Memory Characteristics Research Articles

Executive functions and addictions: a Structural Equation Modeling Approach and the Italian validation of the Adult Executive Functioning Inventory (ADEXI)

Introduction the work aims to observe the associations between psychoactive substance use and gambling and executive functioning as well as to validate the Italian version of the “Adult Executive Functioning Inventory” (ADEXI) scale. Methods data were collected through a representative cross-sectional study among 5,160 people (18-84 years old) called IPSAD® (Italian Population Survey on Alcohol and Other Drugs). Structural Equation Modeling (SEM) was performed to explore the associations between ADEXI and other behaviors measured with standardized questionnaires. Cronbach α has been performed to investigate the psychometric properties of the Italian version of the ADEXI scale. Results SEM showed that both WM and INH were correlated with problematic cannabis use (WM r = 0.112; INH r = 0.251) and gambling (WM r = 0.101; INH r = 0.168), while problematic alcohol use was correlated only with INH (r = 0.233). Cronbach α for the WM subscale was 0.833 (CI 0.826-0.840), while for INH was 0.694 (CI 0.680-0.708). Conclusion results pointed out a strong correlation between addictions (substance-related and non-substance-related) and WM and INH impairments among the adult general population. Moreover, the ADEXI scale could be considered a valuable tool for general population surveys to detect working memory and inhibition characteristics.

Physical Reservoir Computing Using van der Waals Ferroelectrics for Acoustic Keyword Spotting.

Acoustic keyword spotting (KWS) plays a pivotal role in the voice-activated systems of artificial intelligence (AI), allowing for hands-free interactions between humans and smart devices through information retrieval of the voice commands. The cloud computing technology integrated with the artificial neural networks has been employed to execute the KWS tasks, which however suffers from propagation delay and the risk of privacy breach. Here, we report a single-node reservoir computing (RC) system based on the CuInP2S6 (CIPS)/graphene heterostructure planar device for implementing the KWS task with low computation cost. Through deliberately tuning the Schottky barrier height at the ferroelectric CIPS interfaces for the thermionic injection and transport of the electrons, the typical nonlinear current response and fading memory characteristics are achieved in the device. Additionally, the device exhibits diverse synaptic plasticity with an excellent separation capability of the temporal information. We construct a RC system through employing the ferroelectric device as the physical node to spot the acoustic keywords, i.e., the natural numbers from 1 to 9 based on simulation, in which the system demonstrates outstanding performance with high accuracy rate (>94.6%) and recall rate (>92.0%). Our work promises physical RC in single-node configuration as a prospective computing platform to process the acoustic keywords, promoting its applications in the artificial auditory system at the edge.

Potential for multi-application advancements from doping zirconium (Zr) for improved optical, electrical, and resistive memory properties of zinc oxide (ZnO) thin films

Transition metal-doped Zinc oxide (ZnO) thin films with an optimal wide band gap and semiconducting nature find numerous applications in optoelectronic devices, gas sensors, spintronic devices, and electronics. In this study, Zirconium (Zr) doped ZnO thin films were deposited on ITO (Indium Tin oxide) coated glass substrate using RF-magnetron sputtering. Optical and electrical properties were examined for their potential use in resistive random-access memory (RRAM) applications. X-ray Diffraction (XRD), UV–vis spectroscopy, x-ray photoelectron spectroscopy (XPS), Atomic force microscopy (AFM) and Scanning electron microscopy (SEM) were used to investigate structural, optical, and compositional properties and roughness respectively. The results demonstrate that the films possess crystalline properties. Additionally, an augmentation in Zr concentration correlates with an elevation in the optical band gap, ascending from 3.226 eV to 3.26 eV, accompanied by an increase in Urbach energy from 0.0826 eV to 0.1234 eV. The film with the highest Zr content among all the films demonstrated the best electrical performance for resistive memory applications. Incorporating Zr as a dopant shows enhancement in the electrical performance and such ZnO films with optimum concertation of Zr can potentially be used in RRAM. ZnO being a versatile host material, its doping with Zr may extend its applications in catalysis, gas sensing, energy storage, and biomedical engineering. ZnO thin films employ zirconium (Zr) as a dopant, which is a novel way to improve the material’s characteristics. Although ZnO has been thoroughly researched, adding Zr presents a novel technique to enhance optical, electrical, and resistive memory characteristics all at once that has not been fully investigated.

