One-step Memory Research Articles

Mine for life: Charting ownership effects in memory from adolescence to old age.

This study investigates the self-reference effect (SRE) with an ownership memory task across several age groups, providing the first age exploration of implicit ownership memory biases from adolescence to older adulthood (N = 159). Using a well-established ownership task, participants were required to sort images of grocery items as belonging to themselves or to a fictitious unnamed Other. After sorting and a brief distractor task, participants completed a surprise one-step source memory test. Overall, there was a robust SRE, with greater source memory accuracy for self-owned items. The SRE attenuated with age, such that the magnitude of difference between self and other memory diminished into older adulthood. Importantly, these findings were not due to a deterioration of memory for self-owned items, but rather an increase in memory performance for other-owned items. Linear mixed effects analyses showed self-biases in reaction times, such that self-owned items were identified more rapidly compared with other owned items. Again, age interacted with this effect showing that the responses of older adults were slowed, especially for other-owned items. Several theoretical implications were drawn from these findings, but we suggest that older adults may not experience ownership-related biases to the same degree as younger adults. Consequently, SREs through the lens of mere ownership may attenuate with age.

Fabrication of one-step shape memory gradient sound absorber with wrinkled inner wall and closed-pore structure

Plastic foam sound-absorbing material is of great significance to improve indoor sound environment. However, since the preparation of foamed material is fundamentally induced to form a homogeneous pore structure, the sound absorption frequency range is relatively narrow, limiting the sound absorption performance to a great extent. In order to improve the sound absorption performance in a wide frequency range, this work designed the combination of macro-porous gradient foam (MGF) and closed-pore resonator, introduced part of polyimide (PI) components based on the one-step free foaming method of polyurethane foam (PUF), and proposed a straightforward route to prepare basic polymer gradient foam. Through the arrangement and combination of different bubble holes, the sharp hole structure and the Helmholtz resonance structure were constructed by analogy. Importantly, the internal structure of the foam system was reorganized under the action of surface tension due to the inward collapse of the viscous bubbles during the process of bursting, resulting in the formation of wrinkled structures and small bubbles, which enhanced the resonant sound absorption at lower frequencies. In 200–6400 Hz, the MGF1 sample had the highest sound absorption capacity, with a cavity size range of 8.27–4.14 mm and the ability to absorb 90% of the acoustic wave above 1100 Hz. In addition, MGF samples also exhibited heat insulation behavior (0.047–0.061 W/(m·K)), shape memory, and energy absorption capacity. This gradient compound resonance structure satisfies the convenience of process manufacturing and high-efficiency sound absorption performance, which is essential for the wide application of plastic foam sound-absorbing materials.

Data-driven applications for wind energy analysis and prediction: The case of “La Haute Borne” wind farm

The current paper investigates several methods of predicting wind energy generation for the onshore “La Haute Borne” wind farm. The hybrid model has been developed to get short-term power forecasts using both historical in-situ measurements available from ENGIE and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) global reanalysis dataset.Meteorology is connected with chemistry because weather is the state of air, determined by pressure and temperature variations. This means that enriching historical measurements with weather data has a potential to get better predictions. It was shown that adding three extra meteorological parameters – pressure, humidity, and temperature – allowed to reach a higher accuracy compared with cases when weather parameters were completely ignored or used partially; this was proved by applying multivariate, one-step Long Short-Term Memory (LSTM) networks.Finally, the paper explains how to apply LSTM networks for the day-ahead forecasts. In particular, two state-of-the-art models were investigated – a base LSTM network and a more advanced method which combines convolutional and LSTM layers through the CNN-LSTM approach. Results showed the latter network reached higher accuracy for both 12- and 24-h forecasts and performed faster than an ordinary LSTM network. A significant advantage of both methods deals with their light structure which allows running models on the Central Processing Unit (CPU).

