wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The bootstrap's consistency is proven under mild assumptions for a general class of volatility models and bootstrap intervals are constructed for the conditional Value-at-Risk to quantify the uncertainty induced by estimation. A large-scale simulation study is conducted revealing that the equal-tailed percentile interval based on the fixed-design residual bootstrap tends to fall short of its nominal value.
Revised:  The bootstrap's consistency is proven under mild assumptions for a general class of volatility models and bootstrap intervals are constructed for the conditional Value-at-Risk . A simulation study reveals that the equal-tailed percentile interval based on the fixed-design residual bootstrap tends to fall short of its nominal value.
Edit intent: clarity
Original: This paper presents the nonparametric inference problem about the probability density function of a latent variable in the measurement error model with repeated measurements.
Revised: Kotlarski's identity has been widely used in applied economic research. However, how to conduct inference based on this popular identification approach has been an open question for two decades. This paper addresses this open problem by constructing a novel confidence band for the density function of a latent variable in the measurement error model with repeated measurements.
Edit intent: meaning-changed
Original: This paper presents the nonparametric inference problem about the probability density function of a latent variable in the measurement error model with repeated measurements. We construct a system of linear complex-valued moment restrictions by Kotlarski's identity , and then establish a confidence band for the density of the latent variable.
Revised: This paper presents the nonparametric inference problem about the probability density function of a latent variable in the measurement error model . The confidence band builds on our finding that we can rewrite Kotlarski's identity , and then establish a confidence band for the density of the latent variable.
Edit intent: meaning-changed
Original:  We construct a system of linear complex-valued moment restrictions by Kotlarski's identity , and then establish a confidence band for the density of the latent variable. Our confidence band controls the asymptotic size uniformly over a class of data generating processes, and it is consistent against all fixed alternatives.
Revised:  We construct a system of linear complex-valued moment restrictions by Kotlarski's identity as a system of linear moment restrictions. The confidence band controls the asymptotic size uniformly over a class of data generating processes, and it is consistent against all fixed alternatives.
Edit intent: clarity
Original: Rough path analysis applies successfully to multidimensional stochastic processes. Ito diffusion processes on the other hand have provided models for stock market prices since F. Black and M. Scholes, Harrison and co-authors. In the following note, we intend to bring some controlled rough path considerations to the study of multidimensional Black-Scholes models with position-dependent volatilities. The Ito formula for reduced rough paths provides the syntactical analogy to bridge classical Mathematical Financeto the (Davie's type)RDE-based one. Once such connection is established, rough integration bounds are employed to estimate the errors arising from time-discretisation of the theoretical continuous-time trading strategies. We find that the portfolio trajectory resulting from the hedging strategy, when analysed pathwise , is sensitive to Greeks beyond the leading first-order Delta, and is Gamma-sensitive in particular.
Revised: We describe the pricing and hedging practices refraining from the use of probability. We encode volatility in an enhancement of the price trajectory and we give pathwise presentations of the fundamental equations of Mathematical Finance. In particular this allows us to assess model misspecification, generalising the so-called fundamental theorem of derivative trading (see Ellersgaard et al. 2017). Our pathwise integrals and equations exhibit the role of Greeks beyond the leading first-order Delta, and is Gamma-sensitive in particular.
Edit intent: meaning-changed
Original:  We find that the portfolio trajectory resulting from the hedging strategy, when analysed pathwise , is sensitive to Greeks beyond the leading first-order Delta, and is Gamma-sensitive in particular. This provides a theoretical underpinning to reconcile traders' practice of Gamma-neutral hedging. We interpret the Gamma position in terms of (co-)variance swaps on the underlying securities, and we enlarge classical strategies in cash and stocks with positions on such swaps. Furthermore, the proposed framework addresses robustness of Delta hedging. We analyse the error that a practitioner incurs into when hedging with the wrong model. We do so without assuming an Ito's diffusion form for the actual price dynamic, whose trajectory can in particular be rougher than Brownian motion .
Revised:  We find that the portfolio trajectory resulting from the hedging strategy, when analysed pathwise , is sensitive to Greeks beyond the leading first-order Delta, and makes explicit the role of Gamma sensitivities .
Edit intent: clarity
Original: We describe the pricing and hedging practices refraining from the use of probability . We encode volatility in an enhancement of the price trajectory and we give pathwise presentations of the fundamental equations of Mathematical Finance.
Revised: We describe the pricing and hedging practices refraining from the use of probability using rough paths. By encoding the volatility of assets in an enhancement of the price trajectory and we give pathwise presentations of the fundamental equations of Mathematical Finance.
