wanyu/IteraTeR_full_sent huggingface dataset:
Original:  the special cases of this model correspond to specific examples of different types of cell appendages . Exploiting the techniques of level crossings developed for random excursions of stochastic process, we have derived analytical expressions of passage times for hitting various thresholds, sojourn times of random excursions beyond the threshold and the extreme lengths attained during the lifetime of these protrusions. As a concrete example, we apply our general formulae to flagella(also called cilia) of eukaryotic cells thereby getting estimates of the typical length scales and time scales associated with the various aspects of random fluctuations. Since most of the earlier works investigated only the mean length of cell protrusions, our study of the fluctuations opens a new horizon in the field of subcellular size control .
Revised:  the special cases of this model correspond to specific examples of different types of cell appendages . Exploiting the techniques of level crossings developed for random excursions of stochastic process, we have derived analytical expressions of passage times for hitting various thresholds, sojourn times of random excursions beyond the threshold and the extreme lengths attained during the lifetime of these model flagella. We identify different parameter regimes of this model flagellum that mimic those of the wildtype and mutants of a well known flagellated cell. By analysing our model in these different parameter regimes, we demonstrate how mutation can alter the level-crossing statistics even when the steady state length remains unaffected by the same mutation. Comparison of the theoretically predicted level crossing statistics, in addition to mean and variance of the length, in the steady state with the corresponding experimental data can be used in near future as stringent tests for the validity of the models of flagellar length control. The experimental data required for this purpose, though never reported till now, can be collected, in principle, using a method developed very recently for flagellar length fluctuations .
Edit intent: clarity
Original:  Only very recently \mbox{%DIFAUXCMD DBLP:journals/corr/abs-1907-11422,DBLP:journals/corr/abs-2002-07152 extended the classical +2 (respectively, +4) spanners for unweighted graphs of size O(n^{3/2}) (resp., O(n^{7/5})) to the weighted setting, where the additive error is +2W (resp., +4W).
Revised:  Only very recently ABSKS20 extended the classical +2 (respectively, +4) spanners for unweighted graphs of size O(n^{3/2}) (resp., O(n^{7/5})) to the weighted setting, where the additive error is +2W (resp., +4W).
Edit intent: meaning-changed
Original:  This means that for every pair u,v, the additive stretch is at most +2W_{u,v}, where W_{u,v} is the maximal edge weight on the shortest u-v path (weights are normalized so that the minimum edge weight is 1) .
Revised:  This means that for every pair u,v, the additive stretch is at most +2W_{u,v}, where W_{u,v} is the maximal edge weight on the shortest u-v path .
Edit intent: coherence
Original:  In addition, \mbox{%DIFAUXCMD DBLP:journals/corr/abs-2002-07152  algorithm yielding a +8W_{max} spanner of size O(n^{4/3}), here W_{max} is the maximum edge weight in the entire graph.
Revised:  In addition, ABSKS20 algorithm yielding a +8W_{max} spanner of size O(n^{4/3}), here W_{max} is the maximum edge weight in the entire graph.
Edit intent: meaning-changed
Original:  In addition, \mbox{%DIFAUXCMD DBLP:journals/corr/abs-2002-07152  algorithm yielding a +8W_{max} spanner of size O(n^{4/3}), here W_{max} is the maximum edge weight in the entire graph.
Revised:  In addition, \mbox{%DIFAUXCMD DBLP:journals/corr/abs-2002-07152 showed an algorithm yielding a +8W_{max} spanner of size O(n^{4/3}), here W_{max} is the maximum edge weight in the entire graph.
Edit intent: clarity
Original:  In this work we improve the latter result by devising a simple deterministic algorithm for a +(6+\varepsilon)W spanner for weighted graphs with size O(n^{4/3}) (for any constant \varepsilon>0), thus nearly matching the classical +6 spanner of size O(n^{4/3}) for unweighted graphs.  ]  We also show a simple randomized algorithm for a +4W emulator of size O(n^{4/3})  \cdot W) spanner, and we also obtain a tradeoff between size and stretch.
