Long Memory Time Research Articles

Photo-induced dynamics with continuous and discrete quantum baths.

The ultrafast quantum dynamics of photophysical processes in complex molecules is an extremely challenging computational problem with a broad variety of fascinating applications in quantum chemistry and biology. Inspired by recent developments in open quantum systems, we introduce a pure-state unraveled hybrid-bath method that describes a continuous environment via a set of discrete, effective bosonic degrees of freedom using a Markovian embedding. Our method is capable of describing both, a continuous spectral density and sharp peaks embedded into it. Thereby, we overcome the limitations of previous methods, which either capture long-time memory effects using the unitary dynamics of a set of discrete vibrational modes or use memoryless Markovian environments employing a Lindblad or Redfield master equation. We benchmark our method against two paradigmatic problems from quantum chemistry and biology. We demonstrate that compared to unitary descriptions, a significantly smaller number of bosonic modes suffices to describe the excitonic dynamics accurately, yielding a computational speed-up of nearly an order of magnitude. Furthermore, we take into account explicitly the effect of a δ-peak in the spectral density of a light-harvesting complex, demonstrating the strong impact of the long-time memory of the environment on the dynamics.

Stability analysis of fractional difference equations with delay.

Long-term memory is a feature observed in systems ranging from neural networks to epidemiological models. The memory in such systems is usually modeled by the time delay. Furthermore, the nonlocal operators, such as the "fractional order difference," can also have a long-time memory. Therefore, the fractional difference equations with delay are an appropriate model in a range of systems. Even so, there are not many detailed studies available related to the stability analysis of fractional order systems with delay. In this work, we derive the stability conditions for linear fractional difference equations with an arbitrary delay τ and even for systems with distributed delay. We carry out a detailed stability analysis for the cases of single delay with τ=1 and τ=2. The results are extended to nonlinear maps. The formalism can be easily extended to multiple time delays.

The Therapeutic Effects of Noninvasive Brain Stimulation Combined with Cognitive Training in Elders with Alzheimer's Disease or Amnesic Mild Cognitive Impairment.

Recent studies have indicated that noninvasive brain stimulation combined with cognitive interval (NIBS-CI) improved cognitive function in people with Alzheimer's disease (AD) or Amnesic mild cognitive impairment (a-MCI). While previous interventions have demonstrated that a single targeted cognitive intervention can improve cognitive function, the outcomes of using both interventions simultaneously are less well-established. Therefore, this study aims to perform a meta-analysis to determine the effectiveness of NIBS-CI in treating cognitive impairment associated with AD and a-MCI, with the goal of obtaining novel insights into this combined intervention. PubMed, Web of Science, ProQuest and Central Cochrane library databases were searched up to December 2022. The primary cognitive outcomes were extracted from the included article. A mean difference (MD) and standardized mean difference (SMD) with a 95% confidence interval were calculated by using random-effect models. Twelve studies with a total of 587 AD patients were included. The findings demonstrated that NIBS-CI significantly improved cognitive function of AD patients in cognitive outcomes (SMD = -0.52, 95%CI (-0. 93, -0.11)) and ADAS-COG (MD = -1.16, 95%CI (-1.69, -0.63)). The pooled results showed that NIBS-CI did not improve cognitive function of AD patients in short-time memory (SMD = 0.057, 95%CI (-0.13, 0.25), P = 0.56) and long-time memory (SMD = 0.001, 95%CI (-0.20, 0.20), P = 0.99). There is evidence for a positive effect of NIBS-CI on overall cognitive function of AD and a-MCI. Considering the limited sample size, it is important to interpret the findings related to memory with caution. To obtain more robust results, future studies should be conducted with larger sample sizes and incorporate objective neurophysiological and neuroimaging tools. These methodological enhancements will allow for a better understanding of the therapeutic targets and provide a more comprehensive assessment of the effects of NIBS-CI treatment.

