Memory effect in time fractional Schrödinger equation

A significant obstacle impeding the advancement of the time fractional Schrodinger equation lies in the challenge of determining its precise mathematical formulation.In order to address this,we undertake an exploration of the time fractional Schrodinger equation within the context of a non-Markovian environment.By leveraging a two-level atom as an illustrative case,we find that the choice to raise i to the order of the time derivative is inappropriate.In contrast to the conventional approach used to depict the dynamic evolution of quantum states in a non-Markovian environment,the time fractional Schrodinger equation,when devoid of fractional-order operations on the imaginary unit i,emerges as a more intuitively comprehensible framework in physics and offers greater simplicity in computational aspects.Meanwhile,we also prove that it is meaningless to study the memory of time fractional Schrodinger equation with time derivative 1<α≤2.It should be noted that we have not yet constructed an open system that can be fully described by the time fractional Schrodinger equation.This will be the focus of future research.Our study might provide a new perspective on the role of time fractional Schrodinger equation.

A Memory-Guided Anomaly Detection Model with Contrastive Learning for Multivariate Time Series

Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification.These limitations can result in the misjudgment of models,leading to a degradation in overall detection performance.This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block(CLME)to overcome the above limitations.The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations.The memory block can record normal patterns of these representations through the utilization of attention-based addressing and reintegration mechanisms.These two modules together effectively alleviate the problem of generalization.Furthermore,this paper introduces a fusion anomaly detection strategy that comprehensively takes into account the residual and feature spaces.Such a strategy can enlarge the discrepancies between normal and abnormal data,which is more conducive to anomaly identification.The proposed CLME model not only efficiently enhances the generalization performance but also improves the ability of anomaly detection.To validate the efficacy of the proposed approach,extensive experiments are conducted on well-established benchmark datasets,including SWaT,PSM,WADI,and MSL.The results demonstrate outstanding performance,with F1 scores of 90.58%,94.83%,91.58%,and 91.75%,respectively.These findings affirm the superiority of the CLME model over existing stateof-the-art anomaly detection methodologies in terms of its ability to detect anomalies within complex datasets accurately.

A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.

Memory effect on the pressure-temperature condition and induction time of gas hydrate nucleation

The focus of this study is to investigate the influence of memory effect and the relation of its existence with the dissociation temperature,using gas hydrate formation and dissociation experiments.This is beneficial because memory effect is considered as an effective approach to promote the thermodynamic and dynamic conditions of gas hydrate nucleation.Seven experimental systems （twenty tests in total） were performed in a 1 L pressure cell.Three types of hydrate morphology,namely massive,whiskery and jelly crystals were present in the experiments.The pressures and temperatures at the time when visual hydrate crystals appeared were measured.Furthermore,the influence of memory effect was quantified in terms of pressure-temperature-time （p-T-t） relations.The results revealed that memory effect could promote the thermodynamic conditions and shorten the induction time when the dissociation temperature was not higher than 25℃.In this study,the nucleation superpressure and induction time decrease gradually with time of tests,when the earlier and the later tests are compared.It is assumed that the residual structure of hydrate dissociation,as the source of the memory effect,provides a site for mass transfer between host and guest molecules.Therefore,a driving force is created between the residual structures and its surrounding bulk phase to promote the hydrate nucleation.However,when the dissociation temperature was higher than 25 ℃,the memory effect vanished.These findings provide references for the application of memory effect in hydrate-based technology.

Estimation of unloading relaxation depth of Baihetan Arch Dam foundation using long-short term memory network

The unloading relaxation caused by excavation for construction of high arch dams is an important factor influencing the foundation’s integrity and strength.To evaluate the degree of unloading relaxation,the long-short term memory(LSTM)network was used to estimate the depth of unloading relaxation zones on the left bank foundation of the Baihetan Arch Dam.Principal component analysis indicates that rock charac-teristics,the structural plane,the protection layer,lithology,and time are the main factors.The LSTM network results demonstrate the unloading relaxation characteristics of the left bank,and the relationships with the factors were also analyzed.The structural plane has the most significant influence on the distribution of unloading relaxation zones.Compared with massive basalt,the columnar jointed basalt experiences a more significant unloading relaxation phenomenon with a clear time effect,with the average unloading relaxation period being 50 d.The protection layer can effectively reduce the unloading relaxation depth by approximately 20%.