A fully floating memristor emulator with long-term memory

In this paper, we proposed a fully floating memristor emulator with long-term memory characteristics. The circuit comprises operational amplifiers and an analog switch. Switching between incremental and decremental modes is easily achieved by changing the polarity of the input signal. The key feature of the emulator is its long-term memory, made possible by the switch and voltage follower. The correctness of the derived formula is verified using Matlab, and the emulator's pinched hysteresis loop and non-volatility are assessed using LTspice. Additionally, an experimental platform was constructed for physical testing, with the physical results showing consistency with the simulations. Finally, the proposed emulator was applied to a simple read-write circuit, demonstrating its practical applicability.

Simulink Modeling and Analysis of a Three-Dimensional Discrete Memristor Map

The memristor, a novel device, has been widely utilized due to its small size, low power consumption, and memory characteristics. In this paper, we propose a new three-dimensional discrete memristor map based on coupling a one-dimensional chaotic map amplifier with a memristor. Firstly, we analyzed the memristor model to understand its characteristics. Then, a Simulink model for this three-dimensional discrete memristor map was developed. Lastly, the complex dynamical characteristics of the system were analyzed via equilibrium points, bifurcation diagrams, Lyapunov exponent spectra, complexity, and multistability. This study revealed the phenomena of coexisting attractors and hyperchaotic attractors. Simulink modeling confirmed that the discrete memristors effectively enhanced the chaos complexity in the three-dimensional discrete memristor map. This approach addresses the shortcomings of randomness, the lack of ergodicity, and the small key space in a one-dimensional chaotic map, thereby enriching the theoretical analysis and circuit implementation of chaos.

Applying (ARFIMA) Model for Forecast the Saudi Stock Market Prices

The interest in the topic of time series forecasting has increased during the recent years and thus appeared specific modern methods, for example Autoregressive Fractional Integrated Moving Average model (ARFIMA), or what is called long memory model, which use fractionally difference (d) instead of integer which used in ARIMA models. In this study we displayed long memory feature and its tests. Our discussion supported by analysing the real time series (daily closing index of the Saudi Arabia Stock Market prices) over period 1/1/2018 to 19/12/2022, including 1240 observations, to make a good model to giving forecast results for future, using statistical tests and statistical software (R- 4.2.2 program). Firstly we checked that the data series was unstable by testing unit root, using Dickey fuller and Philips Perron tests ; and confirmed the presence of a long memory pattern in it by calculating the Hurst exponent (H), then calculated the fractional differential coefficient (d), determined the appropriate models for analysis and prediction, then the best model was chosen among them based on the comparison criteria, then predicted the values for the next 5 days, R - 4.2.2 software was used in all of these tests and predictions Statistical results indicated that the optimal model to represent the data series is ARFIMA (3,0.383,2) which used to predict future values.

Towards mixed physical node reservoir computing: light-emitting synaptic reservoir system with dual photoelectric output

Memristor-based physical reservoir computing holds significant potential for efficiently processing complex spatiotemporal data, which is crucial for advancing artificial intelligence. However, owing to the single physical node mapping characteristic of traditional memristor reservoir computing, it inevitably induces high repeatability of eigenvalues to a certain extent and significantly limits the efficiency and performance of memristor-based reservoir computing for complex tasks. Hence, this work firstly reports an artificial light-emitting synaptic (LES) device with dual photoelectric output for reservoir computing, and a reservoir system with mixed physical nodes is proposed. The system effectively transforms the input signal into two eigenvalue outputs using a mixed physical node reservoir comprising distinct physical quantities, namely optical output with nonlinear optical effects and electrical output with memory characteristics. Unlike previously reported memristor-based reservoir systems, which pursue rich reservoir states in one physical dimension, our mixed physical node reservoir system can obtain reservoir states in two physical dimensions with one input without increasing the number and types of devices. The recognition rate of the artificial light-emitting synaptic reservoir system can achieve 97.22% in MNIST recognition. Furthermore, the recognition task of multichannel images can be realized through the nonlinear mapping of the photoelectric dual reservoir, resulting in a recognition accuracy of 99.25%. The mixed physical node reservoir computing proposed in this work is promising for implementing the development of photoelectric mixed neural networks and material-algorithm collaborative design.