One-Step Memory Random Walk on Complex Networks: An Efficient Local Navigation Strategy

We propose one-step memory random walk on complex networks for which at each time step, the walker will not be allowed to revisit the last position. Mean first passage time is adopted to quantify its search efficiency and a procedure is provided for calculating it analytically. Interestingly, we find that in the same circumstance, one-step memory random walk usually takes less time than random walk for finding a target given in advance. Furthermore, this navigation strategy presents a better performance even in comparison with corresponding optimal biased random walk when moving on networks without small-world effect. Our findings are confirmed on two canonical network models and a number of real networks. Our work reveals that one-step memory random walk is an efficient local search strategy, which can be applied to transportation and information spreading.

Discrete-Time Quantum Walk with Memory on the Cayley Graph of the Dihedral Group

By adding one-step memory to enrich the model of discrete-time quantum walk on the Caylay graph of the dihedral group, the model of quantum walk with memory on the Cayley graph of the dihedral group is constructed. The Fourier Transform is used to analyze the walk, and a formula for probability distribution and time-average probability distribution is given. According to the one-to-one correspondence between quantum walk with memory on a regular graph and quantum walk without memory on the corresponding line digraph, the diagrammatic counterpart of quantum walk with one-step memory on the Cayley graph of the dihedral group is presented. Moreover, basic probabilistic properties of the proposed model are further studied using numerical simulation method. Furthermore, the similarities and differences are discussed in comparison to the memoryless model.

One-dimensional quantum walks with two-step memory

In this paper, we investigate one-dimensional quantum walks with two-step memory, which can be viewed as an extension of quantum walks with one-step memory. We develop a general formula for the amplitudes of the two-step memory walk with Hadamard coin by using path integral approach and numerically simulate its process. The simulation shows that the probability distribution of this new walk is different from that of the Hadamard quantum walk with one-step memory, while it presents some similarities with that of the normal Hadamard quantum walk without memory.

Correlations and transport in exclusion processes with general finite memory

We consider the correlations and the hydrodynamic description of persistent random walkers on a lattice. We derive a drift-diffusion equation and identify a memory-dependent critical density. Above the critical density, the effective diffusion coefficient decreases with the particles’ propensity to move forward and below the critical density it increases with their propensity to move forward. If the correlations are neglected the critical density is exactly 1/2. We also derive a low-density approximation for the same time correlations between different sites. We perform simulations on a one-dimensional system with one-step memory and find good agreement between our analytical derivation and the numerical results. We also consider the previously unexplored special case of totally anti-persistent particles. Generally, the correlation length converges to a finite value. However in the special case of totally anti-persistent particles and density 1/2, the correlation length diverges with time. Furthermore, connecting a system of totally anti-persistent particles to external particle reservoirs creates a discontinuity in the density between the bulk and the reservoir. We find a qualitative description of this phenomenon which agrees reasonably well with the numerics.

Transport in exclusion processes with one-step memory: density dependence and optimal acceleration

We study a lattice gas of persistent walkers, in which each site is occupied by at most one particle and the direction each particle attempts to move to depends on its last step. We analyse the mean squared displacement (MSD) of the particles as a function of the particle density and their persistence (the tendency to continue moving in the same direction). For positive persistence the MSD behaves as expected: it increases with the persistence and decreases with the density. However, for strong anti-persistence we find two different regimes, in which the dependence of the MSD on the density is non-monotonic. For very strong anti-persistence there is an optimal density at which the MSD reaches a maximum. In an intermediate regime, the MSD as a function of the density exhibits both a minimum and a maximum, a phenomenon which has not been observed before. We derive a mean-field theory which qualitatively explains this behaviour.

When multiplicative noise stymies control

We consider the stabilization of an unstable discrete-time linear system that is observed over a channel corrupted by continuous multiplicative noise. Our main result shows that if the system growth is large enough, then the system cannot be stabilized. This is done by showing that the probability that the state magnitude remains bounded must go to zero with time. Our proof technique recursively bounds the conditional density of the system state to bound the progress the controller can make. This sidesteps the difficulty encountered in using the standard data-rate theorem style approach; that approach does not work because the mutual information per round between the system state and the observation is potentially unbounded. It was known that a system with multiplicative observation noise can be stabilized using a simple memoryless linear strategy if the system growth is suitably bounded. The second main result in this paper shows that while memory cannot improve the performance of a linear scheme, a simple nonlinear scheme that uses one-step memory can do better than the best linear scheme.

