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2024 Frontiers in Optics, FiO 2024

作者： Wang, Yuetian Liu, Yuanpeng Geng, Wenpu Zhao, Wenqian Pan, Zhongqi Yue, Yang School of Information and Communications Engineering Xi'an Jiaotong University Xi'an710049 China School of Opto-Electronics Information Science and Engineering Xi'an Jiaotong University Xi'an710049 China Institute of Modern Optics Nankai University Tianjin300350 China Department of Electrical & Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States

Trench-assisted seven-core graded-index ring fiber is proposed and designed to support 210 OAM modes in the C-band with > 99.27% purity. It features with © Optica Publishing Group 2024, © 2024 The Author(s)

关键词： Optical depth

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Enhancing WSN Performance with DRL-CaCC: An Adaptive Cache-Aware Congestion Control Mechanism

Enhancing WSN Performance with DRL-CaCC: An Adaptive Cache-A...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Melchizedek Alipio Miroslav Bures Department of Electronics and Computer Engineering De La Salle University Malate Manila Philippines Department of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague Prague 2 Czech Republic

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Traditionally, rule-based is the predominant approach in Wireless Sensor Networks (WSN) communication protocols for managing caching and congestion control that relies on explicitly defined, unchanging models. A growing trend has recently been toward incorporating adaptive methods that leverage machine learning (ML) and deep learning (DL) during network congestion conditions. However, the design of an adaptive cache-aware congestion control mechanism in WSN using Deep Reinforcement Learning (DRL) is yet to be explored. Hence, this work developed a DRL-based adaptive cache-aware congestion control mechanism called DRL-CaCC to alleviate WSN during congestion scenarios. DRL-CaCC employs interme-diate caching parameters as its state space and adaptively moves the congestion window as its action space. It aims to find the optimal congestion window movement to avoid further network congestion while ensuring maximum cache utilization. Results show that DRL-CaCC achieved an average improvement gain from 10% to 40% compared to two other cache-based congestion control mechanisms in terms of cache utilization, throughput, and end-to-end delay metrics.

关键词： Measurement Wireless sensor networks Adaptive systems Protocols Aerospace electronics Throughput Market research Deep reinforcement learning Information and communication technology Delays

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LSTM Network Assisted Content Caching at the Edge for Video on Demand

LSTM Network Assisted Content Caching at the Edge for Video ...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMla), IEEE International Conference on

作者： Mahmoud Darwich Magdy Bayoumi Department of Mathematics and Computer Science University of Mount Union Alliance OH Department of Electrical and Computer Engineering University of Louisiana Lafayette LA

Video-on-demand (VoD) services often suffer from long start-up delays and poor streaming quality due to distance from centralized data centers. In this work, we develop a long short-term memory (LSTM) network to predict future video requests and intelligently cache content at the edge for optimized VoD delivery. We train the LSTM model on a large proprietary dataset of VoD access logs capturing real-world sequential patterns. Using caching simulations, our approach increases cache hit ratios by 56% compared to recency-based caching and 41% over popularity-based schemes. By proactively caching predicted videos at the edge, our method reduces average video startup time by 32% and decreases upstream bandwidth consumption by 48%. These substantial gains demonstrate the value of forecasting VoD popularity using LSTMs and harnessing edge computing to cache content closer to viewers. Our intelligent edge caching system can improve quality of experience for users and lower costs for providers.

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TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

TSEML: A task-specific embedding-based method for few-shot c...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ran Su Rui Shi Hui Cui Ping Xuan Chengyan Fang Xikang Feng Qiangguo Jin School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China School of Software Northwestern Polytechnical University Shaanxi China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University Guangdong China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA FewShot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification.