High-Dimensional Physical Reservoir with Back-End-of-Line-Compatible Tin Monoxide Thin-Film Transistor.

This work demonstrates a physical reservoir using a back-end-of-line compatible thin-film transistor (TFT) with tin monoxide (SnO) as the channel material for neuromorphic computing. The electron trapping and time-dependent detrapping at the channel interface induce the SnO·TFT to exhibit fading memory and nonlinearity characteristics, the critical assets for physical reservoir computing. The three-terminal configuration of the TFT allows the generation of higher-dimensional reservoir states by simultaneously adjusting the bias conditions of the gate and drain terminals, surpassing the performances of typical two-terminal-based reservoirs such as memristors. The high-dimensional SnO TFT reservoir performs exceptionally in two benchmark tests, achieving a 94.1% accuracy in Modified National Institute of Standards and Technology handwritten number recognition and a normalized root-mean-square error of 0.089 in Mackey-Glass time-series prediction. Furthermore, it is suitable for vertical integration because its fabrication temperature is <250 °C, providing the benefit of achieving a high integration density.

High‐performance shape memory characteristics by integrating urea linkages into the polyurethane elastomer

AbstractShape memory polymers have been widely researched to develop multifunctional materials that respond smartly to external stimulations and programmed signals. Among them, shape memory polyurethane (SMPU) is drawing great attention owing to its highly tunable properties and ease of integrating new functions. However, the effect of urea bonds on SMPU elastomers has rarely been investigated because of the extreme reactivity of amine crosslinkers. In this study, we used diethanolamine (DEOA), which contains a secondary amine, as the crosslinker for SMPU synthesis. Unlike other crosslinkers containing primary amines, they can form urea bonds in a more gentle manner. The urea bonds reinforce the intermolecular interactions between the hard segments with strong hydrogen bonding, thus increasing the phase separation and degree of crystallization. Consequently, the urea‐containing SMPU exhibits improved mechanical strength and shape memory abilities for both fixing and recovery. Moreover, the transcarbamoylation reaction, which enables the reconfiguration of the original shape, is observed. The introduction of the urea bonds into the SMPU elastomer can be beneficial in achieving flawless shape memory performances in various sensitive applications.

An Ultrafast Multibit Memory Based on the ReS2/h-BN/Graphene Heterostructure.

The exponential growth of data in the big data era has made it imperative to improve the data storage density and calculation speed. Therefore, the development of a multibit memory with an ultrafast operational speed is of great significance. In this work, a floating-gate (FG) memory based on the ReS2/h-BN/graphene van der Waals heterostructure is reported. The device exhibits ultrafast and multilevel nonvolatile memory characteristics, notably featuring an exceptionally large memory window of 113.36 V, a substantial erasing/programming current ratio of 107, an ultrafast operational speed of 30 ns, outstanding endurance exceeding 1000 cycles, and retention performance exceeding 1100 s. Furthermore, the device exhibits both electrically and optically tunable multilevel nonvolatile memory behavior. By controlling the voltage and light pulse parameters, the device achieves an electrical memory state of 130 levels (>7 bits) and an optical memory state of 45 levels (>5 bits).

Explainable highway performance degradation prediction model based on LSTM

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.

Systematic Analysis of Spacer and Gate Length Scaling on Memory Characteristics in 3D NAND Flash Memory

This study investigates the impact of oxide/nitride (ON) pitch scaling on the memory performance of 3D NAND flash memory. We aim to enhance 3D NAND flash memory by systematically reducing the spacer length (Ls) and gate length (Lg) to achieve improved memory characteristics. Using TCAD simulations, we evaluate the effects of Ls and Lg scaling on the program speed, erase speed, and Z-interference. Furthermore, we examine the influence of concave and convex channel structures in the context of Ls and Lg scaling. By analyzing the distributions of electron and hole-trapped charges, we provide insights into optimizing the trade-offs between the memory window and retention characteristics. This research offers valuable guidelines for improving the reliability and performance of 3D NAND flash memory through a systematic analysis of spacer and gate length scaling.

Selectively maintaining an object's feature in visual working memory: A comparison between highly discriminable and fine-grained features.