Edit intent: meaning-changed
Original:  We encode volatility in an enhancement of the price trajectory and we give pathwise presentations of the fundamental equations of Mathematical Finance. In particular this allows us to assess model misspecification, generalising the so-called fundamental theorem of derivative trading (see Ellersgaard et al. 2017).
Revised:  We encode volatility in an enhancement of the price trajectory , we give a pathwise presentation of the replication of European options. The continuity properties of rough-paths allow us to generalise the so-called fundamental theorem of derivative trading (see Ellersgaard et al. 2017).
Edit intent: meaning-changed
Original:  In particular this allows us to assess model misspecification, generalising the so-called fundamental theorem of derivative trading (see Ellersgaard et al. 2017). Our pathwise integrals and equations exhibit the role of Greeks beyond the leading-order Delta, and makes explicit the role of Gamma sensitivities .
Revised:  In particular this allows us to assess model misspecification, generalising the so-called fundamental theorem of derivative trading , showing that a small misspecification of the model will yield only a small excess profit or loss of the replication strategy. Our hedging strategy is an enhanced version of classical delta hedging where we use volatility swaps to hedge the second order terms arising in rough-path integrals, resulting in improved robustness .
Edit intent: meaning-changed
Original:  we derive analytic expressions for the first four conditional moments of the forward return, of the forward variance, of the aggregated return and of the aggregated variance -- corresponding moments for some specific GARCH models largely used in practice are recovered as special cases ;
Revised:  we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moment for the most commonly used GARCH models are stated as special cases ;
Edit intent: clarity
Original:  we derive analytic expressions for the first four conditional moments of the forward return, of the forward variance, of the aggregated return and of the aggregated variance -- corresponding moments for some specific GARCH models largely used in practice are recovered as special cases ; we derive the limits of these moments as the time horizon increases, establishing regularity conditions for the moments of aggregated returns to converge to normal moments ;
Revised:  we derive analytic expressions for the first four conditional moments of the forward return, of the forward variance, of the aggregated return and of the aggregated variance -- corresponding moments for some specific GARCH models largely used in practice are recovered as special cases . We also the limits of these moments as the time horizon increases, establishing regularity conditions for the moments of aggregated returns to converge to normal moments ;
Edit intent: clarity
Original:  we derive the limits of these moments as the time horizon increases, establishing regularity conditions for the moments of aggregated returns to converge to normal moments ; and we demonstrate empirically that some excellent approximate predictive distributions can be obtained from these analytic moments, thus precluding the need for time-consuming simulations.
Revised:  we derive the limits of these moments as the time horizon increases, establishing regularity conditions for the moments of aggregated returns to converge to normal moments . Our empirical study yields excellent approximate predictive distributions can be obtained from these analytic moments, thus precluding the need for time-consuming simulations.
Edit intent: clarity
Original: In this paper, we implement two state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO)  in portfolio management.
Revised: In this paper, we implement three state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) in portfolio management.
Edit intent: meaning-changed
Original: In this paper, we implement two state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO)  in portfolio management.
Revised: In this paper, we implement two state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG) , Proximal Policy Optimization (PPO) in portfolio management.
Edit intent: fluency
Original: In this paper, we implement two state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO)  in portfolio management.
Revised: In this paper, we implement two state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization (PPO) and Policy Gradient (PG) in portfolio management.
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Original:  We present the performances of them under different settings, including different learning rate, objective function, markets, feature combinations, in order to provide insights for parameter tuning, features selection and data preparation  .
Revised:  We present the performances of them under different settings, including different learning rate, objective function, markets, feature combinations, in order to provide insights for parameter tuning, features selection and data preparation . We also conduct intensive experiments in China Stock market and show that PG is more desirable in financial market than DDPG and PPO, although both of them are more advanced. What's more, we propose a so called Adversarial Training method and show that it can greatly improve the training efficiency and significantly promote average daily return and sharpe ratio in back test. Based on this new modification, our experiments results show that our agent based on Policy Gradient can outperform UCRP .
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Original:  We analyze the processes of system boot up and shutdown on two platforms, the Raspberry Pi 3 and  Zero Wireless, and enhance duty-cycling performance by identifying and disabling time consuming or unnecessary units initialized in the userspace.
Revised:  We analyze the processes of system boot up and shutdown on two platforms, the Raspberry Pi 3 and Raspberry Pi Zero Wireless, and enhance duty-cycling performance by identifying and disabling time consuming or unnecessary units initialized in the userspace.
