wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We extend the theory underlying the Neural Engineering Framework to systematically exploit nonlinear interactions between the local membrane potential and conductance-based synaptic channels as a computational resource. In particular, we demonstrate that even a single passive distal dendritic compartment with AMPA and GABA-A synapses connected to a leaky integrate-and-fire neuron supports the computation of a wide variety of multivariate, bandlimited functions, including the Euclidean norm, controlled shunting, and non-negative multiplication.
Revised:  We extend the theory underlying the Neural Engineering Framework to systematically exploit nonlinear interactions between the local membrane potential and conductance-based synapses. We apply these extensions to a two-compartment LIF neuron that can be seen as a simple model of passive dendritic computation. We show that it is possible to incorporate neuron models with input-dependent nonlinearities into the Neural Engineering Framework without compromising high-level function and that nonlinear post-synaptic currents can be systematically exploited to compute a wide variety of multivariate, bandlimited functions, including the Euclidean norm, controlled shunting, and non-negative multiplication.
Edit intent: clarity
Original:  Our results demonstrate that, for certain operations, the accuracy of dendritic computation is on a par with or even surpasses the accuracy of an additional layer of neurons in the network.
Revised:  By avoiding an additional source of spike noise, the function-approximation accuracy of a single layer of two-compartment LIF neurons is on a par with or even surpasses the accuracy of an additional layer of neurons in the network.
Edit intent: meaning-changed
Original:  Our results demonstrate that, for certain operations, the accuracy of dendritic computation is on a par with or even surpasses the accuracy of an additional layer of neurons in the network. These findings allow modelers to construct large-scale models of neurobiological systems that closer approximate network topologies and computational resources available in biology. Our results may inform neuromorphic hardware design and could lead to a better utilization of resources on existing neuromorphic hardware platforms .
Revised:  Our results demonstrate that, for certain operations, the accuracy of dendritic computation is on a par with or even surpasses that of two-layer spiking neural networks up to a certain target function bandwidth .
Edit intent: clarity
Original:  Strikingly, geometric renormalization of connectome maps in hyperbolic space, which decreases the resolution by coarse-graining and averaging over short similarity distances , predicts the properties of connectomes including self-similarity .
Revised:  Strikingly, a geometric network model, where distances are not Euclidean, predicts the multiscale properties of connectomes, including self-similarity. The model relies on the application of a geometric renormalization protocol which decreases the resolution by coarse-graining and averaging over short similarity distances , predicts the properties of connectomes including self-similarity .
Edit intent: meaning-changed
Original:  Our results suggest that the same principles regulate connectivity between brain regions at different length scales and that the multiscale self-similarity of brain connectomes may offer an advantageous architecture for  navigation purposes.
Revised:  Our results prove that simplicity is one of URLanizing principles of the large-scale self-similar architecture of human structural brain networks, with the same connectivity law ruling short and long range connections and operating between brain regions at different length scales and that the multiscale self-similarity of brain connectomes may offer an advantageous architecture for navigation purposes.
Edit intent: meaning-changed
Original:  Our results prove that simplicity is one of URLanizing principles of the large-scale self-similar architecture of human structural brain networks, with the same connectivity law ruling short and long range connections and operating between brain regions at different length scales.
Revised:  Our results suggest that URLanizing principles underlie the multiscale architecture of human structural brain networks, with the same connectivity law ruling short and long range connections and operating between brain regions at different length scales.
Edit intent: clarity
Original:  Our results prove that simplicity is one of URLanizing principles of the large-scale self-similar architecture of human structural brain networks, with the same connectivity law ruling short and long range connections and operating between brain regions at different length scales.
Revised:  Our results prove that simplicity is one of URLanizing principles of the large-scale self-similar architecture of human structural brain networks, where the same connectivity law ruling short and long range connections and operating between brain regions at different length scales.
Edit intent: coherence
Original:  Our results prove that simplicity is one of URLanizing principles of the large-scale self-similar architecture of human structural brain networks, with the same connectivity law ruling short and long range connections and operating between brain regions at different length scales. Moreover, the multiscale self-similarity of brain connectomes may offer an advantageous architecture for decentralized navigation purposes.
Revised:  Our results prove that simplicity is one of URLanizing principles of the large-scale self-similar architecture of human structural brain networks, with the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions.
