Scalable Network Research Articles

Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light

Optical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches. This study introduces a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively. Our framework is designed for real-time, parallelized operations, leveraging 2D arrays of LEDs and photodetectors connected via independent analog electronics. We experimentally demonstrate this approach using a system with a three-layer network with two hidden layers and operate it to recognize images from the MNIST database with a recognition accuracy of 92% and classify classes from a nonlinear spiral data with 86% accuracy. By implementing multiple layers of a deep neural network simultaneously, our approach significantly reduces the number of read-ins and read-outs required and paves the way for scalable optical accelerators requiring ultra low energy.

Reconstruction of High-Resolution Solar Spectral Irradiance Based on Residual Channel Attention Networks

The accurate measurement of high-resolution solar spectral irradiance (SSI) and its variations at the top of the atmosphere is crucial for solar physics, the Earth’s climate, and the in-orbit calibration of optical satellites. However, existing space-based solar spectral irradiance instruments achieve high-precision SSI measurements at the cost of spectral resolution, which falls short of meeting the requirements for identifying fine solar spectral features. Therefore, this paper proposes a new method for reconstructing high-resolution solar spectral irradiance based on a residual channel attention network. This method considers the stability of SSI spectral features and employs residual channel attention blocks to enhance the expression ability of key features, achieving the high-accuracy reconstruction of spectral features. Additionally, to address the issue of excessively large output features from the residual channel attention blocks, a scaling coefficient adjustment network block is introduced to achieve the high-accuracy reconstruction of spectral absolute values. Finally, the proposed method is validated using the measured SSI dataset from SCIAMACHY on Envisat-1 and the simulated dataset from TSIS-1 SIM. The validation results show that, compared to existing scaling coefficient adjustment algorithms, the proposed method achieves single-spectrum super-resolution reconstruction without relying on external data, with a Mean Absolute Percentage Error (MAPE) of 0.0302% for the reconstructed spectra based on the dataset. The proposed method achieves higher-resolution reconstruction results while ensuring the accuracy of SSI.

Future-Proof Networks: NFV’S Role In 5g and Beyond

Riding the wave of the multifaceted rapid change in communication technologies and universal deployment of 5G, Network Functions Virtualization, or NFV, has emerged as a disruptive framework for adaptable, scalable, and cost-efficient networks. This can be because NFV decouples network functions from specialized hardware, affording the flexibility that addresses growing demands in ultra-reliable low-latency communications, enhanced mobile broadband, and massive machine-type communications. These capabilities are essential for meeting 5G’s requirements and preparing for the more complex challenges anticipated with 6G. As 6G transition begins, the role of NFV has to be reassessed in order to meet the increased challenges for ultra-low latency, massive connectivity, and unprecedented network reliability. This paper considers the ability of NFV to evolve with new networking standards by unpacking its potentials, limitations, and opportunities for improvement. This research determines factors critical to the scalability, flexibility, and efficiency of NFV in various network scenarios through simulations, case studies, and performance metric analyses. In doing this, it raises the need for advancing NFV architecture to stay relevant. Rather than only looking at current applications, this work addresses new future needs and maybe even answers issues for future stringent network standards that do not compromise on profitability. Such insights will be useful for developing future-proof infrastructures, positioning NFV as a cornerstone in nextgeneration networks such as 6G and beyond.

A few-shot oil spill segmentation network guided by multi-scale feature similarity modeling

Segmentation of oil spills with few-shot samples using UAV optical and SAR images is crucial for enhancing the efficiency of oil spill monitoring. Current oil spill semantic segmentation predominantly relies on SAR images, rendering it relatively data-dependent. We propose a flexible and scalable few-shot oil spill segmentation network that transitions from UAV optical images to SAR images based on the image similarity of oil spill regions in both types of images. Specifically, we introduce an Adaptive Feature Enhancement Module (AFEM) between the support set branch and the query set branch. This module leverages the precise oil spill information from the UAV optical image support set to derive initial oil spill templates and subsequently refines and updates the query oil spill templates through training to guide the segmentation of SAR oil spills with limited samples. Additionally, to fully exploit information from both low and high-level features, we design a Feature Fusion Module (FFM) to merge these features. Finally, the experimental results demonstrate the effectiveness of our network in enhancing the performance of UAV optical-to-SAR oil spill segmentation with few samples. Notably, the SAR oil spill detection accuracy reaches 75.88% in 5-shot experiments, representing an average improvement of 5.3% over the optimal baseline model accuracy.