Edit intent: fluency
Original:  We analyze the processes of system boot up and shutdown on two platforms, the Raspberry Pi 3 and  Zero Wireless, and enhance duty-cycling performance by identifying and disabling time consuming or unnecessary units initialized in the userspace.
Revised:  We analyze the processes of system boot up and shutdown on two platforms, the Raspberry Pi 3 and Zero Wireless, and enhance duty-cycling performance by identifying and disabling time-consuming or unnecessary units initialized in the userspace.
Edit intent: fluency
Original:  Moreover, it appears that classical dynamical analyses of reachability and attractors have a simpler computational complexity: -- reachability can be assessed in a polynomial number of iterations (instead of being PSPACE-complete with update modes);
Revised:  Moreover, it appears that classical dynamical analyzes of reachability and attractors have a simpler computational complexity: -- reachability can be assessed in a polynomial number of iterations (instead of being PSPACE-complete with update modes);
Edit intent: fluency
Original:  Moreover, it appears that classical dynamical analyses of reachability and attractors have a simpler computational complexity: -- reachability can be assessed in a polynomial number of iterations (instead of being PSPACE-complete with update modes);
Revised:  Moreover, it appears that classical dynamical analyses of reachability and attractors have a simpler computational complexity: - reachability can be assessed in a polynomial number of iterations (instead of being PSPACE-complete with update modes);
Edit intent: fluency
Original:  Moreover, it appears that classical dynamical analyses of reachability and attractors have a simpler computational complexity: -- reachability can be assessed in a polynomial number of iterations (instead of being PSPACE-complete with update modes); -- attractors are hypercubes, and deciding the existence of attractors with a given upper-bounded dimension is in NP (instead of PSPACE-complete with update modes) .
Revised:  Moreover, it appears that classical dynamical analyses of reachability and attractors have a simpler computational complexity: -- reachability can be assessed in a polynomial number of iterations .
Edit intent: meaning-changed
Original:  } otherwise (instead of being PSPACE-complete with update modes);- deciding wherever a configuration belongs to an attractor is in coNP with locally-monotonic BNs, and coNP^{\text{coNP}} otherwise (instead of PSPACE-complete with update modes).
Revised:  Thus, reachability is in P with locally-monotonic BNs, and P^{\text{NP} otherwise (instead of being PSPACE-complete with update modes);- deciding wherever a configuration belongs to an attractor is in coNP with locally-monotonic BNs, and coNP^{\text{coNP}} otherwise (instead of PSPACE-complete with update modes).
Edit intent: meaning-changed
Original:  and the semantics is minimal with respect to this model refinement criteria: to any most permissive trajectory, there exists a multilevel refinement of the BN which can reproduce it.} In brief, the most permissive semantics of BNs enables a correct abstract reasoning on dynamics of BNs, with a greater tractability than previously introduced update modes . This technical report lists the main definitions and properties of the most permissive semantics of BNs, and draw some remaining open questions .
Revised:  and the semantics is minimal with respect to this model refinement criteria: to any most permissive trajectory, there exists a multilevel refinement of the BN which can reproduce it.} In brief, the most permissive semantics of BNs enables a correct abstract reasoning on dynamics of BNs, with a greater tractability than previously introduced update modes .
Edit intent: coherence
Original:   We also demonstrate in  simulation study that our procedure has good small sample properties.
Revised:  To construct simultaneous confidence regions on many target parameters, sufficiently fast estimation rates of the nuisance functions are crucial. In this context, we establish uniform estimation rates and sparsity guarantees of the square-root estimator in a random design under approximate sparsity conditions that might be of independent interest for related problems in high-dimensions. We also demonstrate in simulation study that our procedure has good small sample properties.
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Original: Empirical applications of dynamic discrete choice models usually either take the discount factor to be known or rely on ad hoc functional form assumptions to identify and estimate it. We give identification results under economically motivated exclusion restrictions on primitive utilities.
Revised: Empirical research often cites observed choice responses to variation that shifts expected discounted future utilities, but not current utilities, as an intuitive source of information on time preferences. We study the identification of dynamic discrete choice models under such economically motivated exclusion restrictions on primitive utilities.
Edit intent: meaning-changed
Original:  In contrast, the structure of the boundaries for inhibitory coupling is different, and already for homogeneous networks unveils the presence of various partially synchronized states not present  the Kuramoto model: Collective chaos, quasiperiodic partial synchronization (QPS), and a novel state which we call modulated-QPS (M-QPS).
Revised:  In contrast, the structure of the boundaries for inhibitory coupling is different, and already for homogeneous networks unveils the presence of various partially synchronized states not present in the Kuramoto model: Collective chaos, quasiperiodic partial synchronization (QPS), and a novel state which we call modulated-QPS (M-QPS).