Edit intent: clarity
Original:  Linear stability analysis of the resulting models is performed and it is shown that harvesting is sustainable . We next introduce a fractionalised time derivative to the model and investigate its effect. It is revealed that the so-called memory concept of the fractional derivative damps out oscillations in the population numbers so that the system as a whole settles on a equilibrium quicker than it would with integer time derivatives  .
Revised:  Linear stability analysis of the resulting models is performed and it is shown that harvesting is sustainable , and the memory concept of the fractional derivative damps out oscillations in the population numbers so that the system as a whole settles on a equilibrium quicker than it would with integer time derivatives .
Edit intent: coherence
Original:  It is revealed that the so-called memory concept of the fractional derivative damps out oscillations in the population numbers so that the system as a whole settles on a equilibrium quicker than it would with integer time derivatives  .
Revised:  It is revealed that the so-called memory concept of the fractional derivative damps out oscillations in the population numbers so that the system as a whole settles on an equilibrium quicker than it would with integer time derivatives .
Edit intent: fluency
Original:  It is revealed that the so-called memory concept of the fractional derivative damps out oscillations in the population numbers so that the system as a whole settles on a equilibrium quicker than it would with integer time derivatives  .
Revised:  It is revealed that the so-called memory concept of the fractional derivative damps out oscillations in the population numbers so that the system as a whole settles on a equilibrium quicker than it would with integer time derivatives . Finally, some possible physical explanations are given for the obtained results. It is shown that the stability requires the memory concept in the model .
Edit intent: meaning-changed
Original:   Volume change and advective solute transport varies with the phase of muscle stimulation during periodic contraction but remains significant at all conditions . Nonetheless, advective transport varies will occur in all cases where the sarcomere is not isovolumetric .
Revised:  Lattice spacing depends on a variety of factors that produce radial force, including crossbridges, titin-like molecules, and other structural proteins. Volume change and advective solute transport varies with the phase of muscle stimulation during periodic contraction but remains significant at all conditions . Nonetheless, advective transport varies will occur in all cases where the sarcomere is not isovolumetric .
Edit intent: meaning-changed
Original:   Volume change and advective solute transport varies with the phase of muscle stimulation during periodic contraction but remains significant at all conditions . Nonetheless, advective transport varies will occur in all cases where the sarcomere is not isovolumetric .
Revised:  Volume change and advective transport varies with the phase of muscle stimulation during periodic contraction but remains significant at all conditions . Nonetheless, advective transport varies will occur in all cases where the sarcomere is not isovolumetric .
Edit intent: fluency
Original:   Volume change and advective solute transport varies with the phase of muscle stimulation during periodic contraction but remains significant at all conditions . Nonetheless, advective transport varies will occur in all cases where the sarcomere is not isovolumetric .
Revised:  Volume change and advective solute transport varies with the phase of muscle stimulation but remains significant at all conditions . Nonetheless, advective transport varies will occur in all cases where the sarcomere is not isovolumetric .
Edit intent: clarity
Original:   Simulations show that the test controls size in small samples (n = 30, p = 1000), and often has substantially higher power against alternatives with multiple violations  .
Revised:  We provide a condition guaranteeing size control in large samples. Simulations show that the test controls size in small samples (n = 30, p = 1000), and often has substantially higher power against alternatives with multiple violations .
Edit intent: meaning-changed
Original:  They are also satisfied empirically by the option data on the S&P 500 over a ten yearsperiod. Our proposed model outperforms the widely used surface stochastic volatility inspired  model on the mean average percentage error in both in-sample and out-of-sample datasets.
Revised:  They are also satisfied empirically by the option data on the S&P 500 index over twenty years. The developed neural network model and its simplified variations outperform the widely used surface stochastic volatility inspired model on the mean average percentage error in both in-sample and out-of-sample datasets.
Edit intent: meaning-changed
Original:  The research of this study has a fundamental methodological contribution to the emerging trend of applying the state-of-the-art information technology into business studies as our model provides a frameworkof integrating data-driven machine learning algorithms with financial theories and this framework can be easily extended and applied to solve other problems in finance or other business fields .
Revised:  This study has two main methodological contributions. First, an accurate deep learning prediction model is developed and tailored to implied volatility surfaces. Second, a framework, which seamlessly combines data-driven machine learning algorithms with financial theories and this framework can be easily extended and applied to solve other problems in finance or other business fields .