Preferential learning and memory resolve social dilemma

Cooperation is widespread in society, thus how to explain this phenomenon has become one open question. According to empirical experience, preferential learning and memory seem to be two effective ways to this issue, which, however, still needs validation in scientific research. Motivate by this point, we consider one-step memory and preference learning (i.e. learning the strategy of subject performing best, which is tuned by a preferential parameter α) in prisoner's dilemma game. α=0 enables the model going back to control treatment where objects randomly selected. While for α > 0, individuals prefer objects that perform better. Compared with control treatment, we find that increasing preferential parameter α can promote cooperative behavior monotonously. In particular, the larger the value of α, the stronger and more compact clusters they can form. Finally, in order to investigate the robustness of this mechanism, we also study the evolution of cooperation in small-world network and random regular network.

Evolutionary fate of memory-one strategies in repeated prisoner’s dilemma game in structured populations

We study evolutionary spatial prisoner’s dilemma game involving a one-step memory mechanism of the individuals whenever making strategy updating. In particular, during the process of strategy updating, each individual keeps in mind all the outcome of the action pairs adopted by himself and each of his neighbors in the last interaction, and according to which the individuals decide what actions they will take in the next round. Computer simulation results imply that win-stay-lose-shift like strategy win out of the memory-one strategy set in the stationary state. This result is robust in a large range of the payoff parameter, and does not depend on the initial state of the system. Furthermore, theoretical analysis with mean field and quasi-static approximation predict the same result. Thus, our studies suggest that win-stay-lose-shift like strategy is a stable dominant strategy in repeated prisoner’s dilemma game in homogeneous structured populations.

SEMI-MARKOV MODELS FOR SEISMIC HAZARD ASSESSMENT IN CERTAIN AREAS OF GREECE

The long-term probabilistic seismic hazard is studied through the application of semi-Markov model. In this model a sequence of earthquakes is considered as a Markov process and the waiting time distributions depend only on the type of the last and the next event. The principal hypothesis of the model is the property of one-step memory, according to which the probability of moving to any future state depends only on the present state. The model under consideration defines a continuous-time, discrete-state stationary process in which successive state occupancies are governed by the transition probabilities of the Markov process. The space of states is considered to be finite and the process started far in the past has achieved stationarity. Firstly, a non-parametric method is applied in order to determine the waiting times. Then, the waiting times derived by means of the exponential and Weibull distributions will be compared to each other, as well as with the actual waiting times. Thus, the probability of occurrence of the anticipated earthquakes of a specific magnitude scale is calculated. The models are applied to an historical catalogue for Northern Aegean Sea.

Winning in sequential Parrondo games by players with short-term memory

The original Parrondo game, denoted as AB3, contains two independent games: A and B. The winning or losing of games A and B is defined by the change of one unit of capital. Game A is a losing game if played continuously, with winning probability , where . Game B is also losing and has two coins: a good coin with winning probability is used if the player’s capital is not divisible by 3, otherwise a bad coin with winning probability is used. The Parrondo paradox refers to the situation where the mixture of games A and B in a sequence leads to winning in the long run. The paradox can be resolved using Markov chain analysis. We extend this setting of the Parrondo game to involve players with one-step memory. The player can win by switching his choice of A or B game in a Parrondo game sequence. If the player knows the identity of the game he plays and the state of his capital, then the player can win maximally. On the other hand, if the player does not know the nature of the game, then he is playing a (C, D) game, where either (C = A, D = B), or (C = B, D = A). For a player with one-step memory playing the AB3 game, he can achieve the highest expected gain with switching probability equal to 3/4 in the (C, D) game sequence. This result has been found first numerically and then proven analytically. Generalization to an AB mod(M) Parrondo game for other integers M has been made for the general domain of parameters . We find that for odd M the Parrondo effect does exist. However, for even M, there is no Parrondo effect for two cases: the initial game is A and the initial capital is even, or the initial game is B and the initial capital is odd. There is still a possibility of the Parrondo effect for the other two cases when M is even: the initial game is A and the initial capital is odd, or the initial game is B and the initial capital is even. These observations from numerical experiments can be understood as the factorization of the Markov chains into two distinct cycles. Discussion of these effects on games is also made in the context of feedback control of the Brownian ratchet.

