Jarzyski’s Equality and Crooks’ Fluctuation Theorem for General Markov Chains with Application to Decision-Making Systems

We define common thermodynamic concepts purely within the framework of general Markov chains and derive Jarzynski’s equality and Crooks’ fluctuation theorem in this setup. In particular, we regard the discrete-time case, which leads to an asymmetry in the definition of work that appears in the usual formulation of Crooks’ fluctuation theorem. We show how this asymmetry can be avoided with an additional condition regarding the energy protocol. The general formulation in terms of Markov chains allows transferring the results to other application areas outside of physics. Here, we discuss how this framework can be applied in the context of decision-making. This involves the definition of the relevant quantities, the assumptions that need to be made for the different fluctuation theorems to hold, as well as the consideration of discrete trajectories instead of the continuous trajectories, which are relevant in physics.     

Crooks Fluctuation Theorem for Single Polymer Dynamics in Time-Dependent Flows: Understanding Viscoelastic Hysteresis

Nonequilibrium work relations have fundamentally advanced our understanding of molecular processes. In recent years, fluctuation theorems have been extensively applied to understand transitions between equilibrium steady-states, commonly described by simple control parameters such as molecular extension of a protein or polymer chain stretched by an external force in a quiescent fluid. Despite recent progress, far less is understood regarding the application of fluctuation theorems to processes involving nonequilibrium steady-states such as those described by polymer stretching dynamics in nonequilibrium fluid flows. In this work, we apply the Crooks fluctuation theorem to understand the nonequilibrium thermodynamics of dilute polymer solutions in flow. We directly determine the nonequilibrium free energy for single polymer molecules in flow using a combination of single molecule experiments and Brownian dynamics simulations. We further develop a time-dependent extensional flow protocol that allows for probing viscoelastic hysteresis over a wide range of flow strengths. Using this framework, we define quantities that uniquely characterize the coil-stretch transition for polymer chains in flow. Overall, generalized fluctuation theorems provide a powerful framework to understand polymer dynamics under far-from-equilibrium conditions.     

Hamiltonian Derivation of a Detailed Fluctuation Theorem

We analyze the microscopic evolution of a system undergoing a far-from-equilibrium thermodynamic process. Explicitly accounting for the degrees of freedom of participating heat reservoirs, we derive a hybrid result, similar in form to both the fluctuation theorem and a statement of detailed balance. We relate this result to the steady-state fluctuation theorem and to a free energy relation valid far from equilibrium.     

Fluctuation Analysis of Decoy State QKD with Finite Data-Set Size

Decoy state method quantum key distribution （QKD） is one of the promising practical solutions for BB84 QKD with coherent light pulses. The number of data-set size in practical QKD protocol is always finite, which will cause statistical fluctuations. In this paper, we apply absolutely statistical fluctuation to amend the yield and error rate of the quantum state. The relationship between exchanged number of quantum signals and key generation rate is analyzed in our simulation, which offers a useful reference for experiment.     

Fluctuation Analysis of Decoy State QKD with Finite Data-Set Size

Decoy state method quantum key distribution (QKD) is one of the promisingpractical solutions for BB84 QKD with coherent light pulses. The number ofdata-set size in practical QKD protocol is always finite, which will causestatistical fluctuations. In this paper, we apply absolutely statisticalfluctuation to amend the yield and error rate of the quantum state. Therelationship between exchanged number of quantum signals and key generation rate is analyzed in our simulation, which offers a useful reference for experiment.     

Generalized Structure Functions and Multifractal Detrended Fluctuation Analysis Applied to Vegetation Index Time Series: An Arid Rangeland Study

Estimates suggest that more than 70% of the world’s rangelands are degraded. The Normalized Difference Vegetation Index (NDVI) is commonly used by ecologists and agriculturalists to monitor vegetation and contribute to more sustainable rangeland management. This paper aims to explore the scaling character of NDVI and NDVI anomaly (NDVIa) time series by applying three fractal analyses: generalized structure function (GSF), multifractal detrended fluctuation analysis (MF-DFA), and Hurst index (HI). The study was conducted in four study areas in Southeastern Spain. Results suggest a multifractal character influenced by different land uses and spatial diversity. MF-DFA indicated an antipersistent character in study areas, while GSF and HI results indicated a persistent character. Different behaviors of generalized Hurst and scaling exponents were found between herbaceous and tree dominated areas. MF-DFA and surrogate and shuffle series allow us to study multifractal sources, reflecting the importance of long-range correlations in these areas. Two types of long-range correlation appear to be in place due to short-term memory reflecting seasonality and longer-term memory based on a time scale of a year or longer. The comparison of these series also provides us with a differentiating profile to distinguish among our four study areas that can improve land use and risk management in arid rangelands.     

