wanyu/IteraTeR_full_sent huggingface dataset:
Original: We present an algorithm to correct molecular dynamics non-equilibrium simulationsbased on the knowledge of time-dependent experimental data. This is inspired by a similar scheme used for equilibrium simulations , and is based on the principle of maximum caliber that guarantees that as little subjective information as possible is added to a model, besides the available experimental data.
Revised: Inferential methods can be used to integrate experimental informations and molecular simulations , and is based on the principle of maximum caliber that guarantees that as little subjective information as possible is added to a model, besides the available experimental data.
Edit intent: clarity
Original:  This is inspired by a similar scheme used for equilibrium simulations , and is based on the principle of maximum caliber that guarantees that as little subjective information as possible is added to a model, besides the available experimental data.
Revised:  This is inspired by a similar scheme used for equilibrium simulations . The maximum entropy principle provides a framework for using equilibrium experimental data and it has been shown that replica-averaged simulations, restrained using a static potential, are a practical and powerful implementation of such principle. Here we show that replica-averaged simulations restrained using a time-dependent potential are equivalent to the principle of maximum caliber that guarantees that as little subjective information as possible is added to a model, besides the available experimental data.
Edit intent: meaning-changed
Original:  This is inspired by a similar scheme used for equilibrium simulations , and is based on the principle of maximum caliber that guarantees that as little subjective information as possible is added to a model, besides the available experimental data. We also show that the same algorithm, in the case of simplified models, can be used to speed up the computational time needed by a simulation by one to two orders of magnitude .
Revised:  This is inspired by a similar scheme used for equilibrium simulations , and is based on the principle of maximum caliber , the dynamic version of the principle of maximum entropy, and thus may allow to integrate time-resolved data in molecular dynamics simulations. We provide an analytical proof of the equivalence as well as a computational validation making use of simple models and synthetic data. Some limitations and possible solutions are also discussed .
Edit intent: meaning-changed
Original:  If this condition is not satisfied, we show that queue lengths are bounded by j^\star+1, where j^\star\in\mathbb{N\left\lceil {\log (\lambda d +1)} }\right\rceil .
Revised:  If this condition is not satisfied, we show that queue lengths are tightly bounded by\left\lceil {\log (\lambda d +1)} }\right\rceil .
Edit intent: meaning-changed
Original:  If this condition is not satisfied, we show that queue lengths are bounded by j^\star+1, where j^\star\in\mathbb{N\left\lceil {\log (\lambda d +1)} }\right\rceil .
Revised:  If this condition is not satisfied, we show that queue lengths are bounded by j^\star+1, where j^\star\in\mathbb{N\left\lceil - \frac{ \log (1-\lambda){\log (\lambda d +1)} }\right\rceil .
Edit intent: meaning-changed
Original:  This is in contrast with the classic version of the power-of-d-choice algorithm, where queue lengths are unbounded. Our upper bound on the size of the most loaded server, j^*+1, is tight and increases slowly when \lambda approaches its critical value from below. For instance, when \lambda= 0.995 and d=2 (respectively, d=3), we find that no server will contain more than just 5 (3) jobs\ge  in equilibrium.
Revised:  This is in contrast with the classic version of the power-of-d-choice algorithm, where at the fluid scale a strictly positive proportion of servers containing i jobs exists for all i\ge in equilibrium.
Edit intent: clarity
Original:  For instance, when \lambda= 0.995 and d=2 (respectively, d=3), we find that no server will contain more than just 5 (3) jobs\ge  in equilibrium.
Revised:  For instance, when \lambda= 0.995 and d=2 (respectively, d=3), we find that no server will contain more than just 5 (3) jobs\ge 0, in equilibrium.
Edit intent: meaning-changed
Original:  We give a polynomial time algorithm that constructs such a morph as a composition of a linear number of steps where each step either moves vertices along horizontal lines or moves vertices along vertical lines  .
Revised:  We give a polynomial time algorithm that constructs such a morph as a composition of a linear number of steps where each step either moves vertices along horizontal lines or moves vertices along vertical lines . Moreover, we show that a linear number of steps is worst-case optimal. To obtain our result, we use a well-known technique by Hong and Nagamochi for finding redrawings with convex faces while preserving y-coordinates. Using a variant of Tutte's graph drawing algorithm, we obtain a new proof of Hong and Nagamochi's result which comes with a better running time. This is of independent interest, as Hong and Nagamochi's technique serves as a building block in existing morphing algorithms .
