wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this paper, we show how hindsight can be introduced to likelihood-ratio policy gradient methods, generalizing this capacity to an entire class of highly successful algorithms.
Revised:  In this paper, we demonstrate how hindsight can be introduced to likelihood-ratio policy gradient methods, generalizing this capacity to an entire class of highly successful algorithms.
Edit intent: fluency
Original:  In this paper, we show how hindsight can be introduced to likelihood-ratio policy gradient methods, generalizing this capacity to an entire class of highly successful algorithms.
Revised:  In this paper, we show how hindsight can be introduced to policy gradient methods, generalizing this capacity to an entire class of highly successful algorithms.
Edit intent: clarity
Original:  In this paper, we show how hindsight can be introduced to likelihood-ratio policy gradient methods, generalizing this capacity to an entire class of highly successful algorithms.
Revised:  In this paper, we show how hindsight can be introduced to likelihood-ratio policy gradient methods, generalizing this idea to a broad class of successful algorithms.
Edit intent: clarity
Original:  Our preliminary experiments suggest that hindsight may increase the sample efficiencyof policy gradient methods .
Revised:  Our experiments on a diverse selection of sparse-reward environments show that hindsight leads to a remarkable increase in sample efficiency .
Edit intent: clarity
Original:  Furthermore, the Cram\'er-Rao lower bound is derived for the estimated distance which emphasizes, analytically, the existence of an optimal UAV altitude.
Revised:  Furthermore, the Cramer-Rao lower bound is derived for the estimated distance which emphasizes, analytically, the existence of an optimal UAV altitude.
Edit intent: fluency
Original: Approximate computing is a research area where we investigate a wide spectrum of techniques to trade off accuracy of computation for better performance or energy consumption.
Revised: Approximate computing is a research area where we investigate a wide spectrum of techniques to trade off computation accuracy for better performance or energy consumption.
Edit intent: clarity
Original:  We provide in this article a general introduction to approximate computing.
Revised:  In this work, we provide a general introduction to approximate computing.
Edit intent: clarity
Original:  Our taxonomy emphasizes the cost expected for tackling AC across the entire system stack.
Revised:  Our taxonomy emphasizes the expected cost of tackling approximate computing across the entire system stack.
Edit intent: clarity
Original:  We provide selected pointers to the literature based on the proposed taxonomy .
Revised:  We conclude by discussing the unique opportunities as well as challenges of nondeterministic approximate computing .
Edit intent: clarity
Original:  Assuming moderately eccentric binaries that circularize prior to coalescence, we smoothly match the eccentric inspiral with a stand-alone, quasi-circular merger, which is constructed using machine learning algorithms that are trained with quasi-circular numerical relativity waveforms.
Revised:  Assuming eccentric binaries that circularize prior to coalescence, we smoothly match the eccentric inspiral with a stand-alone, quasi-circular merger, which is constructed using machine learning algorithms that are trained with quasi-circular numerical relativity waveforms.
Edit intent: clarity
Original:  In particular, we use ENIGMA to show that the gravitational wave transients GW150914, GW151226, GW170104 and GW170814  can be effectively recovered with spinning, quasi-circular templates if the eccentricity of these events at a gravitational wave frequency of 10Hz satisfies e_0\leq \{0.175,\, 0.125,\,0.175,\,0.175  \}, respectively.
Revised:  We use ENIGMA to show that the gravitational wave transients GW150914, GW151226, GW170104 and GW170814 can be effectively recovered with spinning, quasi-circular templates if the eccentricity of these events at a gravitational wave frequency of 10Hz satisfies e_0\leq \{0.175,\, 0.125,\,0.175,\,0.175 \}, respectively.
Edit intent: coherence
Original:  In particular, we use ENIGMA to show that the gravitational wave transients GW150914, GW151226, GW170104 and GW170814  can be effectively recovered with spinning, quasi-circular templates if the eccentricity of these events at a gravitational wave frequency of 10Hz satisfies e_0\leq \{0.175,\, 0.125,\,0.175,\,0.175  \}, respectively.
Revised:  In particular, we use ENIGMA to show that GW150914, GW151226, GW170104 and GW170814 can be effectively recovered with spinning, quasi-circular templates if the eccentricity of these events at a gravitational wave frequency of 10Hz satisfies e_0\leq \{0.175,\, 0.125,\,0.175,\,0.175 \}, respectively.
Edit intent: clarity
Original:  In particular, we use ENIGMA to show that the gravitational wave transients GW150914, GW151226, GW170104 and GW170814  can be effectively recovered with spinning, quasi-circular templates if the eccentricity of these events at a gravitational wave frequency of 10Hz satisfies e_0\leq \{0.175,\, 0.125,\,0.175,\,0.175  \}, respectively.