On the Validity of Detrended Fluctuation Analysis at Short Scales

Detrended Fluctuation Analysis (DFA) has become a standard method to quantify the correlations and scaling properties of real-world complex time series. For a given scale ℓ of observation, DFA provides the function F(ℓ), which quantifies the fluctuations of the time series around the local trend, which is substracted (detrended). If the time series exhibits scaling properties, then F(ℓ)∼ℓα asymptotically, and the scaling exponent α is typically estimated as the slope of a linear fitting in the logF(ℓ) vs. log(ℓ) plot. In this way, α measures the strength of the correlations and characterizes the underlying dynamical system. However, in many cases, and especially in a physiological time series, the scaling behavior is different at short and long scales, resulting in logF(ℓ) vs. log(ℓ) plots with two different slopes, α1 at short scales and α2 at large scales of observation. These two exponents are usually associated with the existence of different mechanisms that work at distinct time scales acting on the underlying dynamical system. Here, however, and since the power-law behavior of F(ℓ) is asymptotic, we question the use of α1 to characterize the correlations at short scales. To this end, we show first that, even for artificial time series with perfect scaling, i.e., with a single exponent α valid for all scales, DFA provides an α1 value that systematically overestimates the true exponent α. In addition, second, when artificial time series with two different scaling exponents at short and large scales are considered, the α1 value provided by DFA not only can severely underestimate or overestimate the true short-scale exponent, but also depends on the value of the large scale exponent. This behavior should prevent the use of α1 to describe the scaling properties at short scales: if DFA is used in two time series with the same scaling behavior at short scales but very different scaling properties at large scales, very different values of α1 will be obtained, although the short scale properties are identical. These artifacts may lead to wrong interpretations when analyzing real-world time series: on the one hand, for time series with truly perfect scaling, the spurious value of α1 could lead to wrongly thinking that there exists some specific mechanism acting only at short time scales in the dynamical system. On the other hand, for time series with true different scaling at short and large scales, the incorrect α1 value would not characterize properly the short scale behavior of the dynamical system.     

Crossover Phenomena in Detrended Fluctuation Analysis Used in Financial Markets

A systematic analysis of Shanghai and Japan stock indices for the period of Jan. 1984 to Dec. 2005 is performed. After stationarity is verified by ADF （Augmented Dickey-Fuller） test, the power spectrum of the data exhibits a power law decay as a whole characterized by 1/f^β processes with possible long range correlations. Subsequently, by using the method of detrended fluctuation analysis （DFA） of the general volatility in the stock markets, we find that the long-range correlations are occurred among the return series and the crossover phenomena exhibit in the results obviously.Further, Shanghai stock market shows long-range correlations in short time scale and shows short-range correlations in long time scale. Whereas, for Japan stock market, the data behaves oppositely absolutely. Last, we compare the varying of scale exponent in large volatility between two stock markets. All results obtained may indicate the possibility of characteristic of multifractal scaling behavior of the financial markets.     

Multifractal Detrended Fluctuation Analysis of Interevent Time Series in a Modified OFC Model

We use multifractal detrended fluctuation analysis(MF-DFA) method to investigate the multifractal behavior of the interevent time series in a modified Olami-Feder-Christensen(OFC) earthquake model on assortative scale-free networks.We determine generalized Hurst exponent and singularity spectrum and find that these fluctuations have multifractal nature.Comparing the MF-DFA results for the original interevent time series with those for shuffled and surrogate series,we conclude that the origin of multifractality is due to both the broadness of probability density function and long-range correlation.     

Detrended Fluctuation Analysis on Correlations of Complex Networks Under Attack and Repair Strategy

We analyze the correlation properties of the Erdos-Rényi random graph (RG) and the Barabási-Albert scale-free network (SF) under the attack and repair strategy with detrended fluctuation analysis (DFA). The maximum degree k representing the local property of the system, shows similar scaling behaviors for random graphs and scale-free networks. The fluctuations are quite random at short time scales but display strong anticorrelation at longer time scales under the same system size N and different repair probability pre. The average degree , revealing the statistical property of the system, exhibits completely different scaling behaviors for random graphs and scale-free networks. Random graphs display long-range power-law correlations. Scale-free networks are uncorrelated at short time scales; while anticorrelated at longer time scales and the anticorrelation becoming stronger with the increase of pre.     

Nonzero Temperature Squeezing of the Time—Dependent Harmonic Oscillator and the Applications to the Capacitive Coupled Electric Circuit

A new way to calculate the nonzero temperature quantum fluctuations of the time-dependent harmonic oscillator is proposed and the properties of squeezing are exactly given.The method is applied to the capacitive coupled electric circuit.It is explicitly shown that squeezing can appear and the squeezing parameters are related to the physical quantities of the coupled circuit.     

Electron–Phonon Interaction and Quantum Fluctuations in the Time-Dependent Damped Capacitance-Coupled Electric Circuit

Starting from the electron–phonon interaction, the time-dependent capacitance-coupled electric circuit is quantized. Quantum fluctuations derived by this method are different from former ones.     

Quantum Fluctuation of a Mesoscopic Inductance Coupling Circuit at Finite Temperature

We study the quantization of mesoscopic inductance coupling circuit and discuss its time evolution. Bymeans of the thermal field dynamics theory we study the quantum fluctuation of the system at finite temperature.     