A Study on the Mechanisms Influencing Youth Intermittent Disengagement Behavior on Short Video PlatformsBased on the Moderating Role of Self-Control Ability

As a product of the technological revolution in information media, short video platforms feature an increasingly complex scale of information, functional settings, and social networks. These complexities pose risks of perceived overload on the user level, leading to negative usage behaviors such as usage fatigue and intermittent disengagement. Based on the Stimulus-Organism-Response (S-O-R) theoretical framework, this study constructs a model of the mechanisms influencing intermittent disengagement behaviors among young users on short video platforms. Innovatively, the study introduces the moderating role of self-control theory. Data were collected through an online questionnaire, yielding 423 valid samples, and analyzed using structural equation modeling, mediation effect modeling, and moderation effect modeling. Results indicate that perceived information overload has a significant positive impact on the occurrence of intermittent disengagement among youth; usage fatigue partially mediates the pathway from perceived information overload to intermittent disengagement and fully mediates the pathway from perceived functional overload to intermittent disengagement; self-control ability positively moderates the relationship between usage fatigue and intermittent disengagement.

Cavity-mediated iSWAP oscillations between distant spins

AbstractDirect interactions between quantum particles naturally fall off with distance. However, future quantum computing architectures are likely to require interaction mechanisms between qubits across a range of length scales. In this work, we demonstrate a coherent interaction between two semiconductor spin qubits 250 μm apart using a superconducting resonator. This separation is several orders of magnitude larger than for the commonly used direct interaction mechanisms in this platform. We operate the system in a regime in which the resonator mediates a spin–spin coupling through virtual photons. We report the anti-phase oscillations of the populations of the two spins with controllable frequency. The observations are consistent with iSWAP oscillations of the spin qubits, and suggest that entangling operations are possible in 10 ns. These results hold promise for scalable networks of spin qubit modules on a chip.

Scalability and Security in Blockchain Networks: Evaluation of Sharding Algorithms and Prospects for Decentralized Data Storage

This article addresses the issues of scalability and security in blockchain networks, with a focus on sharding algorithms and decentralized data storage. Key challenges include the low throughput and high transaction latency in public networks such as Bitcoin and Ethereum. Sharding is examined as a method to enhance performance through data distribution, but it raises concerns regarding node management and reliability. Sharding schemes, such as Elastico, OmniLedger, Pyramid, RepChain, and SSchain, are analyzed, each presenting its own advantages and drawbacks. Alternative architectures like Directed Acyclic Graphs (DAGs) demonstrate potential for improved scalability but require further refinement to ensure decentralization and security. Protocols such as Brokerchain, Meepo, AHL, Benzene, and CycLedger offer unique approaches to addressing performance and transaction consistency issues. This article emphasizes the need for a comprehensive approach, including dynamic sharding, multi-level consensus, and inter-shard coordination. Additionally, a conceptual model is proposed that incorporates the sharding of transactions, states, and networks, which enables greater scalability and efficiency.

Fault Tolerance Management Implementation from Medium-to-Large-Scale Networks

As network infrastructures grow in complexity, ensuring high availability and resilience becomes critical, especially for medium-to-large scale networks. This study focuses on the development and implementation of fault tolerance management within Software-defined networking (SDN) environments, aimed at minimizing downtime and enhancing network reliability. SDN’s centralized control and dynamic programmability provide an ideal framework for implementing efficient fault detection and recovery mechanisms. The proposed model leverages real-time monitoring, redundancy protocols, and adaptive rerouting strategies to mitigate the impact of node or link failures. Key components of the model include failover mechanisms, load balancing, and traffic rerouting algorithms, designed to maintain seamless network operations during failures. Through simulation and testing, the model demonstrates significant improvements in network resilience, reducing recovery time and ensuring uninterrupted service delivery. This research provides a comprehensive guide to implementing fault-tolerant networks using SDN, offering scalable solutions that can be adapted to various network sizes and configurations. The findings emphasize the potential of SDN to revolutionize fault management in modern network infrastructures, making it a crucial consideration for future network design and operations.