Research on the Construction of Intelligent Robot Path Recognition System Supported by Deep Learning Network Algorithm

Abstract In this paper, a new intelligent robot path recognition system LADDPG, is constructed by extending the DDPG algorithm with the support of the deep learning network algorithm. The path recognition system is optimized by using the memory system and the decision module guided by the attention mechanism, which solves the problems of the existing DRL intelligent robot path recognition method that cannot store long-time memory and the training time is too long. The system in this paper is validated by conducting path recognition experiments under different road conditions and conducting dynamic obstacle avoidance test experiments. The system can maintain excellent noise immunity and a short path recognition time of 51 ms under the conditions of missing road conditions, circuitous road conditions and large curvature, and in the presence of multiple dynamic obstacles, the system can maintain a collision rate close to 0 while maintaining a very high success rate of 0.98, and the required path recognition time is much lower than that of other methods, with an average reward value of 0.4151. This paper’s system is highly accurate in recognizing intelligent robot paths and has high application value and capability.

Research on Innovative Teaching Path of English Literature in Colleges and Universities Based on Multimodal Discourse Analysis

Abstract English literature teaching can improve students’ literary literacy and English proficiency. In this paper, we preprocess the English literature teaching data in colleges and universities from the two modalities of visual information and auditory information, extract the features of auditory modality, text modality, and video modality of the classroom respectively, and represent the features using bidirectional long-time memory neural network. A multilayer attention network mechanism is introduced to complete multimodal fusion in English literature teaching and label multimodal features after the extraction is done. On this basis, a student-centered innovative teaching path for English literature in colleges and universities is constructed, and teaching practice and multimodal discourse analysis are carried out to explore its teaching effect. The results show that the length of time used for facial smiles in inefficient classrooms (780.52/s) is significantly more than that in inefficient classrooms (150.22/s), and the length of time used for eye interactions (832.63/s) is significantly more than that in inefficient classrooms (720.44/s), and the length of time used for animations (450.42/s) is significantly more than that in inefficient classrooms (128.88/s), and efficient classrooms are more emphasized on student interactions. Students’ reading and writing abilities were significantly different from those before the practice (P<0.05). This study suggests that promoting innovation in teaching English literature in colleges and universities can have a positive practical significance.

The existence and asymptotic behavior of solutions to 3D viscous primitive equations with Caputo fractional time derivatives

On the one hand, the primitive three-dimensional viscous equations for large-scale ocean and atmosphere dynamics are commonly used in weather and climate predictions. On the other hand, ever since the middle of the last century, it has been widely recognized that the climate variability exhibits long-time memory. In this paper, we first prove the global existence of weak solutions to the primitive equations of large-scale ocean and atmosphere dynamics with Caputo fractional time derivatives. Then we establish the existence of an absorbing set, which is positively invariant. Finally, an attractor (strictly speaking, the minimal attracting set containing all the limiting dynamics) is constructed for the time fractional primitive equations, which means that the present state of a system may have long-time influences on the states in far future. However, there was no work on the long-time behavior of the time fractional primitive equations and we fill this gap in this paper.

Load prediction of integrated energy systems for energy saving and carbon emission based on novel multi-scale fusion convolutional neural network

The integrated energy system plays an important role in the energy conservation, emission reduction and the resource-efficient utilization. Accurate load forecasting is a significant basis for the optimal scheduling of the integrated energy system. The integrated energy system has coupling interaction between different energy sources in production, distribution and storage. However, the traditional method cannot effectively extract multi-scale features and utilize the coupling information between the multivariate energy sources. In this paper, a novel multi-scale fusion convolutional neural network integrating the bi-directional long short-term memory network and multi-domains hierarchical decoding is proposed to extract and analyze multivariate load data coupling in the integrated energy system data. The multi-scale fusion convolutional neural network is constructed by the multi-dimension convolution layer to obtain multi-scale feature of the integrated energy system data. Meanwhile, the bi-directional long short-term memory network is applied to extract the time dependencies of the integrated energy system data. Finally, the multi-domains hierarchical decoding extracts the coupling characteristics of different domains to predict multiple domains values. Compared with the backpropagation neural network, the support vector machine, the short and long-time memory network, the bi-directional long short-term memory network, and the convolutional neural network - bi-directional long short-term memory network, the proposed method achieves state-of-the-art results in terms of the mean absolute percentage error with 0.365 %, the explained variance score with 99.987 % and the R2-score with 99.984 %, which proves the effectiveness of the proposed model in the load prediction of the integrated energy system. In addition, the proposed method can provide operational guidance for energy production and storage. Through the operation guidance of the proposed method, the carbon emissions are reduced by 238791.58 kg every week.