Enhancement of refresh time in quasi-nonvolatile memory by the density of states engineering

The recently reported quasi-nonvolatile memory based on semi-floating gate architecture has attracted extensive attention thanks to its potential to bridge the large gap between volatile and nonvolatile memory.However,the further extension of the refresh time in quasi-nonvolatile memory is limited by the charge leakage through the p-n junction.Here,based on the density of states engineered van der Waals heterostructures,the leakage of electrons from the floating gate to the channel is greatly suppressed.As a result,the refresh time is effectively extended to more than 100 s,which is the longest among all previously reported quasi-nonvolatile memories.This work provides a new idea to enhance the refresh time of quasi-nonvolatile memory by the density of states engineering and demonstrates great application potential for high-speed and low-power memory technology.

Reaction mechanism between“memory effect”and induction time of gas hydrate formation

Abstract Using visual experimental apparatus, one system （T40, 1×10^-3 mol/L, nonadded with coal） and another system （T40, 2×10^-3 mol/L, added with coal） were experimented with for three times and two times, respectively. Five groups of P-T experimental parameters were obtained using the data logger system and analyzed combined with the video information of the experiments. Major conclustions show that the induction time is shortened by 10-20 times in the experimental system containing residual pentahedral ring structures; ＂memory effect＂ can accelerate the dynamic progress and improve the thermodynamic conditions of gas hydrate formation.

Binaural Speech Separation Algorithm Based on Long and Short Time Memory Networks

Speaker separation in complex acoustic environment is one of challenging tasks in speech separation.In practice,speakers are very often unmoving or moving slowly in normal communication.In this case,the spatial features among the consecutive speech frames become highly correlated such that it is helpful for speaker separation by providing additional spatial information.To fully exploit this information,we design a separation system on Recurrent Neural Network(RNN)with long short-term memory(LSTM)which effectively learns the temporal dynamics of spatial features.In detail,a LSTM-based speaker separation algorithm is proposed to extract the spatial features in each time-frequency(TF)unit and form the corresponding feature vector.Then,we treat speaker separation as a supervised learning problem,where a modified ideal ratio mask(IRM)is defined as the training function during LSTM learning.Simulations show that the proposed system achieves attractive separation performance in noisy and reverberant environments.Specifically,during the untrained acoustic test with limited priors,e.g.,unmatched signal to noise ratio(SNR)and reverberation,the proposed LSTM based algorithm can still outperforms the existing DNN based method in the measures of PESQ and STOI.It indicates our method is more robust in untrained conditions.

An Efficient Crash Recovery Technique for Real-Time Main Memory Database

This paper presents an efficient recovery scheme suitable for real-time mainmemory database. In the recovery scheme, log records are stored in non-volatile RAM which is dividedinto four different partitions based on transaction types. Similarly, a main memory database isdivided into four partitions based data types. When the using ratio of log store area exceeds thethreshold value, checkpoint procedure is triggered. During executing checkpoint procedure, someuseless log records are deleted. During restart recovery after a crash, partition reloading policyis adopted to assure that critical data are reloaded and restored in advance, so that the databasesystem can be brought up before the entire database is reloaded into main memory. Therefore downtime is obvionsly reduced. Simulation experiments show our recovery scheme obviously improves thesystem performance, and does a favor to meet the dtadlints of real-time transactions.

Dependence of the Blow-up Time with Respect to Parameters for Semilinear Parabolic Equations with Nonlinear Memory

In this paper we discuss the bounds for the modulus of continuity of the blow-up time with respect to three parameters of λ, h, and p respectively for the initial boundary value problem of the semilinear parabolic equation.