Revised:  In this work we improve the latter result by devising a simple deterministic algorithm for a +(6+\varepsilon)W spanner for weighted graphs with size O(n^{4/3}) (for any constant \varepsilon>0), thus nearly matching the classical +6 spanner of size O(n^{4/3}) for unweighted graphs. Furthermore, we show a +(2+\varepsilon)W subsetwise spanner of size O(n\cdot\sqrt{|S| ] We also show a simple randomized algorithm for a +4W emulator of size O(n^{4/3}) \cdot W) spanner, and we also obtain a tradeoff between size and stretch.
Edit intent: meaning-changed
Original:  In this work we improve the latter result by devising a simple deterministic algorithm for a +(6+\varepsilon)W spanner for weighted graphs with size O(n^{4/3}) (for any constant \varepsilon>0), thus nearly matching the classical +6 spanner of size O(n^{4/3}) for unweighted graphs.  ]  We also show a simple randomized algorithm for a +4W emulator of size O(n^{4/3})  \cdot W) spanner, and we also obtain a tradeoff between size and stretch.
Revised:  In this work we improve the latter result by devising a simple deterministic algorithm for a +(6+\varepsilon)W spanner for weighted graphs with size O(n^{4/3}) (for any constant \varepsilon>0), thus nearly matching the classical +6 spanner of size O(n^{4/3}) for unweighted graphs. ABSKS20] We also show a simple randomized algorithm for a +4W emulator of size O(n^{4/3}) \cdot W) spanner, and we also obtain a tradeoff between size and stretch.
Edit intent: meaning-changed
Original:  In this work we improve the latter result by devising a simple deterministic algorithm for a +(6+\varepsilon)W spanner for weighted graphs with size O(n^{4/3}) (for any constant \varepsilon>0), thus nearly matching the classical +6 spanner of size O(n^{4/3}) for unweighted graphs.  ]  We also show a simple randomized algorithm for a +4W emulator of size O(n^{4/3})  \cdot W) spanner, and we also obtain a tradeoff between size and stretch.
Revised:  In this work we improve the latter result by devising a simple deterministic algorithm for a +(6+\varepsilon)W spanner for weighted graphs with size O(n^{4/3}) (for any constant \varepsilon>0), thus nearly matching the classical +6 spanner of size O(n^{4/3}) for unweighted graphs. ] (that had the same size). We also show a simple randomized algorithm for a +4W emulator of size O(n^{4/3}) \cdot W) spanner, and we also obtain a tradeoff between size and stretch.
Edit intent: meaning-changed
Original:  In this work we improve the latter result by devising a simple deterministic algorithm for a +(6+\varepsilon)W spanner for weighted graphs with size O(n^{4/3}) (for any constant \varepsilon>0), thus nearly matching the classical +6 spanner of size O(n^{4/3}) for unweighted graphs.  ]  We also show a simple randomized algorithm for a +4W emulator of size O(n^{4/3})  \cdot W) spanner, and we also obtain a tradeoff between size and stretch.
Revised:  In this work we improve the latter result by devising a simple deterministic algorithm for a +(6+\varepsilon)W spanner for weighted graphs with size O(n^{4/3}) (for any constant \varepsilon>0), thus nearly matching the classical +6 spanner of size O(n^{4/3}) for unweighted graphs. ] We also show a simple randomized algorithm for a +4W emulator of size O(n^{4/3}) . In addition, we show that our technique is applicable for very sparse additive spanners, that have linear size. For weighted graphs, we use a variant of our simple deterministic algorithm that yields a linear size + \cdot W) spanner, and we also obtain a tradeoff between size and stretch.
Edit intent: meaning-changed
Original:  In this work we improve the latter result by devising a simple deterministic algorithm for a +(6+\varepsilon)W spanner for weighted graphs with size O(n^{4/3}) (for any constant \varepsilon>0), thus nearly matching the classical +6 spanner of size O(n^{4/3}) for unweighted graphs.  ]  We also show a simple randomized algorithm for a +4W emulator of size O(n^{4/3})  \cdot W) spanner, and we also obtain a tradeoff between size and stretch.