Recovery With Incomplete Knowledge: Fundamental Bounds on Real-Time Quantum Memories

The recovery of fragile quantum states from decoherence is the basis of building a quantum memory, with applications ranging from quantum communications to quantum computing. Many recovery techniques, such as quantum error correction, rely on the apriori knowledge of the environment noise parameters to achieve their best performance. However, such parameters are likely to drift in time in the context of implementing long-time quantum memories. This necessitates using a "spectator" system, which estimates the noise parameter in real-time, then feed-forwards the outcome to the recovery protocol as a classical side-information. The memory qubits and the spectator system hence comprise the building blocks for a real-time (i.e. drift-adapting) quantum memory. In this article, I consider spectator-based (incomplete knowledge) recovery protocols as a real-time parameter estimation problem (generally with nuisance parameters present), followed by the application of the "best-guess" recovery map to the memory qubits, as informed by the estimation outcome. I present information-theoretic and metrological bounds on the performance of this protocol, quantified by the diamond distance between the "best-guess" recovery and optimal recovery outcomes, thereby identifying the cost of adaptation in real-time quantum memories. Finally, I provide fundamental bounds for multi-cycle recovery in the form of recurrence inequalities. The latter suggests that incomplete knowledge of the noise could be an advantage, as errors from various cycles can cohere. These results are illustrated for the approximate [4,1] code of the amplitude-damping channel and relations to various fields are discussed.

Viscoelastic active diffusion governed by nonequilibrium fractional Langevin equations: Underdamped dynamics and ergodicity breaking

In this work, we investigate the active dynamics and ergodicity breaking of a nonequilibrium fractional Langevin equation (FLE) with a power-law memory kernel of the form K(t)∼t−(2−2H), where 1/2<H<1 represents the Hurst exponent. The system is subjected to two distinct noises: a thermal noise satisfying the fluctuation–dissipation theorem and an active noise characterized by an active Ornstein–Uhlenbeck process with a propulsion memory time τA. We provide analytic solutions for the underdamped active fractional Langevin equation, performing both analytical and computational investigations of dynamic observables such as velocity autocorrelation, the two-time position correlation, ensemble- and time-averaged mean-squared displacements (MSDs), and ergodicity-breaking parameters. Our results reveal that the interplay between the active noise and long-time viscoelastic memory effect leads to unusual and complex nonequilibrium dynamics in the active FLE systems. Furthermore, the active FLE displays a new type of discrepancy between ensemble- and time-averaged observables. The active component of the system exhibits ultraweak ergodicity breaking where both ensemble- and time-averaged MSDs have the same functional form with unequal amplitudes. However, the combined dynamics of the active and thermal components of the active FLE system are eventually ergodic in the infinite-time limit. Intriguingly, the system has a long-standing ergodicity-breaking state before recovering the ergodicity. This apparent ergodicity-breaking state becomes exceptionally long-lived as H→1, making it difficult to observe ergodicity within practical measurement times. Our findings provide insight into related problems, such as the transport dynamics for self-propelled particles in crowded or polymeric media.

Burn-in selection in simulating stationary time series

Many time series models are defined in a recursive manner, which prohibits exact simulations. In practice, one appeals to simulating a long time series and discarding a large number of initial simulated observations, known as the burn-in. For autoregressive models where the dependence decays exponentially fast, the choice of the burn-in is not critical. However, for long-memory time series where the dependence from the remote past is strong, it is not clear how to select the burn-in number. By combining several samplers with randomized burn-in numbers, a method for exactly simulating the expectation of a statistic computed from a time series is developed. Moreover, with some suitably chosen statistics, the exact simulation method can be applied to quantify the effect of burn-in numbers on the simulated sample. Simulation studies are conducted to provide some practical guidances for burn-in selections.