Time Series Forecasting Fusion Network Model Based on Prophet and Improved LSTM

Time series forecasting and analysis are widely used in many fields and application scenarios.Time series historical data reflects the change pattern and trend,which can serve the application and decision in each application scenario to a certain extent.In this paper,we select the time series prediction problem in the atmospheric environment scenario to start the application research.In terms of data support,we obtain the data of nearly 3500 vehicles in some cities in China fromRunwoda Research Institute,focusing on the major pollutant emission data of non-road mobile machinery and high emission vehicles in Beijing and Bozhou,Anhui Province to build the dataset and conduct the time series prediction analysis experiments on them.This paper proposes a P-gLSTNet model,and uses Autoregressive Integrated Moving Average model(ARIMA),long and short-term memory(LSTM),and Prophet to predict and compare the emissions in the future period.The experiments are validated on four public data sets and one self-collected data set,and the mean absolute error(MAE),root mean square error(RMSE),and mean absolute percentage error(MAPE)are selected as the evaluationmetrics.The experimental results show that the proposed P-gLSTNet fusion model predicts less error,outperforms the backbone method,and is more suitable for the prediction of time-series data in this scenario.

Cognitive Demand and Reminders Effect on Time-Based Prospective Memory in Amnesic Mild Cognitive Impairment (AMCI) and in Healthy Elderly

Individuals with-Mild Cognitive Impairment (MCI) often complain of difficulty remembering to carry out intended actions. We investigated the relative efficacy of a different reminder in performing a time-based Prospective Memory (PM) task. The PM performance of 24 participants with amnesic Mild Cognitive Impairment (AMCI) has been compared with that of 24 healthy controls. As ongoing task, samples of the Attentive Matrices Test were used. In the PM task subjects were requested to write an “X” every three minutes during a 9 minutes period. Participants received the task consisting either in a low demand condition (checking number “5”) or in a high demand condition (checking numbers “1”, “4”, “9”). In order to be as punctual as possible, participants were asked to simultaneously write the “X” at task time expiration, using a digital clock. Time monitoring was recorded. Reminder occurring was manipulated in that participants could receive critical, accidental or completely absent reminder. As expected, high cognitive demand was negatively correlated with PM performance and time monitoring. Unexpectedly, all the participants did not benefit from the critical reminder. These findings demonstrated, from a behavioral perspective, that Working Memory (WM) and PM processes are not based on the same memory system and PM may require WM resources at high demand.

Design of 1 kbit antifuse one time programmable memory IP using dual program voltage

A 1 kbit antifuse one time programmable(OTP) memory IP,which is one of the non-volatile memory IPs,was designed and used for power management integrated circuits(ICs).A conventional antifuse OTP cell using a single positive program voltage(VPP) has a problem when applying a higher voltage than the breakdown voltage of the thin gate oxides and at the same time,securing the reliability of medium voltage(VM) devices that are thick gate transistors.A new antifuse OTP cell using a dual program voltage was proposed to prevent the possibility for failures in a qualification test or the yield drop.For the newly proposed cell,a stable sensing is secured from the post-program resistances of several ten thousand ohms or below due to the voltage higher than the hard breakdown voltage applied to the terminals of the antifuse.The layout size of the designed 1 kbit antifuse OTP memory IP with Dongbu HiTek's 0.18 μm Bipolar-CMOS-DMOS(BCD) process is 567.9 μm×205.135 μm and the post-program resistance of an antifuse is predicted to be several ten thousand ohms.

Timed Petri Net Models of Shared-Memory Bus-Based Multiprocessors

In shared-memory bus-based multiprocessors, when the number of processors grows, the processors spend an increasing amount of time waiting for access to the bus (and shared memory). This contention reduces the performance of processors and imposes a limitation of the number of processors that can be used efficiently in bus-based systems. Since the multi-processor’s performance depends upon many parameters which affect the performance in different ways, timed Petri nets are used to model shared-memory bus-based multiprocessors at the instruction execution level, and the developed models are used to study how the performance of processors changes with the number of processors in the system. The results illustrate very well the restriction on the number of processors imposed by the shared bus. All performance characteristics presented in this paper are obtained by discrete-event simulation of Petri net models.

