Computational Network Research Articles

Conductive Islands Assisted Resistive Switching in Biomimetic Artificial Synapse for Associative Learning and Image Recognition

AbstractSolution‐processed memristor devices hold significant potential for advancing synaptic applications, offering scalable, cost‐effective, and efficient solutions for next‐generation neuromorphic systems. These devices replicate the behavior of biological synapses with their gradual and continuous changes in resistance, making them promising for neuromorphic computing and artificial neural networks. However, understanding the temporal characteristics and cumulative effect of successive pulses on the devices is essential for emulating the dynamics of biological synapses. This study proposes a hybrid materials‐based conductive islands‐assisted resistive switching (CIARS) in a solution‐processed active layer consisting of thermally exfoliated graphitic carbon nitride (g‐C3N4 abbreviated as CN) nanosheets embedded with silver nanoparticles (Ag NPs). The fabricated devices demonstrate repeatable and bipolar memory features with a lower operating voltage (≤0.5 V), emulating the frequency and amplitude‐dependent synaptic plasticities. The threshold switching occurs due to the formation of conduction filaments (CFs) of silver ion (Ag+), whereas the analog behavior results from the voltage pulse‐dependent modulation of Schottky barrier height combined with metallic CFs formation. The experimental findings demonstrate the efficacy of the CIARS mechanism within the material platform, highlighting its potential for applications in associative learning, Morse code detection, and image recognition.

SD-Meta: The Software-Defined Network of Human-Centric Metaverse for Multi-Lead or Multi-Media Data in Spread Spectrum Communications

In this paper, the novel adjacent two-end link or network multi-end link concept of software-defined network is presented to support the data transmission of electroencephalogram, audio and video streaming in spread spectrum communications. Instead of solving the problem of software-defined prioritization scheme in networking calculation and communication, the southbound-northbound structure of existing neural or information network is found in control and perceptual interactions. The proposed framework of multi-lead and multi-media structures allow the human-centric Metaverse to execute different operations for local and global planning schemes with the high-speed lead or media data transmission of spread spectrum streaming, depending on the kind of brain-computer interaction, and similar to the system of multi-modal perception in the routing or switching. Firstly, this research designed the filed programmable gate array for conventional routing nodes in the computation of different neural networks. Secondly, this study demonstrates its enhanced applicability with software core for multiple routers in the computing power network of the Internet of brains in different brain-computer smart terminals. Experiment results also show the benefits of proposed model and structure, and exposing the different running indicators in the simulation.

Spatially varying nanophotonic neural networks.

The explosive growth in computation and energy cost of artificial intelligence has spurred interest in alternative computing modalities to conventional electronic processors. Photonic processors, which use photons instead of electrons, promise optical neural networks with ultralow latency and power consumption. However, existing optical neural networks, limited by their designs, have not achieved the recognition accuracy of modern electronic neural networks. In this work, we bridge this gap by embedding parallelized optical computation into flat camera optics that perform neural network computations during capture, before recording on the sensor. We leverage large kernels and propose a spatially varying convolutional network learned through a low-dimensional reparameterization. We instantiate this network inside the camera lens with a nanophotonic array with angle-dependent responses. Combined with a lightweight electronic back-end of about 2K parameters, our reconfigurable nanophotonic neural network achieves 72.76% accuracy on CIFAR-10, surpassing AlexNet (72.64%), and advancing optical neural networks into the deep learning era.

Peer Interaction Dynamics and Second Language Learning Trajectories During Study Abroad: A Longitudinal Investigation Using Dynamic Computational Social Network Analysis

AbstractUsing computational Social Network Analysis (SNA), this longitudinal study investigates the development of the interaction network and its influence on the second language (L2) gains of a complete cohort of 41 U.S. sojourners enrolled in a 3‐month intensive study‐abroad Arabic program in Jordan. Unlike extant research, our study focuses on students’ interactions with alma mater classmates, reconstructing their complete network, tracing the impact of individual students’ positions in the social graph using centrality metrics, and incorporating a developmental perspective with three measurement points. Objective proficiency gains were influenced by predeparture proficiency (negatively), multilingualism, perceived integration of the peer learner group (negatively), and the number of fellow learners speaking to the student. Analyses reveal relatively stable same‐gender cliques, but with changes in the patterns and strength of interaction. We also discuss interesting divergent trajectories of centrality metrics, L2 use, and progress; predictors of self‐perceived progress across skills; and the interplay of context and gender.

