wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Here, the spiking statistics is derived for homoclinic conductance-based neuron models. Switching between rest and spiking is mainly determined along the downstroke of the action potential , a dynamical feature lacking in common neuron modelswith reset dynamics.
Revised:  Here, the spiking statistics is derived for homoclinic conductance-based neuron model in the bistable regime. The ISI densities of bistable homoclinic neurons are found to be unimodal yet distinct from the inverse Gaussian distribution associated with the saddle-node-on-invariant-cycle (SNIC) bifurcation. It is demonstrated that for the HOM bifurcation the transition between rest and spiking is mainly determined along the downstroke of the action potential , a dynamical feature lacking in common neuron modelswith reset dynamics.
Edit intent: meaning-changed
Original:  Switching between rest and spiking is mainly determined along the downstroke of the action potential , a dynamical feature lacking in common neuron modelswith reset dynamics. Nonetheless similar derivations can be applied to quadratic integrate-and-fire models. The derived interspike interval distribution is shown to be unimodal, which warrants the future use of meanfield approaches to networks of bistable neurons .
Revised:  Switching between rest and spiking is mainly determined along the downstroke of the action potential , a dynamical feature that is not captured by the commonly used reset neuron models. The deduced spike statistics can help to identify HOM dynamics in experimental data .
Edit intent: clarity
Original: We examine the classic "Beer-Quiche" game from Cho and Kreps (1987), and relax the assumption that the order placed by the senderis completely observable. Under the optimal degree of transparency, the receiverachieves a higher payoff than with full transparency. Partial obfuscation of the sender's choice encourages separation: committing to a less informative signal about the sender's choice affects the endogenous information generation process such that the receiver thereby secures himself more information .
Revised: How can a receiver design an information structure in order to elicit information from a sender? We study how a decision-maker can acquire more information from an agent by reducing her own ability to observe what the agent transmits. Intuitively, when the two parties' preferences are not perfectly aligned, this garbling relaxes the sender's concern that the receiver will use her information to the sender's disadvantage. We characterize the optimal information structure for the receiver. The main result is that under broad conditions, the receiver can do just as well as if she could commit to a rule mapping the sender's choice affects the endogenous information generation process such that the receiver thereby secures himself more information .
Edit intent: coherence
Original:  Partial obfuscation of the sender's choice encourages separation: committing to a less informative signal about the sender's choice affects the endogenous information generation process such that the receiver thereby secures himself more information .
Revised:  Partial obfuscation of the sender's choice encourages separation: committing to a less informative signal about the sender's message to actions: information design is just as good as full commitment. Similarly, we show that these conditions guarantee that ex ante information acquisition always benefits the receiver, even though this learning might actually lower the receiver's expected payoff in the absence of garbling. We illustrate these effects in a range of economically relevant examples .
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Original:  We used the recently released TUH EEG Seizure Corpus and conducted a thorough search space exploration to evaluate the performance of a combination of various pre-processing techniques, machine learning algorithms, and corresponding hyperparameters on this task.
Revised:  We used the recently released TUH EEG seizure corpus (V1.4.0 and V1.5.2) and conducted a thorough search space exploration to evaluate the performance of a combination of various pre-processing techniques, machine learning algorithms, and corresponding hyperparameters on this task.
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Original:  We show that our algorithms can reach a weighted F1 score of up to 0.907 thereby setting the first benchmark for scalp EEG based multi-class seizure type classification.
Revised:  We show that our algorithms can reach a weighted F1 score of up to 0.901 for seizure-wise cross validation and 0.561 for patient-wise cross validation thereby setting a benchmark for scalp EEG based multi-class seizure type classification.
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Original: We present a Bayesian hierarchical framework for both cross-sectional and time-series return prediction. Our approach builds on a market-timing predictive system that jointly allows for time-varying coefficients driven by  fundamental characteristics.