Revised:  In particular, we use ENIGMA to show that the gravitational wave transients GW150914, GW151226, GW170104 , GW170814 can be effectively recovered with spinning, quasi-circular templates if the eccentricity of these events at a gravitational wave frequency of 10Hz satisfies e_0\leq \{0.175,\, 0.125,\,0.175,\,0.175 \}, respectively.
Edit intent: fluency
Original:  In particular, we use ENIGMA to show that the gravitational wave transients GW150914, GW151226, GW170104 and GW170814  can be effectively recovered with spinning, quasi-circular templates if the eccentricity of these events at a gravitational wave frequency of 10Hz satisfies e_0\leq \{0.175,\, 0.125,\,0.175,\,0.175  \}, respectively.
Revised:  In particular, we use ENIGMA to show that the gravitational wave transients GW150914, GW151226, GW170104 and GW170814 can be effectively recovered with spinning, quasi-circular templates if the eccentricity of these events at a gravitational wave frequency of 10Hz satisfies e_0\leq \{0.175,\, 0.125,\,0.175,\,0.175 ,\, 0.125 \}, respectively.
Edit intent: fluency
Original:  While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies that lead to their most effective and efficient use , remains challenging.
Revised:  While various preventive measures exist (i.a., vaccination and school closures), deciding on strategies that lead to their most effective and efficient use remains challenging.
Edit intent: fluency
Original:  To this end, individual-based epidemiological models are essential to assist decision makers in determining the best strategy to curve epidemic spread.
Revised:  To this end, individual-based epidemiological models are essential to assist decision makers in determining the best strategy to curb epidemic spread.
Edit intent: fluency
Original:  However, individual-based models are computationally intensive and therefore it is pivotal to identify the optimal strategy using a minimal amount of model evaluations.
Revised:  However, individual-based models are computationally intensive and it is therefore pivotal to identify the optimal strategy using a minimal amount of model evaluations.
Edit intent: fluency
Original:  Consequently, we present a new sampling method to optimize the evaluation of preventive strategies using fixed budget best-arm identification algorithms.
Revised:  Consequently, we present a new sampling technique to optimize the evaluation of preventive strategies using fixed budget best-arm identification algorithms.
Edit intent: clarity
Original: We report the emergent dynamics of a community structured modular network of chaotic Hindmarsh-Rose  neurons with inhibitory synapses.
Revised: We report the emergent dynamics of a community structured modular network of chaotic Hindmarsh-Rose (HR) neurons with inhibitory synapses.
Edit intent: clarity
Original:  We find the inhibitory coupling between the neuronal modules leads to complete synchronization of neurons in a module, and also pushes modules into interesting sequence of travelling burst patterns.
Revised:  We find the inhibitory coupling between the neuronal modules lead to complete synchronization of neurons in a module, and also pushes modules into interesting sequence of travelling burst patterns.
Edit intent: fluency
Original:  We find the inhibitory coupling between the neuronal modules leads to complete synchronization of neurons in a module, and also pushes modules into interesting sequence of travelling burst patterns.
Revised:  We find the inhibitory coupling between the neuronal modules leads to complete synchronization of neurons in a module, and also pushes modules into interesting sequences of travelling burst patterns.
Edit intent: fluency
Original:  When differing dynamical time-scales are introduced for neurons in each module , hence breaking the symmetry between them, we see specific sequences of travelling burst patterns that are characteristic of the time-scale mismatch and coupling strengths.
Revised:  When dynamical time-scales are introduced for neurons in each module , hence breaking the symmetry between them, we see specific sequences of travelling burst patterns that are characteristic of the time-scale mismatch and coupling strengths.
Edit intent: clarity
Original:  When differing dynamical time-scales are introduced for neurons in each module , hence breaking the symmetry between them, we see specific sequences of travelling burst patterns that are characteristic of the time-scale mismatch and coupling strengths.
Revised:  When differing dynamical time-scales vary for neurons in each module , hence breaking the symmetry between them, we see specific sequences of travelling burst patterns that are characteristic of the time-scale mismatch and coupling strengths.
Edit intent: clarity
Original:  When differing dynamical time-scales are introduced for neurons in each module , hence breaking the symmetry between them, we see specific sequences of travelling burst patterns that are characteristic of the time-scale mismatch and coupling strengths.
Revised:  When differing dynamical time-scales are introduced for neurons in different modules , hence breaking the symmetry between them, we see specific sequences of travelling burst patterns that are characteristic of the time-scale mismatch and coupling strengths.
Edit intent: clarity
Original:  When differing dynamical time-scales are introduced for neurons in each module , hence breaking the symmetry between them, we see specific sequences of travelling burst patterns that are characteristic of the time-scale mismatch and coupling strengths.
Revised:  When differing dynamical time-scales are introduced for neurons in each module , hence breaking the symmetry among them, we see specific sequences of travelling burst patterns that are characteristic of the time-scale mismatch and coupling strengths.