Revised:  In this work we improve the latter result by devising a simple deterministic algorithm for a +(6+\varepsilon)W spanner for weighted graphs with size O(n^{4/3}) (for any constant \varepsilon>0), thus nearly matching the classical +6 spanner of size O(n^{4/3}) for unweighted graphs. ] We also show a simple randomized algorithm for a +4W emulator of size O(n^{4/3}) (\sqrt{n\cdot W) spanner, and we also obtain a tradeoff between size and stretch.
Edit intent: meaning-changed
Original: With rapid technology advancements and increased usage of handheld devices such as smartphones, tablets, and smartwatches, people's reliance on these devices has grown beyond their utility as a means to communicate. Today, these devices are also helping people make healthier lifestyle choices regarding nutrition through different dietary applications, thereby saving expensive visits to doctors and nutritionists. These applications provide awareness about nutritional habits and keep track of a person's physical activity for planning their dietary needs .
Revised: Obesity is known to lower the quality of life substantially. It is often associated with increased chances of non-communicable diseases such as diabetes, cardiovascular problems, different types of cancers, etc. Evidence suggests that diet-related mobile applications play a vital role in assisting an individual in making healthier choices and keeping track of food intake .
Edit intent: coherence
Original:  However, the selection or development of the dietary application for catering individual needs, and implementing successful interventions toward behavioral change is a challenging process .
Revised:  However, due to an abundance of similar applications, it becomes pertinent to evaluate each of them in terms of functionality, usability, and possible design issues to truly determine state-of-the-art solutions for the future. Since these applications involve implementing multiple user requirements and recommendations from different dietitians, the evaluation becomes quite complex .
Edit intent: coherence
Original:  Therefore, the following research exercise aims to review existing nutritional applications at length to highlight key features and problems that enhance or undermine an application's usability and build the case for a generalized implementation that can cater to most people's needs.
Revised:  Therefore, this study aims to review existing nutritional applications at length to highlight key features and problems that enhance or undermine an application's usability and build the case for a generalized implementation that can cater to most people's needs.
Edit intent: clarity
Original:  Therefore, the following research exercise aims to review existing nutritional applications at length to highlight key features and problems that enhance or undermine an application's usability and build the case for a generalized implementation that can cater to most people's needs.
Revised:  Therefore, the following research exercise aims to review existing dietary applications at length to highlight key features and problems that enhance or undermine an application's usability and build the case for a generalized implementation that can cater to most people's needs.
Edit intent: fluency
Original:  Therefore, the following research exercise aims to review existing nutritional applications at length to highlight key features and problems that enhance or undermine an application's usability and build the case for a generalized implementation that can cater to most people's needs. The findingsfrom this analysis will help to inform the future development of more effective mobile apps .
Revised:  Therefore, the following research exercise aims to review existing nutritional applications at length to highlight key features and problems that enhance or undermine an application's usability . For this purpose, we have examined the published literature from various scientific databases of the CINAHL, Science Direct, and PUBMED. Out of our findings, fifty-six primary studies met our inclusion criteria after filtering out titles, abstracts, and full text. A total of 35 apps are analyzed from the selected studies. Our detailed analysis concluded the comprehensiveness of freely available mHealth applications from users and dietitians' frames of reference .
Edit intent: coherence
Original:  For this purpose, we have examined the published literature from various scientific databases of the CINAHL, Science Direct, and PUBMED. Out of our findings, fifty-six primary studies met our inclusion criteria after filtering out titles, abstracts, and full text.
Revised:  For this purpose, we have examined the published literature from various scientific databases of the PUBMED, CINAHL (January 2010-December 2019) and Science Direct (2010-2019). We followed PRISMA guidelines, and out of our findings, fifty-six primary studies met our inclusion criteria after filtering out titles, abstracts, and full text.
Edit intent: meaning-changed
Original:  A total of 35 apps are analyzed from the selected studies . Our detailed analysis concluded the comprehensiveness of freely available mHealth applications from users and dietitians' frames of reference.
Revised:  A total of 35 apps are analyzed from the selected studies and extracted the data of each of the identified apps.Following our detailed analysis on the comprehensiveness of freely available mHealth applications from users and dietitians' frames of reference.