Revised: This paper investigates asset allocation problems when returns are predictable. We introduce a market-timing Bayesian hierarchical (BH) approach that adopts heterogeneous time-varying coefficients driven by fundamental characteristics.
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Original:  With a Bayesian formulation for ensemble learning, we examine the joint predictability as well as portfolio efficiency via predictive distribution. In the empirical analysis of asset-sector allocation, our hierarchical ensemble learning portfolio achieves 500\% cumulative returns in the period 1998-2017, and outperforms most workhorse benchmarks as well as the passive investing index. Our Bayesian inference for model selection identifies useful macro predictors (long-term yield, inflation, and stock market variance) and asset characteristics (dividend yield, accrual, and gross profit). Using the selected model for predicting sector evolution, an equally weighted long-short portfolio on winners over losers achieves a 46\% Sharpe ratio with a significant Jensen's alpha. Finally, we explore an underexploited connection between classical Bayesian forecasting and modern ensemble learning.
Revised:  Our approach includes a joint estimation of conditional expected returns and covariance matrix and considers estimation risk for portfolio analysis. The hierarchical prior allows modeling different assets separately while sharing information across assets. We demonstrate the performance of the U.S. equity market. Though the Bayesian forecast is slightly biased, our BH approach outperforms most alternative methods in point and interval prediction. Our BH approach in sector investment for the recent twenty years delivers a 0.92
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Original:  An initial investigation of the biological target system and the desired components as well as the definition of design objectivesincluding the functional mechanism are fundamental prerequisites. The subsequent computational design is needed to combine the selected components and to obtain novel functionality.
Revised:  An initial investigation of the biological target system and the desired components as well as the desired components in order to define design objectives. Subsequent computational design is needed to combine the selected components and to obtain novel functionality.
Edit intent: clarity
Original:  We show that the optimal full information spreads are biased when the exact market regime is unknown, and the MM needs to adjust for `regime risk' in terms of liquidity volatility and sensitivity to regime changes  .
Revised:  We show that the optimal full information spreads are biased when the exact market regime is unknown, and the market maker needs to adjust for `regime risk' in terms of liquidity volatility and sensitivity to regime changes .
Edit intent: clarity
Original:  We show that the optimal full information spreads are biased when the exact market regime is unknown, and the MM needs to adjust for `regime risk' in terms of liquidity volatility and sensitivity to regime changes  .
Revised:  We show that the optimal full information spreads are biased when the exact market regime is unknown, and the MM needs to adjust for additional regime uncertainty in terms of liquidity volatility and sensitivity to regime changes .
Edit intent: clarity
Original:  We show that the optimal full information spreads are biased when the exact market regime is unknown, and the MM needs to adjust for `regime risk' in terms of liquidity volatility and sensitivity to regime changes  .
Revised:  We show that the optimal full information spreads are biased when the exact market regime is unknown, and the MM needs to adjust for `regime risk' in terms of liquidity volatility and sensitivity to regime changes L sensitivity and observed order flow volatility .
Edit intent: meaning-changed
Original:  An important concern with SMM, which matches sample with simulated moments, is that a parametric distribution is required but economic quantities that depend on this distribution, such as welfare and asset-prices, can be sensitive to misspecification.
Revised:  An important concern with SMM, which matches sample with simulated moments, is that a parametric distribution is required . However, economic quantities that depend on this distribution, such as welfare and asset-prices, can be sensitive to misspecification.
Edit intent: coherence
Original:  Here we combine complementary computational/theoretical approaches to elucidate the structural and thermodynamic details of the chaperone-induced expansion of a substrate protein  with a particular emphasis on the critical role played by ATP hydrolysis.
Revised:  Here we combine complementary computational/theoretical approaches to elucidate the structural and thermodynamic details of the chaperone-induced expansion of a substrate protein , with a particular emphasis on the critical role played by ATP hydrolysis.