宽脉冲中子注入下中子裂变链统计涨落的数值计算

研究了裂变系统在宽脉冲外中子源注入驱动下裂变链统计涨落问题.提出了概率分布W_n方程的分解-合成数值模拟计算方法,使裂变系统注入中子源数值模拟计算扩展到一般广义条件(具有任意时间分布、注入时间零点t₀概率分布函数P(t₀)和数概率分布S(n)等).同时讨论了应用物理模型的等效条件.将基本前提等效条件从单介质区模型扩展到多介质区模型.为解概率分布W_n方程必备参数提供了计算平均公式.     

The Fundamentals of Particle Size Analysis by Transmission Fluctuation Spectrometry.

A transmission signal measured on a flowing suspension of particles with a high spatial and temporal resolution shows significant fluctuations, which contain the complete information on particle size distribution and particle concentration. In Parts 1 and 2, the basic properties of signal fluctuations were studied for temporal averaging by a gliding time window and for spatial averaging by a circular beam of uniform intensity. However, the experimental implementation of such conditions is difficult. Now, the theory is extended for Gaussian beams of variable diameter and averaging by signal filtering in the frequency domain. This provides the basis for an experimental implementation by transmission of a laser beam and analog signal processing by an array of low pass filters.     

Influence of Signal and Noise on Statistical Fluctuation of Single-Mode Laser System

On the basis of calculating the steady-state mean normalized intensity fluctuation of a signal-mode laser system driven by both colored pump noise with signal modulation and the quantum noise with cross-correlation between its real and imaginary parts, we analyze the influence of modulation signal, noise, and its correlation form on the statistical fluctuation of the laser system. We have found that when the amplitude of modulation signal weakens and its frequency quickens, the statistical fluctuation will reduce rapidly. The statistical fluctuation of the laser system can be restrained by reducing the intensity of pump noise and quantum noise. Moreover, with prolonging of colored cross-correlation time, the statistical fluctuation of laser system experiences a repeated changing process, that is, from decreasing to augmenting,then to decreasing, and finally to augmenting again. With the decreasing of the value of cross-correlation coefficient, the statistical fluctuation will decrease too. When the cross-correlation form between the real part and imaginary part of quantum noise is zero correlation, the statistical fluctuation of laser system has a minimum. Compared with the influence of intensity of pump noise, the influence of intensity of quantum noise on the statistical fluctuation is smaller.     

Influence of Signal and Noise on Statistical Fluctuation of Single-Mode Laser System

On the basis of calculating the steady-state mean normalized intensity fluctuation of a signal-mode laser system driven by both colored pump noise with signal modulation and the quantum noise with cross-correlation between its real and imaginary parts, we analyze the influence of modulation signal, noise, and its correlation form on the statistical fluctuation of the laser system. We have found that when the amplitude of modulation signal weakens and its frequency quickens, the statistical fluctuation will reduce rapidly. The by reducing the intensity of pump noise and quantum noise. statistical fluctuation of the laser system can be restrained Moreover, with prolonging of colored cross-correlation time, the statistical fluctuation of laser system experiences a repeated changing process, that is, from decreasing to augmenting, then to decreasing, and finally to augmenting again. With the decreasing of the value of cross-correlation coe~cient, the statistical fluctuation will decrease too. When the cross-correlation form between the real part and imaginary part of quantum noise is zero correlation, the statistical fluctuation of laser system has a minimum. Compared with the influence of intensity of pump noise, the influence of intensity of quantum noise on the statistical fluctuation is smaller.     

An Analysis of Stapp’s “A Bell-Type Theorem without Hidden Variables”

H.P. Stapp has proposed a number of demonstrations of a Bell-type theorem which dispensed with an assumption of hidden variables, but relied only upon locality together with an assumption that experimenters can choose freely which of several incompatible observables to measure. In recent papers his strategy has centered upon counterfactual conditionals. Stapp’s paper in American Journal of Physics, 2004, replies to objections raised against earlier expositions of this strategy and proposes a simplified demonstration. The new demonstration is criticized, several subtleties in the logic of counterfactuals are pointed out, and the proofs of J.S. Bell and his followers are advocated.Dedicated to the memory of my dear friend Asher Peres.     

Talbot Images of Two-Dimensional Gaussian Beam Arrays with Amplitude Fluctuation

Talbot images constructed in the Fresnel diffraction region due to two-dimensional Gaussian beam arrays with amplitude fluctuation are statistically investigated using a mathematical model based on the propagation law of a Gaussian beam. The average intensity distribution of Talbot images is analyzed theoretically, and the corresponding simulation is conducted by computer. The relative fluctuation of the peak intensity to the mean with respect to Talbot images is found to be less than that of the source by approximately 1/4. Furthermore, the effect of the finite size of source arrays on the formation of Talbot images is revealed throughout the simulation as well as the effect of the amplitude fluctuation.     

Single-molecule experiments in biophysics: Exploring the thermal behavior of nonequilibrium small systems

Biomolecules carry out very specialized tasks inside the cell where energies involved are few tens ofκ _B T, small enough for thermal fluctuations to be relevant in many biomolecular processes. In this paper I discuss a few concepts and present some experimental results that show how the study of fluctuation theorems applied to biomolecules contributes to our understanding of the nonequilibrium thermal behavior of small systems.