Edit intent: fluency
Original:  For a modular network with only two time-scales, the neuronal sub-ensembles enter into synchronized frequency locked clusters with the bursting sequences having recurring patterns  .
Revised:  Thus for a modular network with only two time-scales, the neuronal sub-ensembles enter into synchronized frequency locked clusters with the bursting sequences having recurring patterns .
Edit intent: coherence
Original:  For a modular network with only two time-scales, the neuronal sub-ensembles enter into synchronized frequency locked clusters with the bursting sequences having recurring patterns  .
Revised:  For a modular network with two time-scales, the neuronal sub-ensembles enter into synchronized frequency locked clusters with the bursting sequences having recurring patterns .
Edit intent: clarity
Original:  For a modular network with only two time-scales, the neuronal sub-ensembles enter into synchronized frequency locked clusters with the bursting sequences having recurring patterns  .
Revised:  For a modular network with only two time-scales, the neuronal communities enter into synchronized frequency locked clusters with the bursting sequences having recurring patterns .
Edit intent: clarity
Original:  This paper presents WYS* , a new domain-specific language (DSL) implementation for writing MPCs.
Revised:  This paper presents Wys* , a new domain-specific language (DSL) implementation for writing MPCs.
Edit intent: fluency
Original:  WYS* is a Verified, Domain-Specific Integrated Language Extension (VDSILE), a new kind of embedded DSL hosted in F*, a full-featured, verification-oriented  programming language.
Revised:  WYS* is a Verified, Domain-Specific Integrated Language Extension (VDSILE), a new kind of embedded DSL hosted in F*, a verification-oriented programming language.
Edit intent: clarity
Original:  WYS* source programs are essentially F* programs written against an MPC library , meaning that  programmers can use F*'s logic to verify the correctness and security properties of their programs.
Revised:  Wys* source programs are essentially F* programs written against an MPC library , meaning that programmers can use F*'s logic to verify the correctness and security properties of their programs.
Edit intent: fluency
Original:  WYS* source programs are essentially F* programs written against an MPC library , meaning that  programmers can use F*'s logic to verify the correctness and security properties of their programs.
Revised:  WYS* source programs are essentially F* programs written against an MPC library , meaning that the programmers can use F*'s logic to verify the correctness and security properties of their programs.
Edit intent: coherence
Original:  To reason about the distributed  semantics of these programs, we formalize a deep embedding of WYS* , also in F*.
Revised:  To reason about the distributed semantics of these programs, we formalize a deep embedding of Wys* , also in F*.
Edit intent: fluency
Original:  We mechanize the necessary metatheory to prove that the properties verified for the WYS* source programs carry over to the distributed, multi-party semantics.
Revised:  We mechanize the necessary metatheory to prove that the properties verified for the Wys* source programs carry over to the distributed, multi-party semantics.
Edit intent: fluency
Original:  Finally, we use F*'s extraction mechanism to extract an interpreter that we have proved matches this semantics, yielding a  verified implementation.
Revised:  Finally, we use F*'s extraction to extract an interpreter that we have proved matches this semantics, yielding a verified implementation.
Edit intent: clarity
Original:  WYS* is the first DSL to enable formal verification of source MPC programs, and also the first MPC DSL to provide a verified implementation.
Revised:  Wys* is the first DSL to enable formal verification of source MPC programs, and also the first MPC DSL to provide a verified implementation.
Edit intent: fluency
Original:  With WYS* we have implemented several MPC protocols  , including private set intersection, joint median, and an MPC-based card dealing application, and have verified their security and correctness .
Revised:  With WYS* we have implemented several MPC protocols , including private set intersection, joint median, and an MPC card dealing application, and have verified their security and correctness .
Edit intent: clarity
Original:  With WYS* we have implemented several MPC protocols  , including private set intersection, joint median, and an MPC-based card dealing application, and have verified their security and correctness .
Revised:  With WYS* we have implemented several MPC protocols , including private set intersection, joint median, and an MPC-based card dealing application, and have verified their correctness and security .
Edit intent: fluency
Original: Brain-inspired neuromorphic computing has recently emerged as a promising avenue towards building the next generation of intelligent computing systems.
Revised: Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems.
Edit intent: clarity
Original:  It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could be used to efficiently represent the strength of synaptic connections between the neuronal nodes in neural networks.
Revised:  It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the strength of synaptic connections between the neuronal nodes in neural networks.
Edit intent: coherence
Original:  It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could be used to efficiently represent the strength of synaptic connections between the neuronal nodes in neural networks.
Revised:  It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could be used to efficiently represent the synaptic weights in artificial neural networks.
Edit intent: clarity
Original:  However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be a challengingtask, primarily due to the physical mechanisms that underlie the operation of these devices .