Software-Based Security Framework for Edge andMobile IoT

With the proliferation of Internet of Things (IoT) devices, ensuring secure communications has become imperative. Due to their low cost and embedded nature, many of these devices operate with computational and energy constraints, neglecting the potential security vulnerabilities that they may bring. This work-in-progress is focused on designing secure communication among remote servers and embedded IoT devices to balance security robustness and energy efficiency. The proposed approach uses lightweight cryptography, optimizing device performance and security without overburdening their limited resources. Our architecture stands out for integrating Edge servers and a central Name Server, allowing secure and decentralized authentication and efficient connection transitions between different Edge servers. This architecture enhances the scalability of the IoT network and reduces the load on each server, distributing the responsibility for authentication and key management.

Deep Reinforcement Learning and Enhanced Optimization for Real-Time Energy Management in Wireless Sensor Networks

Constraints are a major issue in radio-based communication in Wireless Sensor Networks, where each sensor node has a limited amount of power. Conventional clustering and optimization methods have been inappropriate for dynamic conditions which lead to timely energy drainage and reduce the network lifetime. In this research, the novel Deep Reinforcement Learning-Enhanced Hybrid African Vulture and Aquila Optimizer has been proposed that optimizes the dynamic clustering and energy-based parameters in real time. The proposed model is designed for optimizing the Wireless Sensor Networks, by including Deep Reinforcement Learning to adjust the dynamic formation of the base of the cluster on real-time data which leads to efficient energy utilization among all the sensor nodes. It combines the best properties of the Aquila and African Vulture Optimizer to optimize the network lifetime and energy consumption. The network lifetime, which is one of the most crucial characteristics, is optimized by using the global search algorithm of African Vulture Optimiser. In contrast, it is optimized by the localized search of Aquila optimizer to reduce energy consumption. The presented novel African Vulture and Aquila model outperforms the existing methods used convention-based optimization methods. It shows a 20% improvement in energy efficiency and faster convergence with better robustness while keeping the network scalability. The proposed approach is perfectly suited for the scalable WSNs which are mainly used in the environment such as smart cities and IoT systems where a timely adaptation process is inevitable.

Robust and energy-efficient RPL optimization algorithm with scalable deep reinforcement learning for IIoT

The increasing complexity and quantity of the Industrial Internet of Things (IIoT) pose new challenges to the traditional routing protocol for low-power and lossy networks (RPL) in terms of dynamic management, data transmission reliability, and energy efficiency optimization. This paper proposes a scalable deep reinforcement learning (DRL) algorithm with a multi-attention actor double critic model for routing optimization (MADC) to meet the requirements of IIoT for efficient and intelligent routing decisions while improving data transmission reliability and energy efficiency. Specifically, MADC employs the centralized training and decentralized execution (CTDE) learning paradigm to decouple the model’s training and inference tasks, which reduces the difficulty and computational cost of model learning and improves the training efficiency. In addition, a lightweight actor network based on multi-scale convolutional attention mechanism is designed in MADC, which can provide intelligent and real-time decision-making capabilities for resource-constrained nodes with low computational and storage complexities. Moreover, a scalable critic network utilizing multiple attention mechanisms is proposed. It is not only suitable for dynamic and changing network environments but also can more comprehensively and accurately evaluate local observation states, providing more accurate and efficient guidance for model optimization. Furthermore, MADC incorporates a double critic network architecture to mitigate potential overestimation issues during training, thereby ensuring the model’s robustness and reliability. Simulation results demonstrate that MADC outperforms existing RPL optimization algorithms in terms of energy efficiency, data transmission reliability, and adaptability.