Edit intent: meaning-changed
Original:  The research of this study has a fundamental methodological contribution to the emerging trend of applying the state-of-the-art information technology into business studies as our model provides a frameworkof integrating data-driven machine learning algorithms with financial theories and this framework can be easily extended and applied to solve other problems in finance or other business fields .
Revised:  The research of this study has a fundamental methodological contribution to the emerging trend of applying the state-of-the-art information technology into business studies as our model provides a frameworkof integrating data-driven models with financial theories and this framework can be easily extended and applied to solve other problems in finance or other business fields .
Edit intent: clarity
Original:  The research of this study has a fundamental methodological contribution to the emerging trend of applying the state-of-the-art information technology into business studies as our model provides a frameworkof integrating data-driven machine learning algorithms with financial theories and this framework can be easily extended and applied to solve other problems in finance or other business fields .
Revised:  The research of this study has a fundamental methodological contribution to the emerging trend of applying the state-of-the-art information technology into business studies as our model provides a frameworkof integrating data-driven machine learning algorithms with financial theories , can be extended and applied to solve other problems in finance or other business fields .
Edit intent: coherence
Original:  The research of this study has a fundamental methodological contribution to the emerging trend of applying the state-of-the-art information technology into business studies as our model provides a frameworkof integrating data-driven machine learning algorithms with financial theories and this framework can be easily extended and applied to solve other problems in finance or other business fields .
Revised:  The research of this study has a fundamental methodological contribution to the emerging trend of applying the state-of-the-art information technology into business studies as our model provides a frameworkof integrating data-driven machine learning algorithms with financial theories and this framework can be easily extended and applied to solve other related business problems .
Edit intent: clarity
Original: This paper presents a framework of developing neural networks to predict implied volatility surfaces. It can incorporate the related properties from existing mathematical models and empirical findings, including no static arbitrage, limiting boundaries , asymptotic slopeand volatility smile. These properties are also satisfied empirically in our experiments with the option data on the S&P 500 index over 20 years.
Revised: In this paper we develop a novel neural network model for predicting implied volatility surface. Prior financial domain knowledge is taken into account. A new activation function that incorporates volatility smile is proposed, which is used for the hidden nodes that process the underlying asset price. In addition, financial conditions, such as the absence of arbitrage, the boundaries and the asymptotic slope, are embedded into the loss function. This is one of the very first studies which discuss a methodological framework that incorporates prior financial domain knowledge into neural network architecture design and model training. The proposed model outperforms the benchmarked models with the option data on the S&P 500 index over 20 years.
Edit intent: clarity
Original:  The developed neural network model outperforms the widely used surface stochastic volatility inspired (SSVI) model and other benchmarked neural network models on the mean average percentage error in both in-sample and out-of-sample datasets. This study has two major contributions. First, it contributes to the recent use of machine learning in finance, and an accurate deep learning implied volatility surface prediction model is obtained.
Revised:  More importantly, the domain knowledge is satisfied empirically, showing the model is consistent with the existing financial theories and conditions related to implied volatility surface prediction model is obtained.
Edit intent: coherence
Original:  Here, we demonstrate the utility of this approach by showing that the Microbial Consumer Resource Model (MiCRM) - a minimal model for microbial communities with resource competition, metabolic crossfeeding and stochastic colonization -- can qualitatively reproduce patterns found in survey data including compositional gradients, dissimilarity/overlap correlations, richness/harshness correlations, and nestedness of community composition.
Revised:  Here, we demonstrate the utility of this approach by showing that the Microbial Consumer Resource Model (MiCRM) -- a minimal model for microbial communities with resource competition, metabolic crossfeeding and stochastic colonization -- can qualitatively reproduce patterns found in survey data including compositional gradients, dissimilarity/overlap correlations, richness/harshness correlations, and nestedness of community composition.
Edit intent: fluency
Original: Neural coding is one of the central questions in systems neuroscience for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of brain-machine interface, where decoding incoming stimulus is needed for better performance of physical devices.
Revised: Neural coding is one of the central questions in systems neuroscience for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of brain-machine interface, where decoding incoming stimulus is highly demanded for better performance of physical devices.
Edit intent: clarity
Original:   In addition,  we show that SID can be generalized to arbitrary images by using image datasets of MNIST, CIFAR10, and CIFAR100.