Semantic Guided Matting Net

Abstract Human matting refers to extracting human parts from natural images with high quality, including human detail information such as hair, glasses, hats, etc. This technology plays an essential role in image synthesis and visual effects in the film industry. When the green screen is not available, the existing human matting methods need the help of additional inputs (such as trimap, background image, etc.), or the model with high computational cost and complex network structure, which brings great difficulties to the application of human matting in practice. To alleviate such problems, we use a segmentation network as the foundation and use multiple branches to achieve human segmentation, contour detail extraction, and information fusion. We also propose a foreground probability map module, which uses the feature maps in the segmentation network to pre-estimate the foreground probabilities of each pixel and obtain Semantic Guided Matting Net. Under the condition that only a single image is needed as the input, the human matting task can be realized by making full use of the semantic information in the image. We validate our method on the P3M-10k dataset. Compared with the benchmark, our method has made significant improvements in various evaluation indicators.

Enhancing Urban Surveillance with Fog Computing, Mobile Cloud, and Big Data Analytics in 5G Networks

The new emerging applications in 5G network, in the context of the Internet of Everything (IoE), will introduce high mobility, high scalability, real-time, and low latency requirements that raise new challenges on the services being provided to the users. Fortunately, Fog Computing and Cloud Computing, with their service orchestration mechanisms offer virtually unlimited dynamic resources for computation, storage and service provision, that will effectively cope with the requirements of the forthcoming services. 5G will use the benefits of centralized high performance computing cloud centers, cloud and fog RANs and distributed peer-to-peer mobile cloud that will create opportunities for companies to deploy many new real-time services that cannot be delivered over current mobile and wireless networks. This paper evaluates a model for fog and cloud hybrid environment service orchestration mechanisms for 5G network in terms of energy efficiency per user for different payloads.

End-to-end latency upper bounds and service chain deployment algorithm based on industrial internet network

The diverse service requests in industrial Internet networks require flexible and efficient service chain deployment to ensure the quality of service (QoS). However, current deployment algorithms for service chains are primarily designed to guarantee only low end-to-end latency; they often overlook the amount of service chains that can be accommodated by the network and could lead to severe network load imbalances, significantly reducing service efficiency and causing serious network congestion issues. To address the above issues, we develop a mathematical model of the network topology and service request chains by integrating Network Function Virtualization (NFV) and Software Defined Networking (SDN). Utilizing network calculus theory, we derive the upper bound of end-to-end delay for service chain routing and analyzed the relationship between the upper bound of service chain routing delay and the resource allocation of Virtual Network Function (VNF) nodes. Based on the aforementioned model, we propose a novel service chain deployment algorithm named the Delay-Aware Load-Balanced Routing Algorithm (DLBRA). DLBRA comprehensively considers network traffic load balancing and end-to-end latency of service chains, rationally allocating VNF node resources to complete the determined service chain routing deployment. Experimental results indicate that, compared to the shortest path and load balancing algorithms, DLBRA not only ensures that the end-to-end delay of the service chain meets its QoS requirements, but also effectively reduces network load imbalance, significantly increasing the number of service chain requests that the network can accommodate. Additionally, DLBRA provides tailored deployment guidance for different types of service chains, such as latency-sensitive and data-intensive service chains, ensuring optimal utilization of network resources. This algorithm enhances the efficiency of service chain deployment in industrial internet scenarios and possesses broad application potential in other network environments where delay optimization and load balancing are critical, such as intelligent transportation, cloud computing, and 5G networks.