Accurate and Scalable Graph Convolutional Networks for Recommendation Based on Subgraph Propagation

Accurate and Scalable Graph Convolutional Networks for Recommendation Based on Subgraph Propagation

A Unified Federated Deep Q Learning Caching Scheme for Scalable Collaborative Edge Networks

A Unified Federated Deep Q Learning Caching Scheme for Scalable Collaborative Edge Networks

Dual multi scale networks for medical image segmentation using contrastive learning

Dual multi scale networks for medical image segmentation using contrastive learning

Explaining how a psychosocial intervention (PROACTIVE) based on behavioural activation improved outcomes of depression in older adults living in deprived regions of Brazil: The mediating roles of reduced loneliness and stepped care

BackgroundThe PROACTIVE trial was a task-shared, stepped and collaborative care, psychosocial intervention based on psychoeducation and behavioural activation in 715 older adults (60–94 years; mean (SD) 68·6 (6.9) years; 74·1 % female), that was highly effective at improving recovery from depression among older adults in Brazil. Here we investigate mediators of the intervention's effectiveness. MethodsCausal mediation analysis using interventional indirect effects, simultaneously decomposed the total effect of PROACTIVE on recovery from depression (PHQ-9 < 10) into multiple indirect effects including: dose of intervention (numbers of sessions and activities completed); social support (Luben Social Network Scale); perceived loneliness (UCLA questionnaire); and additional sessions offered to participants who did not respond during the initial phase of the stepped care intervention. ResultsOf the intervention's total effect (difference in probability of recovery from depression between the intervention and control arms 0·216 [bias-corrected 95 % CI: 0·149, 0·291]): 13 % was mediated through reduced loneliness (0·028 [0·013, 0·046]); and 25 % through attending additional sessions for participants who did not initially respond to the intervention (0·055 [0·007, 0·102]). LimitationsDue to limitations in our sample size our study may lack power to detect some nuances such as interactions between different mediators. ConclusionsOur findings emphasise the importance of a home-based intervention to improve depression outcomes where participants are encouraged to self-select activities to mitigate against loneliness. Importantly, our findings suggest that the intervention's stepped-care component offering additional sessions to participants who did not experience an early response shows promise in ensuring a sustained recovery from depression.

An Extensive Analysis of Current Software Defined Network Optimization Techniques

In the modern, technologically advanced era, the use of devices connected to the Internet has increased exponentially. This has led to a significant increase in Internet activity. Network scalability, low levels of service, and the need to manage a large number of clients on a single server (which can lead to denial of service (DOS) attacks) are just a few of the consequences of increased traffic if you are a load balancer, . A method proposed in the literature. Although load balancers are popular, studies have shown that they have a few drawbacks, such as not being programmable and exclusive to a single vendor to overcome these issues and the subsequent increase in Internet traffic saw the emergence of Software Defined Networking (SDN) as a paradigm shift. Software defined networking (SDN) enables programmable load balancers and gives customers the freedom to develop and implement their own load-balancing algorithms this exam covers the origins of software-defined networking (SDN) and Open Flow, with impact on weight and density so included. In this article, we will go through various SDN load balancing techniques. This review builds on existing research challenges, their answers, and possible future research directions. Load balancing in intelligent software-defined (SDN) algorithms is typically performed using mathematical tools. Finally, we detail the metrics used to evaluate the performance of the algorithms.

Integrated spectrally multiplexed light–matter interface at telecom band

The light–matter interface is an important building block for long-distance quantum networks. Towards a scalable quantum network with high-rate quantum information processing, it requires to develop integrated light–matter interfaces with broadband and multiplexing capacities. Here we demonstrate a light–matter interface at the telecom band in an integrated system. A five-spectral-channel atomic-frequency-comb photonic memory is prepared on a laser-written Er3+:LiNbO3 chip. The bandwidth of each channel is 4 GHz with a channel spacing of 15 GHz. The signal photons from time-bin entangled photon pairs at the telecom band are sent into the on-chip memory and recalled after a storage time of 152 ns. The entanglement-preserving nature of our integrated quantum interface is assessed by an input/output fidelity of &gt;92% for all five spectral channels. Our light–matter interfaces constitute a notable step forward toward a high-rate quantum network involving integrated devices.