Revised:  However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging .
Edit intent: clarity
Original:  To address this challenge , we present a generic multi-memristive synaptic architecture with an efficient global clock-based arbitration scheme.
Revised:  To address this , we present a generic multi-memristive synaptic architecture with an efficient global clock-based arbitration scheme.
Edit intent: clarity
Original:  To address this challenge , we present a generic multi-memristive synaptic architecture with an efficient global clock-based arbitration scheme.
Revised:  To address this challenge , we present a multi-memristive synaptic architecture with an efficient global clock-based arbitration scheme.
Edit intent: clarity
Original:  To address this challenge , we present a generic multi-memristive synaptic architecture with an efficient global clock-based arbitration scheme.
Revised:  To address this challenge , we present a generic multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme.
Edit intent: fluency
Original:  We show that this concept is applicable to both artificial neural networks (ANN) and spiking neural networks (SNN). Experimental results involving over a million phase change memory devices are presented where an SNN with multi-memristive synapses is used for unsupervised learning of temporal correlations between event-based data streams .
Revised:  We show that this concept is applicable to both artificial neural networks (ANN) and spiking neural networks (SNN). Experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations between event-based data streams .
Edit intent: coherence
Original:  We show that this concept is applicable to both artificial neural networks (ANN) and spiking neural networks (SNN). Experimental results involving over a million phase change memory devices are presented where an SNN with multi-memristive synapses is used for unsupervised learning of temporal correlations between event-based data streams .
Revised:  We show that this concept is applicable to both artificial neural networks (ANN) and spiking neural networks (SNN). Experimental results involving over a million phase change memory devices are presented where an SNN with multi-memristive synapses is used for unsupervised learning of temporal correlations using a spiking neural network .
Edit intent: clarity
Original:  The work presented opens a pathway for realizing large-scale neural networks using memristive technology, and represents a significant step towards the realization of  energy-efficient neuromorphic computing systems.
Revised:  The work presents a significant step towards the realization of energy-efficient neuromorphic computing systems.
Edit intent: clarity
Original:  The work presented opens a pathway for realizing large-scale neural networks using memristive technology, and represents a significant step towards the realization of  energy-efficient neuromorphic computing systems.
Revised:  The work presented opens a pathway for realizing large-scale neural networks using memristive technology, and represents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems.
Edit intent: clarity
Original: In this work we propose a deep architecture for the classification of multivariate time series.
Revised: We propose a deep architecture for the classification of multivariate time series.
Edit intent: clarity
Original:  By means of a recurrent and untrained reservoir we generate a vectorial representation that embeds the temporal relationships in the data.
Revised:  By means of a recurrent and untrained reservoir we generate a vectorial representation that embeds temporal relationships in the data.
Edit intent: fluency
Original:  To overcome the limitations of the reservoir vanishing memory, we introduce a bidirectional reservoir, whose last state captures also the past dependencies in the input.
Revised:  To improve the memorization capability, we implement a bidirectional reservoir, whose last state captures also the past dependencies in the input.
Edit intent: clarity
Original:  To overcome the limitations of the reservoir vanishing memory, we introduce a bidirectional reservoir, whose last state captures also the past dependencies in the input.
Revised:  To overcome the limitations of the reservoir vanishing memory, we introduce a bidirectional reservoir, whose last state captures also past dependencies in the input.
Edit intent: fluency
Original:  These are subsequently fed into a deep feedforward network , which is trained to perform the final classification.
Revised:  These are subsequently fed into a deep feedforward network trained to perform the final classification.
Edit intent: coherence
Original:  Results show that our method performs better than a standard echo state network , andit can be trained much faster than a fully-trained recurrent network  .
Revised:  Results show that our method performs better than a standard echo state network and, at the same time, achieves results comparable to a fully-trained recurrent network .
Edit intent: clarity
Original:  We analytically demonstrate that, in the case when the recovery time \tau R is larger than the infection time \tau I , the infection flows on a one direction, i. e. from an infected node to its susceptible neighbor but, that neighbor can not reinfect that node during its infection time.
Revised:  We analytically demonstrate that, in the case of the recovery time \tau R is larger than the infection time \tau I , the infection flows on a one direction, i. e. from an infected node to its susceptible neighbor but, that neighbor can not reinfect that node during its infection time.
Edit intent: fluency
Original:  We analytically demonstrate that, in the case when the recovery time \tau R is larger than the infection time \tau I , the infection flows on a one direction, i. e. from an infected node to its susceptible neighbor but, that neighbor can not reinfect that node during its infection time.
Revised:  We analytically demonstrate that, in the case when the recovery time R is larger than the infection time \tau I , the infection flows on a one direction, i. e. from an infected node to its susceptible neighbor but, that neighbor can not reinfect that node during its infection time.
Edit intent: fluency