Repeated Sequential Prisoner's Dilemma: The Stackleberg Variant

We study the Stackleberg variant of the repeated Sequential Prisoner's Dilemma (SPD). The game goes in two stages, and the two players, the leader and the follower, are asymmetric in both stages. In the first stage of the game, the leader chooses a strategy (for the repeated SPD of the second stage), which is immediately known to the follower. In the second stage, they play repeated SPD: In each round the follower moves after observing the leader's action. Assuming complete rationality, we find some extraordinary properties of this model. (i) The (subgame perfect) equilibrium payoff profile is unique, which lies on the corner of the region predicted by classical folk theorems: It is best for the leader and at the same time worst for the follower, (ii) the leader has simple optimal strategies that are one-step memory and stationary. These features are in great contrast with classical results, where either uniqueness cannot be guaranteed and equilibrium strategies are often quite complicated, or bounded rationality is required. Although full cooperation, i.e., the outcome is always (cooperate, cooperate), is not attainable in our model, at least a half of the optimal social welfare can be guaranteed. We also do a non-equilibrium analysis which makes the usual equilibrium analysis more convincing.

Strategies generalization and payoff fluctuation optimization in the iterated ultimatum game

An iterated version of ultimatum game, based on generalized probabilistic strategies, which are mathematically modeled by accepting proposal functions is presented. These strategies account for the behavior of the players by mixing levels of altruism and greed. We obtained analytically the moments of the payoff of the players under such a generalization. Our analysis is divided into two cases: (i) no memory players, where players do not remember previous decisions, and (ii) one-step memory players, where the offers depend on players’ last decision. We start considering the former case. We show that when the combination of the proposer’s altruism and responder’s greed levels balances the proposer’s greedy and responder’s altruism levels, the average and variance of the payoff of both players are the same. Our analysis is carried out considering that the acceptance of an offer depends on: (a) a fixed probability p or (b) the value offered. The combination of cases (i) and (a) shows that there exists a p value that maximizes the cumulative gain after n iterations. Moreover, we show n×p diagrams with ïso-average” and ïso-variance” of the cumulative payoff. Our analytical results are validated by Monte Carlo simulations. For the latter case, we show that when players have no memory (i), there are cutoff values, which the variance of the proposer’s cumulative payoff presents local maximum and minimum values, while for the responder, the same amount presents a global maximum. Case (b) combined with one-step memory players (ii), we verified, via MC simulations that, for the same number of iterations, the responder obtains different cumulative payoffs by setting different cutoff values. This result composes an interesting pattern of stripes in the cutoff per n diagrams. Simultaneously, looking at variance of this amount, for the responder player in a similar diagram, we observe regions of iso-variance in non trivial patterns which depend on initial value of the proposal. Our contributions detailed by analytical and MC simulations are useful to design new experiments in the ultimatum game in stochastic scenarios.