Embryonic skeleton development and neonatal learning and memory ability of rats anesthetized with pentobarbital sodium: Differences of administration occasion and time

BACKGROUND: Generally speaking, anesthesia is often used in gravid body and it has been already proved that many kind of medicine can result in malformation. OBJECTIVE: To explore embryonic skeleton development and neonatal learning and memory of rats anesthetized with pentobarbital sodium in gravid rats. DESIGN: A randomized control trial. SETTING: Laboratory Animal Center of Xuzhou Medical College. MATERIALS: A total of 80 adult female SD rats, of clean grade and weighing 220-240 g, were selected in this study. The main reagents were detailed as follows: pentobarbital sodium (Shanghai Xingzhi Chemical Plant, batch number: 921019); MG-2 maze test apparatus (Zhangjiagang Biomedical Instrument Factory); somatotype microscope (Beijing Taike Instrument Co., Ltd.). METHODS: ① A total of 160 SD rats of half males and females were selected in this study. All rats were copulated. The day that the plug was checked out in the vagina next day was looked as the first day of pregnancy. Gravid rats were divided randomly into four groups, including early anesthesia group, second anesthesia group, late anesthesia group and control group with 20 in each group. Rats in the early anesthesia group were injected with 25 mg/kg soluble pentobarbitone on the 7th day of pregnancy for once; rats in the second anesthesia group were anesthetized with 25 mg/kg soluble pentobarbitone on the 7th and the 14th days of pregnancy for once; rats in the late anesthesia group were anesthetized with 25 mg/kg soluble pentobarbitone on the 14th day of pregnancy for once; rats in the control group did not treat with anything. The time of anesthetizing was controlled in 3 to 4 hours and ether was absorbed while the time was not enough. ② Half of each group was sacrificed on day 20th of pregnancy and the fetus was taken out to be stained with alizarin red S. After stained, the fetal skeleton was examined. The learning and memorizing of one-month rats that were given birth by the rest gravid rats were tested through electric mare method. Determine their study ability according to their correct rate of 90% or above of arrival at the safe area in 20 s. After they finally learned to arrive at the safe area correctly, test them once more in 24 hours and record the correct rate of 15 times. MAIN OUTCOME MEASURES: The rate of malformation in fetus and ability of learning and memory in one-month rats. RESULTS: A total of 80 female rats were anesthetized in this experiment. Totally 490 immature rats were tested with maze testing machine and 196 fetuses were stained with alizarin red S to observe the development of their skeleton. However, one of the 80 female rats was led to death because of overdose. ① Malformation experiment: Learning ability of second anesthesia group was evidently different from the control group while the other two groups were not in the electric mare method. The fetal skeleton malformation rate of three experimental groups was 87.0%, 60.9% and 17.9%, respectively, while it was 5.6% in the control group. ② Electric mare method: Times of rats which arrived at the safe regions were respectively 49.0±31.0, 68.0±35.0, 47.0±31.0 and 44.0±21.0 in early anesthesia group, second anesthesia group, late anesthesia group and control group; and then, there was significant difference between the second anesthesia group and the control group (P < 0.05). Exact rates of memory of rats were respectively (64.36±14.35)%, (62.15±18.33)%, (54.19±12.28)% and (68.24±15.91)% in early anesthesia group, second anesthesia group, late anesthesia group and control group; and then, there were no significant differences as compared with the control group (P > 0.05). CONCLUSION: The influence of anesthesia with pentobarbital sodium is obvious in fetal skeleton development and learning and memory ability.