Distributed Computing in the Asynchronous LOCAL Model

The LOCAL model is among the main models for studying locality in the framework of distributed network computing. This model is however subject to pertinent criticisms, including the facts that all nodes wake up simultaneously, perform in lock steps, and are failure-free. We show that relaxing these hypotheses to some extent does not hurt local computing. In particular, we show that, for any task T associated to a locally checkable labeling (lcl), if T is solvable in t rounds by a deterministic algorithm in the LOCAL model, then T remains solvable by a deterministic algorithm in O(t) rounds in an asynchronous variant of the LOCAL model whenever t=O(polylogn). This improves the result by Castañeda et al. [TCS, 2019], which was restricted to 3-coloring the rings. More generally, the main contribution of this paper is to show that, perhaps surprisingly, asynchrony and failures in the computations do not restrict the power of the LOCAL model, as long as the communications remain synchronous and failure-free. To this end, this paper introduces a new distributed renaming technique to provide nodes with consistent identifiers.

Systemic risk in banking, fire sales, and macroeconomic disasters

We develop a dynamic computational network model of the banking system where fire sales provide the amplification mechanism of financial shocks. Each period a finite number of banks offers a large, but finite, number of loans to households. Banks with excess liquidity also offer loans to other banks with insufficient liquidity. Thus, each period an interbank loan market is endogenously formed. Bank assets are hit by idiosyncratic shocks drawn from a thin tailed distribution. The uneven distribution of shocks across banks implies that each period there are banks that become insolvent. If insolvent banks happen also to be heavily indebted to other banks, their liquidation can trigger other bank failures. We find that the distribution across time of the growth rate of banking assets has a ‘fat left tail’ that corresponds to rare economic disasters. We also find that the distribution of initial shocks is not a perfect predictor of economic activity; that is some of the uncertainty is endogenous and related to the structure of the interbank network.

Weighted Asymmetry Index: A New Graph-Theoretic Measure for Network Analysis and Optimization

Graph theory is a crucial branch of mathematics in fields like network analysis, molecular chemistry, and computer science, where it models complex relationships and structures. Many indices are used to capture the specific nuances in these structures. In this paper, we propose a new index, the weighted asymmetry index, a graph-theoretic metric quantifying the asymmetry in a network using the distances of the vertices connected by an edge. This index measures how uneven the distances from each vertex to the rest of the graph are when considering the contribution of each edge. We show how the index can capture the intrinsic asymmetries in diverse networks and is an important tool for applications in network analysis, optimization problems, social networks, chemical graph theory, and modeling complex systems. We first identify its extreme values and describe the corresponding extremal trees. We also give explicit formulas for the weighted asymmetry index for path, star, complete bipartite, complete tripartite, generalized star, and wheel graphs. At the end, we propose some open problems.

Controlled Spalling of 4H Silicon Carbide with Investigated Spin Coherence for Quantum Engineering Integration.

We detail scientific and engineering advances which enable the controlled spalling and layer transfer of single crystal 4H silicon carbide (4H-SiC) from bulk substrates. 4H-SiC's properties, including high thermal conductivity and a wide bandgap, make it an ideal semiconductor for power electronics. Moreover, 4H-SiC is an excellent host of solid-state atomic defect qubits for quantum computing and quantum networking. Because 4H-SiC substrates are expensive (due to long growth times and limited yield), techniques for removal and transfer of bulk-quality films are desirable for substrate reuse and integration of the separated films. In this work, we utilize updated approaches for stressor layer thickness control and spalling crack initiation to demonstrate controlled spalling of 4H-SiC, the highest fracture toughness crystal spalled to date. We achieve coherent spin control of neutral divacancy (VV0) qubit ensembles and measure a quasi-bulk spin T2 of 79.7 μs in the spalled films.