Repeated Sequential Prisoner's Dilemma: The Stackleberg Variant

We study the Stackleberg variant of the repeated Sequential Prisoner’s Dilemma (SPD for short). The game goes in two stages, and the two players, the leader and the follower, are asymmetric in both stages. In the first stage of the game, the leader chooses a strategy (for the repeated SPD of the second stage), which is immediately known to the follower. In the second stage, they play repeated SPD: in each round the follower moves after observing the leader’s action. Assuming complete rationality, we find some extraordinary properties of this model. (i) The (sub-game perfect) equilibrium payoff profile is unique, which lies on the corner of the region predicted by classical folk theorems: it is best for the leader and at the same time worst for the follower. (ii) The leader has simple optimal strategies that are one-step memory and stationary. These features are in great contrast with classical results, where either uniqueness cannot be guaranteed and equilibrium strategies are often quite complicated, or bounded rationality is required. Although full cooperation, i.e. the outcome is always (cooperate, cooperate), is not attainable in our model, at least a half of the optimal social welfare can be guaranteed. We also do a non-equilibrium analysis which makes the usual equilibrium analysis more convincing.

Memory does not necessarily promote cooperation in dilemma games

Evolutionary games can model dilemmas for which cooperation can exist in rational populations. According to intuition, memory of the history can help individuals to overcome the dilemma and increase cooperation. However, here we show that no such general predictions can be made for dilemma games with memory. Agents play repeated prisoner’s dilemma, snowdrift, or stag hunt games in well-mixed populations or on a lattice. We compare the cooperation ratio and fitness for systems with or without memory. An interesting result is that cooperation is demoted in snowdrift and stag hunt games with memory when cost-to-benefit ratio is low, while system fitness still increases with memory in the snowdrift game. To illustrate this interesting phenomenon, two further experiments were performed to study R, ST, and P reciprocity and investigate 16 agent strategies for one-step memory. The results show that memory plays different roles in different dilemma games.

Criticality in conserved dynamical systems: Experimental observation vs. exact properties

Conserved dynamical systems are generally considered to be critical. We study a class of critical routing models, equivalent to random maps, which can be solved rigorously in the thermodynamic limit. The information flow is conserved for these routing models and governed by cyclic attractors. We consider two classes of information flow, Markovian routing without memory and vertex routing involving a one-step routing memory. Investigating the respective cycle length distributions for complete graphs, we find log corrections to power-law scaling for the mean cycle length, as a function of the number of vertices, and a sub-polynomial growth for the overall number of cycles. When observing experimentally a real-world dynamical system one normally samples stochastically its phase space. The number and the length of the attractors are then weighted by the size of their respective basins of attraction. This situation is equivalent, for theory studies, to "on the fly" generation of the dynamical transition probabilities. For the case of vertex routing models, we find in this case power law scaling for the weighted average length of attractors, for both conserved routing models. These results show that the critical dynamical systems are generically not scale-invariant but may show power-law scaling when sampled stochastically. It is hence important to distinguish between intrinsic properties of a critical dynamical system and its behavior that one would observe when randomly probing its phase space.

Adaptive Observer-Based Synchronization of Chaotic Systems With First-Order Coder in the Presence of Information Constraints

We analyze the performance of an adaptive chaotic synchronization system under information constraints assuming that some system parameters are unknown and only the system output is measured. Such a problem was studied previously in the absence of information constraints based on an adaptive observer scheme, allowing for its use in message transmission systems. We provide analytical bounds for the closed-loop system performance (asymptotic synchronization error) and conduct a numerical case study for a typical chaotic system, namely the Chua circuit, in the presence of information constraints. It is shown that the time-varying quantizer with one-step memory provides a reasonable approximation of the minimum transmission rate for adaptive state estimation.

Chaotic observer-based synchronization under information constraints

Limitations of observer-based synchronization systems under information constraints (limited information capacity of the coupling channel) are evaluated. We give theoretical analysis for multidimensional drive-response systems represented in the Lurie form (linear part plus nonlinearity depending only on measurable outputs). It is shown that the upper bound of the limit synchronization error (LSE) is proportional to the upper bound of the transmission error. As a consequence, the upper and lower bounds of LSE are proportional to the maximum rate of the coupling signal and inversely proportional to the information transmission rate (channel capacity). Optimality of the binary coding for coders with one-step memory is established. The results are applied to synchronization of two chaotic Chua systems coupled via a channel with limited capacity.