Revised:  We further consider the problem under poisson arrival and general workload distribution, M-SRPT is proved to be asymptotic optimal when the traffic intensity \rho approaches 1, if the task size is upper bound by the derived upper bound \eta{1-\rho}, if the job size distribution has bounded support, which generalizes the result in }[Lin, Wierman and Zwart, 2011 .
Edit intent: meaning-changed
Original:  We consider and analyze  M-SRPT  , a simple modification of the shortest remaining processing time algorithm , which always schedules jobs according to SRPT whenever possible, while processes tasks in an arbitrary order.
Revised:  We consider and analyze Modified Shortest Remaining Processing Time ( M-SRPT , a simple modification of the shortest remaining processing time algorithm , which always schedules jobs according to SRPT whenever possible, while processes tasks in an arbitrary order.
Edit intent: meaning-changed
Original:  We consider and analyze  M-SRPT  , a simple modification of the shortest remaining processing time algorithm , which always schedules jobs according to SRPT whenever possible, while processes tasks in an arbitrary order.
Revised:  We consider and analyze M-SRPT ) scheduling algorithm , a simple modification of the shortest remaining processing time algorithm , which always schedules jobs according to SRPT whenever possible, while processes tasks in an arbitrary order.
Edit intent: meaning-changed
Original:  The modified SRPT algorithm is shown to achieve a competitive ratio of \Theta(\log \alpha +\beta) for minimizing the flow time, where \alpha denotes the ratio of maximum job workload and minimum job workload, \beta represents the ratio between maximum non-preemptive task workload and minimum job workload.
Revised:  The M-SRPT algorithm is proved to achieve a competitive ratio of \Theta(\log \alpha +\beta) for minimizing the flow time, where \alpha denotes the ratio of maximum job workload and minimum job workload, \beta represents the ratio between maximum non-preemptive task workload and minimum job workload.
Edit intent: clarity
Original:  The modified SRPT algorithm is shown to achieve a competitive ratio of \Theta(\log \alpha +\beta) for minimizing the flow time, where \alpha denotes the ratio of maximum job workload and minimum job workload, \beta represents the ratio between maximum non-preemptive task workload and minimum job workload.
Revised:  The modified SRPT algorithm is shown to achieve a competitive ratio of \Theta(\log \alpha +\beta) for minimizing response time, where \alpha denotes the ratio of maximum job workload and minimum job workload, \beta represents the ratio between maximum non-preemptive task workload and minimum job workload.
Edit intent: clarity
Original:  The modified SRPT algorithm is shown to achieve a competitive ratio of \Theta(\log \alpha +\beta) for minimizing the flow time, where \alpha denotes the ratio of maximum job workload and minimum job workload, \beta represents the ratio between maximum non-preemptive task workload and minimum job workload.
Revised:  The modified SRPT algorithm is shown to achieve a competitive ratio of \Theta(\log \alpha +\beta) for minimizing the flow time, where \alpha denotes the ratio between maximum job workload and minimum job workload, \beta represents the ratio between maximum non-preemptive task workload and minimum job workload.
Edit intent: fluency
Original:  We further consider the problem under Poisson arrival and general workload distribution ,\ie and show that M-SRPT is asymptotic optimal when the traffic intensity \rho approaches 1, if the job size distribution has bounded support.
Revised:  We further consider the problem under Poisson arrival and general workload distribution (\ie and show that M-SRPT is asymptotic optimal when the traffic intensity \rho approaches 1, if the job size distribution has bounded support.
Edit intent: fluency