Edit intent: fluency
Original:  Our results are in quantitative agreement with recent single-molecule FRET experiments and highlight the stark non-equilibrium nature of the process, showing that the Hsp70s are optimized to effectively convert chemical energy into mechanical work close to physiological conditions.
Revised:  Our results are in quantitative agreement with recent single-molecule FRET experiments and highlight the stark non-equilibrium nature of the process, showing that Hsp70s are optimized to effectively convert chemical energy into mechanical work close to physiological conditions.
Edit intent: clarity
Original:  Our results are in quantitative agreement with recent single-molecule FRET experiments and highlight the stark non-equilibrium nature of the process, showing that the Hsp70s are optimized to effectively convert chemical energy into mechanical work close to physiological conditions.
Revised:  Our results are in quantitative agreement with recent single-molecule FRET experiments and highlight the stark non-equilibrium nature of the process, showing that the Hsp70s are optimized to convert effectively chemical energy into mechanical work close to physiological conditions.
Edit intent: fluency
Original: Our first main results are the existence and uniqueness of solutions to a `generalised CIR initial-value problem' , which originates from the classical Cox-Ingersoll-Ross (CIR) stochastic differential equation after a time-change.
Revised: Motivated by successes of fast reverting volatility models, and the implicit dependence of `rough' processes on infinitesimal reversionary timescales, we establish a pathwise volatility framework which leads to a complete understanding of volatility trajectories' behaviour in the limit of infinitely-fast reversion. Towards this, we first establish processes that are weakly equivalent to Cox-Ingersoll-Ross (CIR) stochastic differential equation after a time-change.
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Original: Our first main results are the existence and uniqueness of solutions to a `generalised CIR initial-value problem' , which originates from the classical Cox-Ingersoll-Ross (CIR) stochastic differential equation after a time-change. These results hold on a pathwise basis for any continuous driver, and solutions are shown to accommodate any practicable continuous volatility path. We then prove a certain `fast reversion'pathwise convergence of our solutions to a hitting-time limit on the (D,M) topologies of Skorokhod (1956) , thereby also accommodating discontinuous paths.
Revised: Our first main results are the existence and uniqueness of solutions to a `generalised CIR initial-value problem' , which originates from the classical Cox-Ingersoll-Ross (CIR) processes, but in contrast prove well-defined without reference to a probability measure. This provides an unusual example of Skorokhod's representation theorem. In particular, we become able to generalise Heston's model of volatility to an arbitrary degree, by sampling drivers \omega under any probability measure; a rough one if so desired. Our main analysis relates to separable initial-value problems of type x'= \omega(x) + t - x + 1 , thereby also accommodating discontinuous paths.
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Original:  We then prove a certain `fast reversion'pathwise convergence of our solutions to a hitting-time limit on the (D,M) topologies of Skorokhod (1956) , thereby also accommodating discontinuous paths. In the classical Wiener setting, this limit is an inverse Gaussian \quad  L\'evy process.
Revised:  We then prove a certain `fast reversion'pathwise convergence of our solutions to a hitting-time limit on the (D,M) topologies of Skorokhod (1956) , \quad L\'evy process.
Edit intent: coherence
Original:  In the classical Wiener setting, this limit is an inverse Gaussian \quad  L\'evy process.
Revised:  In the classical Wiener setting, this limit is an inverse Gaussian \quad x(0)=0, with \omega only assumed continuous (not Lipschitz, nor H\"older), solutions of which \varphi correspond to time-averages of volatility trajectories \varphi'. Such solutions are shown to exist, be unique and bijective for any \omega, essentially placing no constraints on corresponding volatility trajectories, except for their non-negativity. After bounding these solutions in time, we prove a rare type of convergence result, towards c\`adl\`ag exit-time limits, on Skorokhod's M_1 topology. One immediate corollary of this limiting result is a weak connection between the time-averaged CIR process and the inverse-Gaussian L\'evy process.
