The European Physical Journal B最新文献

Implementing artificial synapses that emulate the synaptic behavior observed in the brain is one of the most critical requirements for neuromorphic computing. Resistive random-access memories (RRAM) have been proposed as a candidate for artificial synaptic devices. For this applicability, RRAM device performance depends on the technology used to fabricate the metal–insulator–metal (MIM) stack and the technology chosen for the selector device. To analyze these dependencies, the integrated RRAM devices in a 4k-bit array are studied on a 200 mm wafer scale in this work. The RRAM devices are integrated into two different CMOS transistor technologies of IHP, namely 250 nm and 130 nm and the devices are compared in terms of their pristine state current. The devices in 130 nm technology have shown lower number of high pristine state current devices per die in comparison to the 250 nm technology. For the 130 nm technology, the forming voltage is reduced due to the decrease of (hbox {HfO}_2) dielectric thickness from 8 nm to 5 nm. Additionally, 5% Al-doped 4 nm (hbox {HfO}_2) dielectric displayed a similar reduction in forming voltage and a lower variation in the values. Finally, the multi-level switching between the dielectric layers in 250 nm and 130 nm technologies are compared, where 130 nm showed a more significant number of conductance levels of seven compared to only four levels observed in 250 nm technology.

The exploration of memristive chaotic system has garnered immense interest and attention in recent years. This paper endeavors to construct a novel four-dimensional memristive chaotic system by incorporating a third-order flux-controlled memristor into a simple chaotic system, leading to the emergence of chaos, periodicity and stability. The dynamic characteristics of the system are manifested through bifurcation diagrams, Lyapunov exponents and phase portraits. The most prominent feature of the system lies in its amplitude modulation, enabling it to achieve varying degrees of amplitude control by modulating the parameters. To validate its practical applicability, a cross-plane color image encryption algorithm has been devised and rigorously subjected to a comprehensive performance analysis. Numerical analysis results show that the NPCR and UACI are 99.6078% and 33.4661%, the correlation coefficients are close to 0, and the maximum information entropy reaches 7.9998. These results conclusively demonstrate the superior efficacy of the system in image encryption.

We investigate the directional localization properties of wave-functions in a two-dimensional tight-binding model with uniform hopping and correlated random on-site energies. By controlling the disorder correlation strength with a parameter (alpha ), we explore the effects of disorder on wave-function localization using Single-Particle Entanglement Entropy (SPEE) and Single-Particle Rényi Entropy (SPRE) at different values of q. Our analysis includes two distinct randomness structures: row-wise and fully correlated disorder. We find that row-wise disorder maintains maximal entanglement for horizontal cuts while enhancing horizontal spread for vertical cuts as (alpha ) increases. In contrast, fully correlated disorder leads to reduced vertical entanglement for horizontal cuts and increased horizontal entanglement for vertical cuts with rising (alpha ). Additionally, our results show that the difference between SPEE and SPRE provides valuable insights into localization behavior. These findings highlight the significance of directional properties in understanding localization transitions in disordered systems.

In this work, we investigate the statistical properties of drink serving in a nightclub bar, utilizing a stochastic model to characterize pedestrian dynamics within the venue. Our model comprises a system of n agents moving across an underlying square lattice of size l representing the nightclub venue. Each agent can exist in one of three states: thirsty, served, or dancing. The dynamics governing the state changes are influenced by a memory time, denoted as (tau ), which reflects their drinking habits. Agents’ movement throughout the lattice is controlled by a parameter (alpha ) which measures the impetus towards/away from the bar. We show that serving time distributions transition from a power-law-like to exponential and back to power-law as we increase (alpha ) starting from a pure random walk scenario ((alpha =0)). Specifically, when (alpha =0), a power-law distribution emerges due to the non-objectivity of the agents. As (alpha ) moves into intermediate values, an exponential behavior is observed, as it becomes possible to mitigate the drastic jamming effects in this scenario. However, for higher (alpha ) values, the power-law distribution resurfaces due to increased congestion. We also demonstrate that the average concentration of served, thirsty, and dancing agents provide a reliable indicator of when the system reaches a gridlock state. Subsequently, we construct comprehensive maps of the system’s stationary state, supporting the idea that for high densities, (alpha ) is not relevant, but for lower densities, the optimal values of measurements occurs at high values of (alpha ). To complete the analysis, we evaluate the conditional persistence, which measures the probability of an agent failing to receive their drink despite attempting to do so. In addition to contributing to the field of pedestrian dynamics, the present results serve as valuable indicators to assist commercial establishments in providing better services to their clients, tailored to the average drinking habits of their customers.

Data integrity and privacy are critical concerns in the financial sector. Traditional methods of data collection face challenges due to privacy regulations and time-consuming anonymization processes. In collaboration with Banco BV, we trained a hybrid quantum-classical generative adversarial network (HQGAN), where a quantum circuit serves as the generator and a classical neural network acts as the discriminator, to generate synthetic financial data efficiently and securely. We compared our proposed HQGAN model with a fully classical GAN by evaluating loss convergence and the MSE distance between the synthetic and real data. Although initially promising, our evaluation revealed that HQGAN failed to achieve the necessary accuracy to understand the intricate patterns in financial data. This outcome underscores the current limitations of quantum-inspired methods in handling the complexities of financial datasets.

Community structure is one of the most important characteristics of network, which can reveal the internal organization structure of nodes. Many algorithms have been proposed to identify community structures in networks. However, the classification accuracy of existing unsupervised community detection algorithms is generally low. Therefore, the semi-supervised community detection algorithm which can greatly improve the classification accuracy by introducing a small number of labeled nodes has attracted much attention. Nevertheless, previous studies were sketchy in terms of label rates and also ignored the variation of classification accuracy under different labeling strategies. In this paper, based on graph convolutional networks, we first study the effect of labeling strategies and label rates on classification accuracy in four real world networks in detail. The research phenomenon is counter-intuitive but surprisingly effective: the classification accuracy of labeling small-degree nodes or random-selection nodes is significantly higher than that of labeling high-degree nodes. The labeling strategies based on acquaintance immune algorithm also prove this result. The interesting question that arises is what topological properties of the network can lead to such results? So we test and verify it in two kinds of synthetic networks. It is found that the phenomenon which labeling small-degree nodes promotes classification accuracy can be observed when the degree distribution of the network follows power-law distribution and the ratio of the external edges of the community to the total edges of nodes in the network is small.

This work presents the development and implementation of an interdisciplinary and intermedial science outreach programme designed for school students. The programme integrates biological systems and technological advancements to provide students with hands-on laboratory experiences and immersive media, including virtual reality videos and augmented reality posters. Through a co-creation process involving scientists and educators, the programme aims to enhance students understanding of bio-inspired information pathways and neurogenesis. Preliminary evaluations indicate high engagement and educational value, suggesting that such interdisciplinary approaches can significantly enrich science education.