Tracking and Data Association Based on Reinforcement Learning

Currently, most multi-target data association methods require the assumption that the target motion model is known, but this assumption is clearly not valid in a real environment. In the case of an unknown system model, the influence of environmental clutter and sensor detection errors on the association results should be considered, as well as the occurrence of strong target maneuvers and the sudden appearance of new targets during the association process. To address these problems, this paper designs a target tracking and data association algorithm based on reinforcement learning. First, this algorithm combines the dynamic exploration capability of reinforcement learning and the long-time memory function of LSTM network to design a policy network that predicts the probability of associating a point with its various possible source targets. Then, the Bayesian network and the multi-order least squares curve fitting method are combined to predict the location of target, and the results are fed into the Bayesian recursive function to obtain the reward. Simultaneously, some corresponding mechanisms are proposed for possible problems that interfere with the association process. Finally, the simulation experimental results show that this algorithm associates the results with higher accuracy compared to other algorithms when faced with the above problem.

A simple model for pink noise from amplitude modulations

We propose a simple model for the origin of pink noise (or 1/f fluctuation) based on the waves with accumulating frequencies. These waves arise spontaneously in a system with synchronization, resonance, and infrared divergence. Many waves with accumulating frequencies can produce signals of arbitrary small frequencies from a system of small size. This beat mechanism can be understood as amplitude modulation. The pink noise can appear after the demodulation process, which produces a variety of pink noise in many fields. The pink noise thus formed from the beat has nothing to do with dissipation or long-time memory. We also suggest new ways of looking at pink noise in earthquakes, solar flares, and stellar activities.

A double network hydrogel electrolyte-based electrochromic device for enhanced electrochromic performance and lower power consumption

This study aims to investigate the electrochromic behavior of a Y-shaped, water-soluble monosubstituted viologen analog, BPYDB-C6H13, in different electrolyte systems. Two all-in-one gel-type ECDs (CMC-Na-ECD and CMC-Na-PAM-ECD) with CMC-Na hydrogel and CMC-Na-PAM double network hydrogel as the electrolytes and a solution-type ECD (DIW-ECD) were fabricated, and the electrochromic behaviors of BPYDB-C6H13 in different electrolytes were studied. Compared to DIW-ECD, the gel-state ECDs, especially CMC-Na-PAM-ECD with double network channel, showed enhanced cycling stability (retaining of the initial optical contrast after 12,000 s: 99.0% versus 95.5%), improved coloration efficiency (168.5 cm2/C versus 64.3 cm2/C), lower power consumption (0.55 mW/cm2 versus 2.11 mW/cm2), and longer memory time, because CMC-Na-PAM hydrogel had a larger pore size and a smoother transfer path than CMC-Na, thus reduced the path of charge transfer, and enhanced the ionic conductivity. The characteristics of easy-to-make, better electrochromic performance, and ultra-low power consumption make this gel-state ECD a promising candidate for applications in the fields of electronic paper, smart windows, displays, etc.

Prenatal Cafeteria Diet Primes Anxiety-like Behavior Associated to Defects in Volume and Diffusion in the Fimbria-fornix of Mice Offspring

Prenatal exposure to high-energy diets primes brain alterations that increase the risk of developing behavioral and cognitive failures. Alterations in the structure and connectivity of brain involved in learning and memory performance are found in adult obese murine models and in humans. However, the role of prenatal exposure to high-energy diets in the modulation of the brain’s structure and function during cognitive decline remains unknown. We used female C57BL6 mice (n = 10) exposed to a high-energy diets (Cafeteria diet (CAF)) or Chow diet for 9 weeks (before, during and after pregnancy) to characterize their effect on brain structural organization and learning and memory performance in the offspring at two-month-old (n = 17). Memory and learning performance were evaluated using the Y-maze test including forced and spontaneous alternation, novel object recognition (NORT), open field and Barnes maze tests. We found no alterations in the short- or long-time spatial memory performance in male offspring prenatally exposed to CAF diet when compared to the control, but they increased time spent in the edges resembling anxiety-like behavior. By using deformation-based morphometry and diffusion tensor imaging analysis we found that male offspring exposed to CAF diet showed increased volume in primary somatosensory cortex and a reduced volume of fimbria-fornix, which correlate with alterations in its white matter integrity. Biological modeling revealed that prenatal exposure to CAF diet predicts low volume in the fimbria-fornix, which was associated with anxiety in the offspring. The findings suggest that prenatal exposure to high-energy diets prime brain structural alterations related to anxiety in the offspring.