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Original:  In the classical Wiener setting, this limit is an inverse Gaussian \quad  L\'evy process. By continuous mapping, analogous results follow for any functional, such as a derivative payoff or price, of our volatility paths. As an example, the classical Heston log-price limit is shown to be very closely related, but not equivalent, to the normal-inverse Gaussian L\'evy process. This exposes the origin of the marginal connection between these two processes revealed in Mechkov (2015) .
Revised:  In the classical Wiener setting, this limit is an inverse Gaussian \quad L\'evy subordinator .
Edit intent: clarity
Original: Motivated by successes of fast reverting volatility models, and the implicit dependence of `rough' processes on infinitesimal reversionary timescales, we establish a pathwise volatility framework which leads to a complete understanding of volatility trajectories' behaviour in the limit of infinitely-fast reversion. Towards this, we first establish processes that are weakly equivalent to Cox-Ingersoll-Ross (CIR) processes, but in contrast prove well-defined without reference to a probability measure. This provides an unusual example of Skorokhod's representation theorem. In particular, we become able to generalise Heston's model of volatility to an arbitrary degree, by sampling drivers \omega under any probability measure; a rough one if so desired. Our main analysisrelates to separable initial-value problems of type x'= \omega(x) + t - x + 1,%DIFDELCMD < \quad %%% x(0)=0, with \omega only assumed continuous (not Lipschitz, nor H\"older), solutions of which \varphi correspond to time-averages of volatility trajectories \varphi'.
Revised: %DIFDELCMD < \quad %%% x(0)=0, with \omega only assumed continuous (not Lipschitz, nor H\"older), solutions of which \varphi correspond to time-averages of volatility trajectories \varphi'.
Edit intent: meaning-changed
Original:  Our main analysisrelates to separable initial-value problems of type x'= \omega(x) + t - x + 1,%DIFDELCMD < \quad %%% x(0)=0, with \omega only assumed continuous (not Lipschitz, nor H\"older), solutions of which \varphi correspond to time-averages of volatility trajectories \varphi'. Such solutions are shown to exist, be unique and bijective for any \omega, essentially placing no constraints on corresponding volatility trajectories, except for their non-negativity. After bounding these solutions in time , we prove a rare type of convergence result, towards c\`adl\`ag exit-time limits, on Skorokhod' s M_1 topology.
Revised:  Our main analysisrelates to separable initial-value problems of type x'= \omega(x) + t - x + 1,%DIFDELCMD < \quad %%% This thesis develops a new framework for modelling price processes in time , we prove a rare type of convergence result, towards c\`adl\`ag exit-time limits, on Skorokhod' s M_1 topology.
Edit intent: meaning-changed
Original:  After bounding these solutions in time , we prove a rare type of convergence result, towards c\`adl\`ag exit-time limits, on Skorokhod' s M_1 topology.
Revised:  After bounding these solutions in finance, such as an equity price or foreign exchange rate. This can be related to the conventional Ito calculus-based framework through the time integral of a price's squared volatility. In the new framework, corresponding processes are strictly increasing, solve random ODEs, and are composed with geometric Brownian motion to obtain price processes. The new framework has no dependence on stochastic calculus, so processes can be studied on a pathwise basis using probability-free ODE techniques and functional analysis. The ODEs considered depend on continuous driving functions which are `spatially irregular' , we prove a rare type of convergence result, towards c\`adl\`ag exit-time limits, on Skorokhod' s M_1 topology.
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Original:  After bounding these solutions in time , we prove a rare type of convergence result, towards c\`adl\`ag exit-time limits, on Skorokhod' s M_1 topology. One immediate corollary of this limiting result is a weak connection between the time-averaged CIR process and  the inverse-Gaussian L\'evy subordinator .
Revised:  After bounding these solutions in time , meaning they need not have any spatial regularity properties such as Holder continuity. They are however strictly increasing in time, thus temporally asymmetric. When sensible initial values are chosen, IVP solutions are also strictly increasing, and the inverse-Gaussian L\'evy subordinator .