Advantages of Big Data Analysis and AI Technology in Data Collecting And Processing of Preventive Maintenance

In recent years, the emergence of Cyber-Physical systems (CPS), big data, cloud computing, and industrial wireless networks have promoted the development of Industry 4.0. Intelligent manufacturing systems are the leaders of this change. In smart manufacturing, the emphasis will be placed on using technologies such as big data analytics, cloud computing, edge computing, and artificial intelligence (AI). The advantages of new technologies are significantly reflected in the comparison of preventive maintenance and traditional maintenance based on intelligent technology: preventive maintenance significantly reduces the cost of prevention and, at the same time, dramatically improves the accuracy and timeliness of maintenance, bringing inestimable value to intelligent manufacturing. This paper will analyze new technologies’ innovative advantages and technical prospects through big data analysis and specific application scenarios of AI technology in preventive maintenance. Firstly, in data acquisition, this paper examines the architecture of preventive maintenance systems based on big data. Then, it analyzes the composition of the data pipeline: in the realization of data collection and processing in the field of cloud computing and edge computing, and the aspect of data processing, it discusses the participation of AI technology in data understanding and processing and the unprecedented changes it brings.

Fabric Defect Detection Method Based on Multi-scale Fusion Attention Mechanisms

Abstract Fabric defect detection is extremely important for the development of the textile industry, but the existing traditional image processing algorithms are not good enough to detect fabric defects, and the detection efficiency and accuracy of the classical deep learning model is not satisfactory, so this paper proposes an improved fabric defect detection method based on multi-scale fusion of attention mechanism YOLOv7-PCBS. Based on the YOLOv7 network structure, some of the standard convolutions of the backbone network are replaced with Partial Convolution (PConv) modules, which reduces the amount of network computation and improves the network detection speed; add Coordinate Attention (CA) to enhance the ability of extracting the positional features of tiny defects in fabrics; reconfiguration of the SPPCSPC module to improve small target detection; optimization of Bidirectional Feature Pyramid Network (BiFPN) and design of Tiny- BiFPN for simple and fast multi-scale feature fusion; finally, a novel loss function SIoU with angular loss is introduced to facilitate the fitting of the true and predicted frames and enhance the accuracy of defect prediction. The results show that the algorithm achieves a mAP value of 94.4% on the detection of defects in solid-colored fabrics of six denim materials, which is an improvement of 15.1% compared to the original YOLOv7 algorithm, while the model achieves a frame rate of 59.5 per second. Compared with other traditional deep learning algorithms SSD and Faster-RCNN, the detection accuracies are improved by 21.6% and 15.2%, and the FPS values are improved by 78.1% and 101.0%, respectively. Therefore, the YOLOv7-PCBS fabric defect detection algorithm proposed in this paper makes the fabric defect detection results more accurate while realizing lightweight, which provides an important technical reference for the subsequent improvement of textile quality.

Efficient Schemes for Optimizing Load Balancing and Communication Cost in Edge Computing Networks

Edge computing architectures promise increased quality of service with low communication delays by bringing cloud services closer to the end-users, at the distributed edge servers of the network edge. Hosting server capabilities at access nodes, thereby yielding edge service nodes, offers service proximity to users and provides QoS guarantees. However, the placement of edge servers should match the level of demand for computing resources and the location of user load. Thus, it is necessary to devise schemes that select the most appropriate access nodes to host computing services and associate every remaining access node with the most proper service node to ensure optimal service delivery. In this paper, we formulate this problem as an optimization problem with a bi-objective function that aims at both communication cost minimization and load balance optimization. We propose schemes that tackle this problem and compare their performance against previously proposed heuristics that have been also adapted to target both optimization goals. We study how these algorithms behave in lattice and random grid network topologies with uniform and non-uniform workloads. The results validate the efficiency of our proposed schemes in addition to the significantly lower execution times compared to the other heuristics.

Understanding trends, patterns, and dynamics in global company acquisitions: a network perspective

Studying acquisitions offers invaluable insights into startup trends, aiding informed investment decisions for businesses. However, the scarcity of studies in this domain prompts our focus on shedding light in this area. Employing Crunchbase data, our study delves into the global network of company acquisitions using diverse network analysis techniques. Our findings unveil an acquisition network characterized by a primarily sparse structure comprising localized dense connections. We reveal a prevalent tendency among organizations to acquire companies within their own country and industry, as well as those within the same age bracket. Furthermore, we show that the country, region, city, and category of the companies can affect the formation of acquisition relationships between them. Our temporal analysis indicates a growth in the number of weakly connected components of the network over time, accompanied by a trend toward a sparser network. Through centrality metrics computation in the cross-city acquisition network, we identify New York, London, and San Francisco as pivotal and central hubs in the global economic landscape. Finally, we show that the United States, United Kingdom, and Germany are predominant countries in international acquisitions. The insights from our research assist policymakers in crafting better regulations to foster global economic growth, and aid businesses in deciding which startups to acquire and which markets to target for expansion.