Recent Progress in Photodynamic Immunotherapy with Metal-Based Photosensitizers.

Cancer ranks as a leading cause of death. There is an urgent need to develop minimally invasive methods to eradicate tumors and prevent their recurrence. As a light-driven modality, photodynamic therapy takes advantage of high tumor selectivity and low normal tissue damage. However, it shows poor potential for preventing tumor recurrence. Immunotherapy is currently being used as an alternative treatment for the control of malignant diseases. Although immunotherapy can establish long-time immune memory and efficiently protects treated patients from cancer relapse, its clinical efficacy is limited by the minority of patients' responding rate. Recently, photodynamic immunotherapy, which utilizes photosensitizers as an immunotherapy trigger to exert synergistic effects of photodynamic therapy and tumor immunotherapy, has attracted considerable interest. Like all the newly proposed treatments, there is still room for improvement. In this mini review, the progress in photodynamic immunotherapy with metal-based photosensitizers is summarized. It is hoped that this review can give a broad update on photodynamic immunotherapy and inspire readers.

On optimal block resampling for Gaussian-subordinated long-range dependent processes

Block-based resampling estimators have been intensively investigated for weakly dependent time processes, which has helped to inform implementation (e.g., best block sizes). However, little is known about resampling performance and block sizes under strong or long-range dependence. To establish guideposts in block selection, we consider a broad class of strongly dependent time processes, formed by a transformation of a stationary long-memory Gaussian series, and examine block-based resampling estimators for the variance of the prototypical sample mean; extensions to general statistical functionals are also considered. Unlike weak dependence, the properties of resampling estimators under strong dependence are shown to depend intricately on the nature of nonlinearity in the time series (beyond Hermite ranks) in addition to the long-memory coefficient and block size. Additionally, the intuition has often been that optimal block sizes should be larger under strong dependence (say O(n1/2) for a sample size n) than the optimal order O(n1/3) known under weak dependence. This intuition turns out to be largely incorrect, though a block order O(n1/2) may be reasonable (and even optimal) in many cases, owing to nonlinearity in a long-memory time series. While optimal block sizes are more complex under long-range dependence compared to short-range, we provide a consistent data-driven rule for block selection. Numerical studies illustrate that the guides for block selection perform well in other block-based problems with long-memory time series, such as distribution estimation and strategies for testing Hermite rank.

Two efficient selection methods for high-dimensional CD-CAT utilizing max-marginals factor from MAP query and ensemble learning approach.

Computerized adaptive testing for cognitive diagnosis (CD-CAT) needs to be efficient and responsive in real time to meet practical applications' requirements. For high-dimensional data, the number of categories to be recognized in a test grows exponentially as the number of attributes increases, which can easily cause system reaction time to be too long such that it adversely affects the examinees and thus seriously impacts the measurement efficiency. More importantly, the long-time CPU operations and memory usage of item selection in CD-CAT due to intensive computation are impractical and cannot wholly meet practice needs. This paper proposed two new efficient selection strategies (HIA and CEL) for high-dimensional CD-CAT to address this issue by incorporating the max-marginals from the maximum a posteriori query and integrating the ensemble learning approach into the previous efficient selection methods, respectively. The performance of the proposed selection method was compared with the conventional selection method using simulated and real item pools. The results showed that the proposed methods could significantly improve the measurement efficiency with about 1/2-1/200 of the conventional methods' computation time while retaining similar measurement accuracy. With increasing number of attributes and size of the item pool, the computation time advantage of the proposed methods becomes more significant.

Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model

The PM2.5 index is an important basis for measuring the degree of air pollution. The accurate prediction of PM2.5 concentration has an important guiding role in air pollution prevention and control. The Pearson Correlation Coefficient (PCC) is a common index used to mine the correlation between meteorological factors and other air pollutants. However, this index cannot be used to mine non-linear correlations, nor can it quantitatively analyze the weight of each related attribute. In order to accurately explore the correlation between meteorological factors and other air pollutants and to achieve an accurate prediction of PM2.5 concentration, this paper proposes a short- and long-time memory (LSTM) network prediction model based on Copula entropy (CE) and the adaptive genetic algorithm (AGA). By calculating CE, the correlation between multiple meteorological factors and various atmospheric pollutants and PM2.5 was analyzed. The correlation of influencing factors was sorted according to the size of the correlation coefficients. The contribution rate of meteorological factors and atmospheric pollutants to PM2.5 concentration was determined, used as the weight of each influencing factor and predicted as the input data of the prediction model. In this paper, a long- and short-term memory network (LSTM) suitable for time series data was selected as the prediction model, while the selection of model parameters was taken into account, and the relevant parameters were sought by an adaptive genetic algorithm (AGA). The air pollutant data and meteorological data of Beijing from 1 January 2016 to 31 December 2016 were selected, and MAE and RMSE were used as evaluation indexes. By comparing the experimental results of the CE-AGA-LSTM with those of other eight prediction models (LR, SVM, RF, ARMA, ST-LSTM, LSTM, CE-LSTM and CE-RNN), we found that among the models, the CE-AGA-LSTM model provided the lowest MAE and RMSE values, i.e., 14.5 and 21.88, respectively. At the same time, the loss rate and accuracy of the CE-AGA-LSTM model were evaluated, and the experimental results verified the validity of the model.

Improvement in nonvolatile memory operations for metal–ferroelectric–insulator–semiconductor capacitors using HfZrO2 and ZrO2 thin films as ferroelectric and insulator layers

Metal–ferroelectric–insulator–semiconductor (MFIS) capacitors were characterized to elucidate the optimum design schemes for the ferroelectric field-effect transistor applications. The Hf1−x Zr x O2 (HZO) thin films (18 nm) were prepared on the SiO2 and ZrO2 insulator layers (ILs) with different film thicknesses. The choice of 10 nm thick ZrO2 IL was found to be an optimum condition to properly balance between the values of electric fields applied to the HZO (E HZO) and ZrO2 (E IL) layers, leading to effective improvement in capacitance coupling ratio and to suppression of charge injection for the MFIS capacitors. Furthermore, the crystalline natures of the crystallized HZO films were also found to be strategically controlled on the ZrO2 ILs, which can additionally enhance the E HZO with reducing the E IL. As consequences, the MFIS capacitors using 10 nm thick ZrO2 IL exhibited the ferroelectric memory window as large as 2.5 V at an application of ±5 V, which corresponds to 2.7 times wider value, compared to that obtained from the device using 2 nm thick SiO2 IL. Long-time memory retention and robust program endurance were also verified for the fabricated MFIS capacitors.

Noise-assisted quantum coherence protection in a hierarchical environment

In this paper, we investigate coherence protection of a quantum system coupled to a hierarchical environment by utilizing noise. As an example, we solve the Jaynes-Cummings (J-C) model in presence of both a classical and a quantized noise. The master equation is derived beyond the Markov approximation, where the influence of memory effects from both noises is taken into account. More importantly, we find that the performance of the coherence protection sensitively depends on the non-Markovian properties of both noises. By analyzing the mathematical mechanism of the coherence protection, we show the decoherence caused by a non-Markovian noise with longer memory time can be suppressed by another Markovian noise with shorter memory time. Last but not least, as an outlook, we try to analyze the connection between the atom-cavity entanglement and the atomic coherence, then discuss the possible clue to search for the required noise. The results presented in this paper show the possibility of protecting coherence by utilizing noise and may open a new path to design noise-assisted coherence protection schemes.