Edit intent: meaning-changed
Original:  We implement free energy minimization in a more physically principled manner by effectively casting it to Bayesian control dynamics. Specifically, we build a continuous-state formulation that prescribes the brain's computation for actively inferring the causes of sensory inputs, based on the principle of least action.
Revised:  We implement free energy minimization in a more physically principled formalism by implementing free energy minimization based on the principle of least action.
Edit intent: clarity
Original:  For this, we consider that the motor signal arises from the residual errors in the cognitive expectation about the nonstationary sensory inputs at the proprioceptive level.
Revised:  We build a Bayesian mechanics (BM) by casting the formulation reported in the earlier publication (Kim 2018) to considering active inference beyond passive perception. The BM is a neural implementation of variational Bayes under the FEP in continuous time. The resulting BM is provided as an effective Hamilton's equation of motion and subject to the control signal arising from the brain's prediction errors at the proprioceptive level.
Edit intent: meaning-changed
Original:  Consequently, we derive the effective Hamilton equations that determine the optimal trajectories in the perceptual phase space, which minimize the sensory uncertainty. We also demonstrate their utility using a simple model with biological relevance to sensorimotor regulation processes such as motor reflex arcs or saccadic eye movement .
Revised:  To demonstrate the utility of our approach, we adopt a simple agent-based model and present a concrete numerical illustration of the brain performing recognition dynamics by integrating BM in neural phase space. Furthermore, we recapitulate the major theoretical architectures in the FEP by comparing our approach with the common state-space formulations .
Edit intent: meaning-changed
Original:  This value function is learned through off-policy reinforcement learning, using trajectory optimization as its behavior policy. Furthermore, we introduce "actor-guided" trajectory optimization to mitigate the actor-divergence issue in the proposed method. We benchmark our methods on continuous control tasks and demonstrate that it offers a significant improvement over the underlying model-based and model-free algorithms  .
Revised:  This value function is learned through off-policy reinforcement learning, offline reinforcement learning, and safe learning. We show that this method significantly improves the underlying model-based and model-free algorithms .
Edit intent: meaning-changed
Original: We propose Learning Off-Policy with Online Planning (LOOP) , an efficient reinforcement learning framework, combining the benefits of model-based local trajectory optimization and off-policy algorithms. The agent learns a dynamics model and then uses trajectory optimization with the learned model to select actions.
Revised: We propose Learning Off-Policy with Online Planning (LOOP) , an efficient reinforcement learning framework, combining the benefits of model-based local trajectory optimization and risk-sensitive domains requires performant and flexible deployment policies that can readily incorporate constraints during deployment. One such class of policies are the semi-parametric H-step lookahead policies, which select actions using trajectory optimization over a dynamics model and then uses trajectory optimization with the learned model to select actions.
Edit intent: meaning-changed
Original:  The agent learns a dynamics model and then uses trajectory optimization with the learned model to select actions. To sidestep the myopic effect of fixed-horizon trajectory optimization, a value function learned through an off-policy algorithm is attached to the end of the planning horizon.
Revised:  The agent learns a dynamics model for a fixed horizon with a terminal value function. In this work, we investigate a novel instantiation of H-step lookahead with a learned model and a terminal value function learned through an off-policy algorithm is attached to the end of the planning horizon.
Edit intent: meaning-changed
Original:  To sidestep the myopic effect of fixed-horizon trajectory optimization, a value function learned through an off-policy algorithm is attached to the end of the planning horizon. We investigate various instantiations of this framework and demonstrate its benefit in three settings: online reinforcement learning, offline reinforcement learning, and safe learning. We show that this method significantly improves the underlying model-based and model-free algorithms and achieves state-of-the-art performancein a variety of settings.
Revised:  To sidestep the myopic effect of fixed-horizon trajectory optimization, a value function learned through an off-policy algorithm , named Learning Off-Policy with Online Planning (LOOP). We provide a theoretical analysis of this method, suggesting a tradeoff between model errors and value function errors and empirically demonstrate this tradeoff to be beneficial in deep reinforcement learning. Furthermore, we identify the "Actor Divergence" issue in this framework and propose Actor Regularized Control (ARC), a modified trajectory optimization procedure. We evaluate our method on a set of robotic tasks for Offline and Online RL and demonstrate improved performance. We also show the flexibility of LOOP to incorporate safety constraints during deployment with a set of navigation environments. We demonstrate that LOOP is a desirable framework for robotics applications based on its strong performance in various important RL settings.