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Original:  One immediate corollary of this limiting result is a weak connection between the time-averaged CIR process and  the inverse-Gaussian L\'evy subordinator .
Revised:  One immediate corollary of this limiting result is a weak connection between the time-averaged CIR process and the IVPs' solution set is shown to contain all differentiable bijections on the inverse-Gaussian L\'evy subordinator .
Edit intent: meaning-changed
Original:  One immediate corollary of this limiting result is a weak connection between the time-averaged CIR process and  the inverse-Gaussian L\'evy subordinator .
Revised:  One immediate corollary of this limiting result is a weak connection between the time-averaged CIR process and the non-negative reals. This enables the modelling of any non-negative volatility path which is not zero over intervals, via the time derivative of solutions. Despite this generality, new well-posedness results establish the uniqueness of solutions going forwards in time, and continuity of the IVPs' solution map. Motivation to explore this framework comes from its connection with the Heston volatility model. The framework explains how Heston price processes can converge to an interval-valued generalisation of the NIG Levy process, and reveals a deeper relationship between integrated CIR processes and the IG Levy process. Within this framework, a `Riemann-Liouville-Heston' martingale model is defined which generalises these relationships to fractional counterparts. Implied volatilities from this model are simulated, and exhibit features characteristic of leading `rough' volatility models .
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Original:  For a given set of sets P_1,\ldots,P_L, a composable coreset independently computes subsets C_1\subset P_1, \ldots, C_L\subset P_L, such that i=1^L C_i contains an approximation of a measure of the set i=1^L P_i.
Revised:  For a given set of sets P_1,\ldots,P_L, a composable coreset independently computes subsets C_1\subset P_1, \ldots, C_L\subset P_L, such that i=1^L C_i contains an approximation of a measure of the set i=1^L P_i.
Edit intent: fluency
Original:  A few measures of information flow have been proposed previously in the literature, and measures of directed causal influence are currently being used as a heuristic proxy for information flow. However, there is as yet no rigorous treatment of the problem with formal definitions and clearly stated assumptions, and the process of defining information flow is often conflated with the problem of estimating it. In this work, we provide a new information-theoretic definition for information flow in a computational system, which we motivate using a series of examples.
Revised:  We are interested in a definition that captures the dynamic flow of information about a specific message, and which guarantees an unbroken "information path" between appropriately defined inputs and outputs in the directed graph. Prior measures, including those based on Granger Causality and Directed Information, fail to provide clear assumptions and guarantees about when they correctly reflect information flow about a message. We take a systematic approach---iterating through candidate definitions and counterexamples---to arrive at a definition for information flow in a computational system, which we motivate using a series of examples.
Edit intent: clarity
Original:  In this work, we provide a new information-theoretic definition for information flow in a computational system, which we motivate using a series of examples. We then show that this definition satisfies intuitively desirable properties, including the existence of "information paths", along which information flows from the input of the computational system to its output .
Revised:  In this work, we provide a new information-theoretic definition for information flow that is based on conditional mutual information, and which satisfies desirable properties, including the existence of "information paths", along which information flows from the input of the computational system to its output .
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Original:  In this paper, we investigate the extent to which FP and ES can differ, characterise tree shapes on which the indices are identical, and provide a new, shorter proof that FP and SV are identical on rooted trees .
Revised:  In this paper, we investigate the extent to which FP and ES can differ, characterise tree shapes on which the indices are identical, and study the equivalence of FP and SV are identical on rooted trees .
Edit intent: clarity
Original:  In this paper, we investigate the extent to which FP and ES can differ, characterise tree shapes on which the indices are identical, and provide a new, shorter proof that FP and SV are identical on rooted trees .
Revised:  In this paper, we investigate the extent to which FP and ES can differ, characterise tree shapes on which the indices are identical, and provide a new, shorter proof that FP and SV and its implications in more detail .
Edit intent: meaning-changed