An Improved Time Feedforward Connections Recurrent Neural Networks

Recurrent Neural Networks(RNNs)have been widely applied to deal with temporal problems,such as flood forecasting and financial data processing.On the one hand,traditional RNNs models amplify the gradient issue due to the strict time serial dependency,making it difficult to realize a long-term memory function.On the other hand,RNNs cells are highly complex,which will signifi-cantly increase computational complexity and cause waste of computational resources during model training.In this paper,an improved Time Feedforward Connections Recurrent Neural Networks(TFC-RNNs)model was first proposed to address the gradient issue.A parallel branch was introduced for the hidden state at time t−2 to be directly transferred to time t without the nonlinear transforma-tion at time t−1.This is effective in improving the long-term dependence of RNNs.Then,a novel cell structure named Single Gate Recurrent Unit(SGRU)was presented.This cell structure can reduce the number of parameters for RNNs cell,consequently reducing the computational complexity.Next,applying SGRU to TFC-RNNs as a new TFC-SGRU model solves the above two difficulties.Finally,the performance of our proposed TFC-SGRU was verified through sev-eral experiments in terms of long-term memory and anti-interference capabilities.Experimental results demonstrated that our proposed TFC-SGRU model can cap-ture helpful information with time step 1500 and effectively filter out the noise.The TFC-SGRU model accuracy is better than the LSTM and GRU models regarding language processing ability.

老字号品牌文化记忆的叙事性视觉再生设计研究

在当今社会,消费结构和理念正在经历深刻变革,老字号品牌文化记忆因未被转化为可满足现代传播需求的形式,亟需有效激活其再生价值并提升品牌效应。深入分析老字号品牌文化记忆内涵及当代传承状况,探究叙事性视觉再生设计方法体系和当代叙事性传播模式,提出将老字号品牌的文化记忆与叙事性视觉设计相结合的创新理念。进一步探索品牌年轻化、数字化、多维化的视觉再生设计策略,为老字号品牌的复兴和形象升级提供切实可行的设计思路。

考虑工作记忆容量和时间压力影响的驾驶绩效分析

随着风险驾驶机理研究的不断完善,外部情境因素和驾驶人内部认知差异逐渐成为当前研究的热点和难点。本文为研究时间压力、工作记忆容量与驾驶绩效之间的关系,采用时间约束和动机等主客观相结合的方法实现时间压力的施加,采用复杂跨度范式测量驾驶人工作记忆容量。基于驾驶模拟器系统构建相关驾驶场景,开展心理学与模拟驾驶行为试验,采集驾驶人心理、操作和车辆运行数据,分别就时间压力、工作记忆容量及其组合对驾驶绩效的影响进行分析。结果表明:时间压力对超速比例、碰撞概率、制动反应时间、左转选择间隙皆有显著的影响效应,且时间压力越高,超速比例越大、碰撞概率越高、制动反应时间越快、左转选择间隙越小的频次越多;工作记忆容量仅对制动反应时间有影响,工作记忆容量越高,制动反应时间越快;时间压力与工作记忆容量对制动反应时间无交互影响,但随着时间压力的增大,高工作记忆容量人群与低工作记忆容量人群在制动反应时间上的差异逐渐变小。这些发现为研究时间压力下的驾驶绩效提供了驾驶人认知方面的新见解。

双向长短期记忆网络的时间序列预测方法

时间序列预测即利用历史时间序列数据,预测未来一段时间内的数据信息,以便提前制定相应策略。目前,时间序列的类别复杂繁多,而现有的时间序列预测模型面对多种类型数据时无法取得稳定预测的结果,进而难以同时满足对现实中多种复杂的时序数据预测的应用需求。针对上述问题,提出了一种基于时间注意力机制双向长短期记忆网络的时间序列预测方法。笔者提出的网络模型采用改进的正向和反向传播机制提取时序信息并通过自适应权重分配策略推理未来的时序信息。具体来说,设计了一个改进的双向长短期记忆网络,通过结合双向长短期记忆和长短期记忆网络提取深度时间序列特征,挖掘上下文的时序依赖关系。在此基础上,融合所提出的时间注意力机制,实现对深度时间序列特征进行自适应加权,提升深度时序特征的显著性表达能力。通过与同类代表性方法在多个不同类别数据集上的客观定量对比,实验结果表明,该方法能够在多种类别的复杂时间序列数据上更优的预测性能。