Edit intent: meaning-changed
Original:  We focus on the setting where the service rates \mu are unknown , and are interested in the holding-cost regret -- the difference in the expected holding-costs between that induced by a learning-based rule (that learns \mu) and that from the c \mu rule (which has knowledge of the service rates) over any fixed time horizon.
Revised:  We focus on the problem where the service rates \mu are unknown , and are interested in the holding-cost regret -- the difference in the expected holding-costs between that induced by a learning-based rule (that learns \mu) and that from the c \mu rule (which has knowledge of the service rates) over any fixed time horizon.
Edit intent: clarity
Original:  We focus on the setting where the service rates \mu are unknown , and are interested in the holding-cost regret -- the difference in the expected holding-costs between that induced by a learning-based rule (that learns \mu) and that from the c \mu rule (which has knowledge of the service rates) over any fixed time horizon.
Revised:  We focus on the setting where the service rates \mu are unknown with the holding-cost regret -- the difference in the expected holding-costs between that induced by a learning-based rule (that learns \mu) and that from the c \mu rule (which has knowledge of the service rates) over any fixed time horizon.
Edit intent: coherence
Original:  We focus on the setting where the service rates \mu are unknown , and are interested in the holding-cost regret -- the difference in the expected holding-costs between that induced by a learning-based rule (that learns \mu) and that from the c \mu rule (which has knowledge of the service rates) over any fixed time horizon.
Revised:  We focus on the setting where the service rates \mu are unknown , and are interested in the holding-cost regret (regret against the c \mu rule (which has knowledge of the service rates) over any fixed time horizon.
Edit intent: clarity
Original:  We focus on the setting where the service rates \mu are unknown , and are interested in the holding-cost regret -- the difference in the expected holding-costs between that induced by a learning-based rule (that learns \mu) and that from the c \mu rule (which has knowledge of the service rates) over any fixed time horizon. We first show that empirically learning the service rates and then scheduling using these learned values results in a regret of holding-cost that does not depend on the time horizon .
Revised:  We focus on the setting where the service rates \mu are unknown , and are interested in the holding-cost regret -- the difference in the expected holding-costs between that induced by a learning-based rule (that learns \mu) and that from the c \mu rule with known \mu) as our objective. We show that the greedy algorithm that uses empirically learned service rates results in a regret of holding-cost that does not depend on the time horizon .
Edit intent: clarity
Original:  We first show that empirically learning the service rates and then scheduling using these learned values results in a regret of holding-cost that does not depend on the time horizon . The key insight that allows such a constant regret bound is that a work-conserving scheduling policy in this setting allows explore-free learning, where no penalty is incurred for exploring and learning server rates.
Revised:  We first show that empirically learning the service rates and then scheduling using these learned values results in a regret of holding-cost that does not depend on the time horizon ). This free exploration can be explained in the single server setting by the fact that any work-conserving scheduling policy in this setting allows explore-free learning, where no penalty is incurred for exploring and learning server rates.
Edit intent: meaning-changed
Original:  The key insight that allows such a constant regret bound is that a work-conserving scheduling policy in this setting allows explore-free learning, where no penalty is incurred for exploring and learning server rates. We next consider the multi-server setting. We show that in general, the c \mu rule is not stabilizing (i.e. there are stabilizable arrival and service rate parameters for which the multi-server c \mu rule results in unstable queues ) .
Revised:  The key insight that allows such a constant regret bound is that a work-conserving policy obtains the same number of samples in a busy cycle. In the parallel server setting, we show that the c \mu rule is not stabilizing (i.e. there are stabilizable arrival and service rate parameters for which the multi-server c \mu rule results in unstable queues ) .
Edit intent: clarity
Original:  We show that in general, the c \mu rule is not stabilizing (i.e. there are stabilizable arrival and service rate parameters for which the multi-server c \mu rule results in unstable queues ) .
Revised:  We show that in general, the c \mu rule is not stabilizing (i.e. there are stabilizable arrival and service rate parameters for which the multi-server c \mu rule results in unstable queues , even for arrival rates within the capacity region .