Selectively maintaining information is an essential function of visual working memory (VWM). Recent VWM studies have mainly focused on selective maintenance of objects, leaving the mechanisms of selectively maintaining an object's feature in VWM unknown. Based on the interactive model of perception and VWM, we hypothesized that there are distinct selective maintenance mechanisms for objects containing fine-grained features versus objects containing highly discriminable features. To test this hypothesis, we first required participants to memorize a dual-feature object (colored simple shapes vs. colored polygons), and informed them about the target feature via a retro-cue. Then a visual search task was added to examine the fate of the irrelevant feature. The selective maintenance of an object's feature predicted that the irrelevant feature should be removed from the active state of VWM and should not capture attention when presented as a distractor in the visual search task. We found that irrelevant simple shapes impaired performance in the visual search task (Experiment 1). However, irrelevant polygons did not affect visual search performance (Experiment 2), and this could not be explained by decay of polygons (Experiment 3) or by polygons not capturing attention (Experiment 4). These findings suggest that VWM adopts dissociable mechanisms to selectively maintain an object's feature, depending on the feature's perceptual characteristics.

Double Dissociation of Cognitive Varies under Unilateral Radiation Exposure on the Hippocampus

Although the key position of the hippocampus (HP) in memory processes is not in doubt, the specifics of its participation in cognitive processes are far from being established. At the same time, the role of the HP in differentiating the novelty of impressions is often discussed in the context of adult HP neurogenesis. Radiation exposure to the HP, which inhibits the processes of neurogenesis, can serve as a model for studying this relationship. A homogeneous sample of 28 patients with meningiomas of the chiasmal-sellar area adjacent to the HP was studied. 15 patients were diagnosed with a left-sided location of the tumor and 13 patients with a right-sided one. The two groups were comparable in terms of demographic, clinical and morphometric characteristics. In order to stop the growth of the tumor, the patients underwent radiation therapy (RT), in which the HP on the side of the pathological process was forced to receive a dose comparable to the dose in the tumor. The study using the original technique was carried out before the start of RT, immediately after its completion, 6 and 12 months after the end of RT. Data were obtained on earlier changes in memory characteristics mediated by the right hippocampal region, but at the same time — more pronounced long-term cognitive consequences of ionizing effects on the HP of the left hemisphere.

Self-rectifying NiOX/WOX heterojunction synaptic memristor for crossbar architectured reservoir computing system

In this study, we examine an ITO/NiOX/WOX/Pt p-n heterojunction memristor for neuromorphic applications as a synaptic crossbar array. The transition in the depletion region at the interface between p-type NiOX and n-type WOX crucially influences the self-rectifying characteristics of the device depending on the voltage polarity. Furthermore, long- and short-term memory coexist depending on the switching voltage condition, thus enabling various neuromorphic applications, such as reservoir computing and the use of the synaptic device at the off-chip trained network. The reliable operational characteristics are confirmed by obtaining an average memory window (>9) and rectification ratio (>58) between device-to-device and cycle-to-cycle. Furthermore, synaptic functions were successfully implemented, such as spike-rate-dependent plasticity, spike-number-dependent plasticity, paired-pulse facilitation, post-tetanic potentiation, paired-pulse depression, and post-tetanic depression, in conjunction with repeatability. Ultimately, reservoir computing is accomplished based on the I–V nonlinearity and short-term memory characteristics. A high pattern recognition rate (>96.2 %) is achieved in MNIST tasks using 16 reservoir states, thus affirming the trustworthiness of the reservoir computing system. With its comprehensive approach to synaptic applications for neuromorphic computing systems, the heterojunction device highlights its considerable potential to advance artificial neural networks in conjunction with novel memristor technology directions.

An extreme multistable circuit based on a new memcapacitor and a new meminductor

Expanding the definition of memristor gets memcapacitor and meminductor. Because there are very few physical memory elements, it is very meaningful to apply its equivalent model to the study of physical circuits. In this paper, based on the definition of the memory element, a new memcapacitor and a new meminductor are proposed, and a chaotic oscillator circuit is constructed based on them. Whether the two memory elements have memory characteristics is analyzed. The range of equilibrium points of the chaotic oscillator circuit is determined. The dynamical behavior of this chaotic oscillator circuit is analyzed by the Lyapunov exponent spectrum (LEs), bifurcation diagram (BD) and spectral entropy complexity (SE). Finally, Multisim software is used to verify the correctness of the simulation, and the physical realizability of the system is verified by the DSP platform. The results of the research help to better utilize this chaotic oscillator circuit in the field of communication encryption.