Greenhouse gas emission driving factors and reduction strategies for operational phase of urban rail transit in China

AbstractTo promote the sustainable development of urban rail transit (URT) in China, it is necessary to quantitatively analyze the greenhouse gas (GHG) emissions and its internal driving factors. Based on the LMDI algorithm, this research constructs an assessment model and makes a quantitative analysis of internal driving factors for GHG emissions in the operational phase of the URT system in China. According to the official investigated data, the GHG emission of URT in the operational phase is between 600 × 104 and 2000 × 104 tons of carbon dioxide equivalent (CO2e) per year from 2012 to 2023. The results of the driving factors decomposition showed that the passenger demand intensity was the key and positive factor affecting carbon emissions in the operational phase and the contribution rate was 12.93%–60.81% from 2013 to 2023. Therefore, it is necessary to formulate targeted policies in terms of management and technical means to reduce the average traveling distance demanded by passengers and reasonably control the scale of the line network for GHG reduction in the Chinese URT system.

A Caching-based Framework for Scalable Temporal Graph Neural Network Training

Representation learning over dynamic graphs is critical for many real-world applications such as social network services and recommender systems. Temporal graph neural networks (T-GNNs) are powerful representation learning methods and have demonstrated remarkable effectiveness on continuous-time dynamic graphs. However, T-GNNs still suffer from high time complexity, which increases linearly with the number of timestamps and grows exponentially with the model depth, making them not scalable to large dynamic graphs. To address the limitations, we propose Orca , a novel framework that accelerates T-GNN training by caching and reusing intermediate embeddings. We design an optimal caching policy, named MRD , for the uniform cache replacement problem, where embeddings at different intermediate layers have identical dimensions and recomputation costs. MRD not only improves the efficiency of training T-GNNs by maximizing the number of cache hits but also reduces the approximation errors by avoiding keeping and reusing extremely stale embeddings. For the general cache replacement problem, where embeddings at different intermediate layers can have different dimensions and recomputation costs, we solve this NP-hard problem by presenting a novel two-stage framework with approximation guarantees on the achieved benefit of caching. Furthermore, we have developed profound theoretical analyses of the approximation errors introduced by reusing intermediate embeddings, providing a thorough understanding of the impact of our caching and reuse schemes on model outputs. We also offer rigorous convergence guarantees for model training, adding to the reliability and validity of our Orca framework. Extensive experiments have validated that Orca can obtain two orders of magnitude speedup over state-of-the-art T-GNNs while achieving higher precision on various dynamic graphs.

A composite scaling network of EfficientNet for improving spatial domain identification performance

Spatial Transcriptomics leverages gene expression profiling while preserving spatial location and histological images. However, processing the vast and noisy image data in spatial transcriptomics (ST) for precise recognition of spatial domains remains a challenge. In this study, we propose a method of EfNST for recognizing spatial domains, which employs an efficient composite scaling network of EfficientNet to learn multi-scale image features. Compared with other relevant algorithms on six data sets from three sequencing platforms, EfNST exhibits higher accuracy in discerning fine tissue structures, highlighting its strong scalability to data and operational efficiency. Under limited computing resources, the testing results on multiple data sets show that the EfNST algorithm runs faster while maintaining accuracy. The ablation studies of EfNST model demonstrate the significant effectiveness of the EfficientNet. Within the annotated data sets, EfNST showcases the ability to finely identify subregions within tissue structure and discover corresponding marker genes. In the unannotated data sets, EfNST successfully identifies minute regions within complex tissues and elucidated their spatial expression patterns in biological processes. In summary, EfNST presents a novel approach to inferring cellular spatial organization from discrete data spots with significant implications for the exploration of tissue structure and function.