Edit intent: meaning-changed
Original:  Using these results, we show that learning-based variants of the c\mu rule again result in a constant regret (i. e. does not depend on the time horizon). This result hinges on (i) the busy period concentrations of the multi-server c \mu rule, and that (ii) our learning-based rule is designed to dynamically explore server rates, but in such a manner that it eventually satisfies an explore-free condition .
Revised:  Using these results, we propose an almost greedy algorithm that explores only when the number of samples falls below a threshold. We show that this algorithm delivers constant holding-cost regret because a free exploration condition is eventually satisfied .
Edit intent: meaning-changed
Original:  This limitation represents a major drawback also for state-of-the-art deep and shallow neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time.
Revised:  This limitation represents a major drawback for state-of-the-art deep and shallow neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time.
Edit intent: clarity
Original:  This limitation represents a major drawback also for state-of-the-art deep and shallow neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time.
Revised:  This limitation represents a major drawback also for state-of-the-art deep neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time.
Edit intent: clarity
Original:  This limitation represents a major drawback also for state-of-the-art deep and shallow neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which the number of tasks is not known a priori and the information becomes incrementally available over time.
Revised:  This limitation represents a major drawback also for state-of-the-art deep and shallow neural network models that typically learn representations from stationary batches of training data, thus without accounting for situations in which information becomes incrementally available over time.
Edit intent: clarity
Original:  We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as neurosynaptic plasticity, multi-task transfer learning, intrinsically motivated exploration, and crossmodal learning .
Revised:  We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as structural plasticity, memory replay, curriculum and transfer learning, intrinsically motivated exploration, and crossmodal learning .
Edit intent: meaning-changed
Original:  We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as neurosynaptic plasticity, multi-task transfer learning, intrinsically motivated exploration, and crossmodal learning .
Revised:  We discuss well-established and emerging research motivated by lifelong learning factors in biological systems such as neurosynaptic plasticity, multi-task transfer learning, intrinsic motivation, and multisensory integration .
Edit intent: clarity
Original:  This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to the long-term memory consolidation and retrieval without catastrophic URLetting . Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information.
Revised:  This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to long-term memory consolidation and retrieval without catastrophic URLetting . Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information.
Edit intent: fluency
Original:  This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to the long-term memory consolidation and retrieval without catastrophic URLetting . Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information.
Revised:  This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that together contribute to the development and specialization of our sensorimotor skills as well as to the long-term memory consolidation and retrieval . Consequently, lifelong learning capabilities are crucial for autonomous agents interacting in the real world and processing continuous streams of information.
Edit intent: coherence
Original: In this paper we propose for the first time a unified framework in continuous time suitable for modeling both life and non-life insurance market, with nontrivial dependence with the financial market. We introduce a direct modeling approach, which generalizes the reduced-form framework for credit risk and life insurance.
Revised: In this paper we propose a general framework for modeling an insurance claims' information flow in continuous time, by generalizing the reduced-form framework for credit risk and life insurance.
Edit intent: clarity
Original:  Here, we create the first adversarial examples designed to fool humans, by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by modifying models to more closely match the initial processing of the human visual system.
Revised:  Here, we address this question by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by modifying models to more closely match the initial processing of the human visual system.
Edit intent: clarity
Original:  Here, we create the first adversarial examples designed to fool humans, by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by modifying models to more closely match the initial processing of the human visual system.
Revised:  Here, we create the first adversarial examples designed to fool humans, by leveraging recent techniques that transfer adversarial examples from computer vision models with known parameters and architecture to other models with unknown parameters and architecture, and by matching the initial processing of the human visual system.
Edit intent: clarity
Original:  Examples of such functionals include average derivatives , policy effects , average treatment effects, and many others. The construction relies on building Neyman-orthogonal equations that are approximately invariant to  perturbations of the nuisance parameters , including the Riesz representer for the linear functionals .
Revised:  Examples of such functionals include average derivatives , policy effects , average treatment effects, and average derivatives, conditional on a covariate subvector fixed at a point. Our construction relies on building Neyman-orthogonal equations that are approximately invariant to perturbations of the nuisance parameters , including the Riesz representer for the linear functionals .
Edit intent: meaning-changed
Original:  The construction relies on building Neyman-orthogonal equations that are approximately invariant to  perturbations of the nuisance parameters , including the Riesz representer for the linear functionals .