Utility optimization for computation offloading and splitting in time-varying HAP and LEO satellite integrated MEC networks

To provide ubiquitous and low-latency communication and computation services for remote and disaster areas, high altitude platform (HAP) and low earth orbit (LEO) satellite integrated multi-access edge computing (HLS-MEC) networks have emerged as a promising solution. However, most current studies directly assume that the number of connected satellites is fixed and neglect the modeling of the time-varying multi-satellite computing process. Motivated by this, we establish an M/G/K(t) queuing model to illustrate task computation on satellites. To evaluate the efficiency and quality of computation offloading and splitting, we develop a utility model. This model is defined as a difference between a value function that assesses the trade-offs of task offloading, considering latency reductions and energy savings, and a cost function that quantifies expenses related to latency and energy consumption. After formulating the utility maximization problem, we propose the deep reinforcement learning-based offloading and splitting (DBOS) scheme that can overcome the time-varying uncertainties and high dynamics in the HLS-MEC network. Specifically, the DBOS scheme can learn the best computation offloading and splitting policy to maximize the utility by sensing the number of connected satellites, the distance between the HAP and satellites, the available computing resources, and the task arrival rate. Finally, we evaluate and validate the computational complexity and convergence property of the DBOS scheme. Numerical results show that the DBOS scheme outperforms the other three benchmarks and maximizes the utility under time-varying dynamics.

Competitive interactions shape brain dynamics and computation across species.

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brain's network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.

ZnO-based artificial synaptic diodes with zero-read voltage for neural network computing

Brain-inspired neuromorphic sensory devices play a crucial role in addressing the limitations of von Neumann systems in contemporary computing. Currently, synaptic devices rely on memristors and thin-film transistors, requiring the establishment of a read voltage. A built-in electric field exists within the p–n junction, enabling the operation of zero-read-voltage synaptic devices. In this study, we propose an artificial synapse utilizing a ZnO diode. Typical rectification curves characterize the formation of ZnO diodes. ZnO diodes demonstrate distinct synaptic properties, including paired-pulse facilitation, paired-pulse depression, long-term potentiation, and long-term depression modulations, with a read voltage of 0 V. An artificial neural network is constructed to simulate recognition tasks using MNIST and Fashion-MNIST databases, achieving test accuracy values of 92.36% and 76.71%, respectively. This research will pave the way for advancing zero-read-voltage artificial synaptic diodes for neural network computing.

Retraction Note: Soft computing and trust-based self-organized hierarchical energy balance routing protocol (TSHEB) in wireless sensor networks

Retraction Note: Soft computing and trust-based self-organized hierarchical energy balance routing protocol (TSHEB) in wireless sensor networks

Transformation of Digital Health: Advancing Telerehabilitation with Smartwatch Technology

The emergence of innovative electronic devices hasrevolutionized the role of medicine, specificallytelemedicine and telerehabilitation. A smartwatch is similarin shape to a traditional wristwatch with a built-in,networked computer with an array of sensors. The use ofsmartwatches has provided healthcare workers with newopportunities for monitoring and managing health.Monitoring of vitals like heart rate, blood pressure, andoxygen saturation, has been made possible by the help ofsmartwatches. These features provide the wearer with areal-time picture of their health. Monitoring of vital healthparameters over time can help detect irregularities inhealth status and can also be used for remote patientmonitoring. This enables healthcare professionals tomonitor their patient's health from anywhere.Smartwatches are being used in physical rehabilitationnowadays for monitoring vitals during physical therapyand tracking the progress of the wearer. This mini-reviewdiscusses insights into the benefits of using smartwatchesin healthcare, and focuses on the evolving role ofsmartwatches in telerehabilitation.