Edit intent: fluency
Original: In this paper, I first showed that an indirect information measure is supported by expected learning cost minimizationif and only if it satisfies: 1. monotonicity in Blackwell order, 2. sub-additivity in compound experiment and 3. linearity in mixing with no information.
Revised: We study the indirect cost of information from sequential information cost minimization. A key sub-additivity in compound experiment and 3. linearity in mixing with no information.
Edit intent: clarity
Original: In this paper, I first showed that an indirect information measure is supported by expected learning cost minimizationif and only if it satisfies: 1. monotonicity in Blackwell order, 2. sub-additivity in compound experiment and 3. linearity in mixing with no information. Then I studied a dynamic information acquisition problem with flexible design of information dynamics, costly waiting and costly information. When flow information measure satisfies the three conditions, dynamic problem can be solved in two steps: solving a static rational inattention problem, and implementing optimal learning dynamics. The optimal solution involves stationary Poisson direct signals: arrival of signal directly suggests optimal action, and non-arrival of signal provides no information .
Revised: In this paper, I first showed that an indirect information measure is supported by expected learning cost minimizationif and only if it satisfies: 1. monotonicity in Blackwell order, 2. sub-additivity condition, together with monotonicity equivalently characterizes the class of indirect cost functions generated from any direct information cost. Adding an extra (uniform) posterior separability condition equivalently characterizes the indirect cost generated from any direct cost favoring incremental evidences. We also provide the necessary and sufficient condition when prior independent direct cost generates posterior separable indirect cost .
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Original:  The state adjacent to the critical state  may serve as an indicator or a "precursor" for the critical state (market crash) and this novel method of identifying the long-term precursors may be very helpful for constructing the early warning system in financial markets, as well as in other complex systems.
Revised:  The state adjacent to the critical state (market crash) may serve as an indicator or a "precursor" for the critical state (market crash) and this novel method of identifying the long-term precursors may be very helpful for constructing the early warning system in financial markets, as well as in other complex systems.
Edit intent: fluency
Original:  The state adjacent to the critical state  may serve as an indicator or a "precursor" for the critical state (market crash) and this novel method of identifying the long-term precursors may be very helpful for constructing the early warning system in financial markets, as well as in other complex systems.
Revised:  The state adjacent to the critical state may serve as an indicator or a "precursor" for the critical state and this novel method of identifying the long-term precursors may be very helpful for constructing the early warning system in financial markets, as well as in other complex systems.
Edit intent: clarity
Original: The brain can display self-sustained activity (SSA), which is the persistent firing of neurons in the absence of external stimuli . This spontaneous activity shows low neuronal firing rates and is observed in diverse in vitro and in vivo situations .
Revised: The brain can display self-sustained activity (SSA), which is the persistent firing of neurons in the absence of external stimuli and contributes to signal propagation, neural coding, and dynamic stability. It also plays an important role in cognitive processes .
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Original:  In this work, we study the influence of excitatory/inhibitory balance, connection density, and network size on the self-sustained activity of a neuronal network model. We build a random network of adaptive exponential integrate-and-fire (AdEx) neuron models connected through inhibitory and excitatory chemical synapses.
Revised:  In this work, by means of studying intracellular recordings from CA1 neurons in rats and results from numerical simulations, we demonstrate that self-sustained activity presents high variability of patterns, such as low neural firing rates and activity in the form of small-bursts in distinct neurons. In our numerical simulations, we consider random networks composed of coupled, adaptive exponential integrate-and-fire (AdEx) neuron models connected through inhibitory and excitatory chemical synapses.
Edit intent: meaning-changed
Original:  We build a random network of adaptive exponential integrate-and-fire (AdEx) neuron models connected through inhibitory and excitatory chemical synapses. The AdEx model mimics several behaviours of biological neurons, such as spike initiation, adaptation, and bursting patterns. In an excitation/inhibition balanced state, if the mean connection degree (K) is fixed, the firing rate does not depend on the network size (N) , whereas for fixed N, the firing rate decreases when K increases. However, for large K, SSA states can appear only for large N. We show the existence of SSA states with similar behaviours to those observed in experimental recordings, such as very low and irregular neuronal firing rates, and spike-train power spectra with slow fluctuations, only for balanced networks of large size .
Revised:  We build a random network of adaptive exponential integrate-and-fire neurons. The neural dynamics in the random networks simulate regular spiking (excitatory) and spike-train power spectra with slow fluctuations, only for balanced networks of large size .
Edit intent: clarity