Revised:  The construction relies on building Neyman orthogonal equations for the target parameter that are approximately invariant to perturbations of the nuisance parameters , including the Riesz representer for the linear functionals .
Edit intent: meaning-changed
Original:  The construction relies on building Neyman-orthogonal equations that are approximately invariant to  perturbations of the nuisance parameters , including the Riesz representer for the linear functionals .
Revised:  The construction relies on building Neyman-orthogonal equations that are approximately invariant to small perturbations of the nuisance parameters , including the Riesz representer for the linear functionals .
Edit intent: meaning-changed
Original:  The construction relies on building Neyman-orthogonal equations that are approximately invariant to  perturbations of the nuisance parameters , including the Riesz representer for the linear functionals .
Revised:  The construction relies on building Neyman-orthogonal equations that are approximately invariant to perturbations of the nuisance parameters . To achieve this property we include the linear Riesz representer for the linear functionals .
Edit intent: meaning-changed
Original:  The construction relies on building Neyman-orthogonal equations that are approximately invariant to  perturbations of the nuisance parameters , including the Riesz representer for the linear functionals .
Revised:  The construction relies on building Neyman-orthogonal equations that are approximately invariant to perturbations of the nuisance parameters , including the Riesz representer for the functionals in the equations as the additional nuisance parameter .
Edit intent: meaning-changed
Original:  We use L1-regularized methods to learn approximations to the regression function and the Riesz representer, and construct the estimator for the linear functionals as the solution to the orthogonal estimating equations.
Revised:  We use l1-regularized methods to learn approximations to the regression function and the Riesz representer, and construct the estimator for the linear functionals as the solution to the orthogonal estimating equations.
Edit intent: fluency
Original:  We use L1-regularized methods to learn approximations to the regression function and the Riesz representer, and construct the estimator for the linear functionals as the solution to the orthogonal estimating equations.
Revised:  We use L1-regularized methods to learn approximations to the regression function and the linear representer, in settings where dimension of (possibly overcomplete) dictionary of basis functions P is much larger than N. We then estimate the linear functional by the solution to the orthogonal estimating equations.
Edit intent: meaning-changed
Original:  We use L1-regularized methods to learn approximations to the regression function and the Riesz representer, and construct the estimator for the linear functionals as the solution to the orthogonal estimating equations. We establish that under weak assumptions the estimator  concentrates in a 1 /root n neighborhood of the target with deviations controlled by the normal laws, and the estimator attains the semi-parametric efficiency bound in many cases.
Revised:  We use L1-regularized methods to learn approximations to the regression function and the Riesz representer, and construct the estimator for the linear functionals as the solution to the empirical analog of the orthogonal equations. Our key result is that under weak assumptions the estimator concentrates in a 1 /root n neighborhood of the target with deviations controlled by the normal laws, and the estimator attains the semi-parametric efficiency bound in many cases.
Edit intent: clarity
Original:  We establish that under weak assumptions the estimator  concentrates in a 1 /root n neighborhood of the target with deviations controlled by the normal laws, and the estimator attains the semi-parametric efficiency bound in many cases.
Revised:  We establish that under weak assumptions the estimator of the functional concentrates in a 1 /root n neighborhood of the target with deviations controlled by the normal laws, and the estimator attains the semi-parametric efficiency bound in many cases.
Edit intent: meaning-changed
Original:  We establish that under weak assumptions the estimator  concentrates in a 1 /root n neighborhood of the target with deviations controlled by the normal laws, and the estimator attains the semi-parametric efficiency bound in many cases.
Revised:  We establish that under weak assumptions the estimator concentrates in a L /root n neighborhood of the target with deviations controlled by the normal laws, and the estimator attains the semi-parametric efficiency bound in many cases.
Edit intent: meaning-changed
Original:  We establish that under weak assumptions the estimator  concentrates in a 1 /root n neighborhood of the target with deviations controlled by the normal laws, and the estimator attains the semi-parametric efficiency bound in many cases.
Revised:  We establish that under weak assumptions the estimator concentrates in a 1 /root (n) neighborhood of the target with deviations controlled by the normal laws, and the estimator attains the semi-parametric efficiency bound in many cases.
Edit intent: meaning-changed