Revised:  Our SID also outperforms on the reconstruction of visual stimulus compared to existing fMRI decoding models. In addition, we show that SID can be generalized to arbitrary images by using image datasets of MNIST, CIFAR10, and CIFAR100.
Edit intent: meaning-changed
Original:  The new process helps to integrate  alternatives of description and to derive  equations for the probability density function (PDF) of  burst and inter-burst duration of the proposed continuous time birth-death processes . This PDF might be used to discriminate between spurious memory and true long-range memory in complex systems exhibiting continuing fluctuations .
Revised:  The new process helps to integrate alternatives of description and to derive equations for the probability density function (PDF) of burst and inter-burst duration of the proposed continuous time birth-death processes .
Edit intent: coherence
Original:  We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in HCI . In two laboratory experiments, participants performed an aided decision task .
Revised:  We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in the interaction with intelligent systems . In two laboratory experiments, participants performed an aided decision task .
Edit intent: meaning-changed
Original:  We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in HCI . In two laboratory experiments, participants performed an aided decision task .
Revised:  We analyze the descriptive abilities of a newly developed responsibility quantification model (ResQu) to predict actual human responsibility and perceptions of responsibility in HCI . In two laboratory experiments, participants performed a classification task, and were aided by binary automated classification systems with different capabilities .
Edit intent: meaning-changed
Original:  However, observed values differed from the model predictions, as less knowledgeable participants overestimated their own capabilities  and assumed greater-than-optimal responsibility.
Revised:  The model generally provided quite accurate predictions of the actual values of the measured responsibility. A bias existed only when participants' classification capabilities were much worse than those of the automated classification system. In this case, the participants overestimated their own capabilities and assumed greater-than-optimal responsibility.
Edit intent: meaning-changed
Original:  It can be used to aid system design and guide policy and legal decisions  regarding human responsibility in events involving intelligent systems.
Revised:  It is possible to compute a ResQu model score to predict behavior or perceptions of responsibility, taking into account the characteristics of the human, the intelligent system and the environment. The ResQu model provides a new quantitative method that may aid system design and guide policy and legal decisions regarding human responsibility in events involving intelligent systems.
Edit intent: meaning-changed
Original:  We prove that (\rho_t)_{t \in [0,T]} can be decomposed in a pair of risk measures, acting before and after \tau and we characterize its properties giving suitable assumptions on the driver of the BSDE. Furthermore, we prove an inequality satisfied by the penalty term associated to the robust representation of (\rho_t)_{t \in [0,T]} and we provide an explicit example of such kind of dynamic risk measures, along with its decomposition .
Revised:  We prove that (\rho_t)_{t \in [0,T]} can be decomposed in a pair of risk measures, acting before and after \tau and we characterize its properties giving suitable assumptions on the driver of the BSDE. Furthermore, we prove an inequality satisfied by the penalty term associated to the robust representation of (\rho_t)_{t \in [0,T]} and we discuss the dynamic entropic risk measure case, providing examples where it is possible to write explicitly its decomposition and simulate it numerically .
Edit intent: meaning-changed
Original:  Yet in contrast to modern nonparametric methods and applied work with these procedures, empirical practice with the HP filter almost universally relies on standard settings for the tuning parameter that have been suggested largely by experimentation with macroeconomic data and heuristic reasoning about the form of economic cycles and trends .
Revised:  Yet in contrast to modern nonparametric methods and applied work with these procedures, empirical practice with the HP filter almost universally relies on standard settings for the tuning parameter that have been suggested largely by experimentation with macroeconomic data and heuristic reasoning .
Edit intent: clarity
Original:  The paper develops limit theory to show that the boosted HP  filter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks -- the most common trends that appear in macroeconomic data and current modeling methodology .
Revised:  The paper develops limit theory to show that the boosted HP (bHP) filter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks -- the most common trends that appear in macroeconomic data and current modeling methodology .
Edit intent: meaning-changed
Original:  The paper develops limit theory to show that the boosted HP  filter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks -- the most common trends that appear in macroeconomic data and current modeling methodology .
Revised:  The paper develops limit theory to show that the boosted HP filter asymptotically recovers trend mechanisms that involve unit root processes, deterministic polynomial drifts, and polynomial drifts with structural breaks .
Edit intent: clarity