Edit intent: meaning-changed
Original:  We show that our method is faster and less conservative than other approaches , present results for approximate reachability analysis of neural networks and robust trajectory optimization of high-dimensional uncertain nonlinear systems, and discuss future applications.
Revised:  We demonstrate that our method is faster and less conservative than other approaches , present results for approximate reachability analysis of neural networks and robust trajectory optimization of high-dimensional uncertain nonlinear systems, and discuss future applications.
Edit intent: fluency
Original:  We show that our method is faster and less conservative than other approaches , present results for approximate reachability analysis of neural networks and robust trajectory optimization of high-dimensional uncertain nonlinear systems, and discuss future applications.
Revised:  We show that our method is faster and less conservative than prior work , present results for approximate reachability analysis of neural networks and robust trajectory optimization of high-dimensional uncertain nonlinear systems, and discuss future applications.
Edit intent: clarity
Original: In this paper, we study a portfolio selection problem in which an agent trades a risk-free asset and multiple risky assets with deterministic mean return rates and volatility and wants to maximize the \alpha-quantile of her wealth at some terminal time . Because of the time inconsistency caused by quantiles, we consider intra-personal equilibrium strategies.
Revised: Although maximizing median and quantiles is intuitively appealing and has an axiomatic foundation, it is difficult to study the optimal portfolio strategy due to the discontinuity and time inconsistency in the objective function. We use the intra-personal equilibrium strategies.
Edit intent: meaning-changed
Original:  Because of the time inconsistency caused by quantiles, we consider intra-personal equilibrium strategies. We find that among the class of time-varying, affine portfolio strategies, the intra-personal equilibrium does not exist when \alpha>1/2, leads to zero investment in the risky assets when \alpha<1/2, and is a portfolio insurance strategy when \alpha=1/2.
Revised:  Because of the time inconsistency caused by quantiles, we consider intra-personal equilibrium approach to study the problem. Interestingly, we find that the only viable outcome is from the median maximization, because for other quantiles either the equilibrium does not exist when \alpha>1/2, leads to zero investment in the risky assets when \alpha<1/2, and is a portfolio insurance strategy when \alpha=1/2.
Edit intent: meaning-changed
Original:  We find that among the class of time-varying, affine portfolio strategies, the intra-personal equilibrium does not exist when \alpha>1/2, leads to zero investment in the risky assets when \alpha<1/2, and is a portfolio insurance strategy when \alpha=1/2.
Revised:  We find that among the class of time-varying, affine portfolio strategies, the intra-personal equilibrium does not exist or there is no investment in the risky assets when \alpha<1/2, and is a portfolio insurance strategy when \alpha=1/2.
Edit intent: clarity
Original:  We find that among the class of time-varying, affine portfolio strategies, the intra-personal equilibrium does not exist when \alpha>1/2, leads to zero investment in the risky assets when \alpha<1/2, and is a portfolio insurance strategy when \alpha=1/2. We then compare the intra-personal equilibrium strategy in the case of \alpha=1/2, namely under median maximization , to some other strategies and apply it to explain why more wealthy people invest more precentage of wealth in risky assets .
Revised:  We find that among the class of time-varying, affine portfolio strategies, the intra-personal equilibrium does not exist when \alpha>1/2, leads to zero investment in the risky assets . The median maximization strategy gives a simple explanation to why wealthier people invest more precentage of wealth in risky assets .
Edit intent: coherence
Original:  We then compare the intra-personal equilibrium strategy in the case of \alpha=1/2, namely under median maximization , to some other strategies and apply it to explain why more wealthy people invest more precentage of wealth in risky assets .
Revised:  We then compare the intra-personal equilibrium strategy in the case of \alpha=1/2, namely under median maximization , to some other strategies and apply it to explain why more wealthy people invest more percentage of their wealth in risky assets .
Edit intent: clarity