Gaussian bistable cascade double feedback stochastic resonance weak signal enhancement detection

Stochastic resonance (SR), as a nonlinear weak signal enhancement detection method, has found extensive applications in fields like fault diagnosis and biological information processing. However, further research has revealed that single stochastic resonance systems often fail to meet the requirements of practical engineering applications, leading to a growing interest in multi-system collaborative stochastic resonance. This article first constructs a cascaded SR system based on the Gaussian bistable model, and introduces the idea of feedback into the cascaded system. A Gaussian bistable cascaded dual feedback SR system is proposed to improve the output signal enhancement performance of the system. This system effectively utilizes the memory characteristics inherent in the output feedback and enables multi-level particle transitions and energy transfers in noisy input signals, thereby reducing noise and enhancing the extraction of weak signal features. Furthermore, the BSO optimization algorithm was utilized to optimize the system parameters of the model, resulting in an adaptive Gaussian bistable cascaded double feedback stochastic resonance (GBCDFSR) system. This system is applied to the processing of noisy periodic signals across various noise types. Simulation experiments and engineering applications show that the proposed model has stronger signal detection capabilities.

Delay characteristics of quasi-nonvolatile memory devices operating in positive feedback mechanism.

This study examines the memory and read delay characteristics of quasi-nonvolatile memory (QNVM) devices operating in a positive feedback mechanism through technology computer-aided design simulation. The QNVM devices exhibit a rapid operation speed of 5 ns, a significant sensing margin of approximately 8.0 μA, and a retention time of around 1 s without any external bias. These devices showcase an exceptionally brief read delay of 0.12 ns. The energy band diagrams during the memory operation are analysed to clarify the factors influencing the read delay. The write and standby conditions modulate the potential barrier height during the standby operation, thereby affecting the read delay. Moreover, the shorter rising time causes the reduction of the read delay. This study demonstrates that the QNVM device has the potential to resolve energy consumption and speed issues in nonvolatile memory devices.

Accuracy and completeness of autobiographical memory: evidence from a wearable camera study

ABSTRACT A small wearable camera, SenseCam, passively captured pictures from everyday experience that were later used to evaluate the accuracy and completeness of autobiographical memory. Nine undergraduates wore SenseCams that took pictures every 10 s for two days. After one week and one month, participants first recalled their experiences from specific time periods (timeslices), then reviewed the corresponding pictures to make corrections and report information omitted from initial recall. Results demonstrated the utility of wearable cameras as research tools, and illustrated several characteristics of everyday memory. Recall contents reflected the structure of undergraduate lives. Three different types of omissions were reported: neglected, reminded, and forgotten. Pictures stimulated memory, even for non-visual information (e.g., feelings, thoughts), increasing recall by 23%. The mean completeness of initial recall was 79% (upper bound), with at least 21% forgetting. Accuracy was self-scored by participants (M = 89%), and the mean error rate (11%) provided evidence against strong reconstructive and copy theories of memory. The characteristics of errors shed light on the cognitive processes underlying them. Ratings of recall (confidence, reliving, knowledge, and frequency) supported the episodic/semantic distinction, the dual-process theory of repetition, and reconstructive imagery. Metamemory measures showed a positive correlation between confidence and accuracy.

Prediction of Sunspot Number with Hybrid Model Based on 1D-CNN, BiLSTM and Multi-Head Attention Mechanism

Sunspots have a significant impact on human activities. In this study, we aimed to improve solar activity prediction accuracy. To predict the sunspot number based on different aspects, such as extracted features and relationships among data, we developed a hybrid model that includes a one-dimensional convolutional neural network (1D-CNN) for extracting the features of sunspots and bidirectional long short-term memory (BiLSTM) embedded with a multi-head attention mechanism (MHAM) to learn the inner relationships among data and finally predict the sunspot number. We evaluated our model and several existing models according to different evaluation indicators, such as mean absolute error (MAE) and root mean square error (RMSE). Compared with the informer, stacked LSTM, XGBoost-DL, and EMD-LSTM-AM models, the RMSE and MAE of our results were more than 42.5% and 65.1% lower, respectively. The experimental results demonstrate that our model has higher accuracy than other methods.