关键词： Metalearning Deep learning Knowledge engineering Medical treatment Benchmark testing Bioinformatics Cancer

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TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes

arXiv

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arXiv 2024年

作者： Su, Ran Shi, Rui Cui, Hui Xuan, Ping Fang, Chengyan Feng, Xikang Jin, Qiangguo School of Computer Software College of Intelligence and Computing Tianjin University Tianjin China School of Software Northwestern Polytechnical University Shaanxi China Department of Computer Science and Information Technology La Trobe University Melbourne Australia Department of Computer Science School of Engineering Shantou University Guangdong China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China

Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtypes remains a significant challenge for conventional data-intensive deep learning approaches. In this work, we focus on the few-shot molecular subtype prediction problem in heterogeneous and small cancer datasets, aiming to enhance precise diagnosis and personalized treatment. We first construct a new few-shot dataset for cancer molecular subtype classification and auxiliary cancer classification, named TCGA Few-Shot, from existing publicly available datasets. To effectively leverage the relevant knowledge from both tasks, we introduce a task-specific embedding-based meta-learning framework (TSEML). TSEML leverages the synergistic strengths of a model-agnostic meta-learning (MAML) approach and a prototypical network (ProtoNet) to capture diverse and fine-grained features. Comparative experiments conducted on the TCGA Few-Shot dataset demonstrate that our TSEML framework achieves superior performance in addressing the problem of few-shot molecular subtype classification. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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An innovative evaluation of microstructure and tribological behavior of AISI 1060 and CuZn37Pb2 under laboratory and complex working conditions

Tribologia: Finnish Journal of Tribology

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Tribologia: Finnish Journal of Tribology 2024年第3期41卷 16-36页

作者： Bougoffa, M. Seyf Eddine Benchaa, Sayhia Abdelhak, Hadibi Ouissem, Benmesbah Si-Hem, Benkhaled Yasmine, labiod Laboratory of Advanced Technologies in Mechanical Production Department of Mechanical Engineering University of Badji Mokhtar Laboratory LMI Annaba Algeria Faculté des Mathématiques et des Sciences de la Matière Université de Ouargla Ouargla30000 Algeria Laboratory Networks and Systems Laboratory-LRS Department of computer science University of Badji Mokhtar P.o.Box 12 Annaba23000 Algeria

Wear is a significant industrial issue caused by the interaction of multiple complex factors rather than solely by material properties. CuZn37Pb2 and AISI 1060 steel are particularly susceptible to wear due to extensive industrial applications. This study developed a wear test tool, machined on a horizontal lathe, for testing under dry and lubricated conditions. A tribological comparison was conducted between the lathe test and a tribometer, examining factors like surface roughness, load, sliding speed, wear track diameter, track width, contact temperature, wear loss, and wear rate relative to the friction coefficient. Experiments were performed with torques ranging from 25 to 100 N, speeds of 0.30, 0.40, and 0.50 m/s, and wear track diameters of 4, 6, 8, and 10 mm. Worn surfaces and wear tracks were analyzed using optical microscopy and SEM-EDS. The influence of temperature (50 °C to 200 °C) on friction properties was studied, showing that sample morphology and test type greatly affect tribological response. Despite a wear rate calculation error below 8.12%, results indicated differences between laboratory and real-world tribological responses. This study enhances wear understanding by examining numerous previously unstudied characteristics and shows that, although wear cannot be entirely eliminated, it can be significantly minimized. laboratory testing provides prototypes for industrial challenges, effectively linking academic research with industry needs. © 2024 The Authors.

关键词： Tribology

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Error-Correcting Codes for Combinatorial Composite DNA

Error-Correcting Codes for Combinatorial Composite DNA

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IEEE International Symposium on Information Theory

作者： Omer Sabary Inbal Preuss Ryan Gabrys Zohar Yakhini Leon Anavy Eitan Yaakobi Faculty of Computer Science Technion-Israel Institute of Technology Haifa Israel Faculty of Computer Science Reichman University Hezliya Israel Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Data storage in DNA is developing as a possible solution for archival digital data. Recently, to further increase the potential capacity of DNA-based data storage systems, the combinatorial composite DNA synthesis method was suggested. This approach extends the DNA alphabet by harnessing short DNA fragment reagents, known as shortmers. The shortmers are building blocks of the alphabet symbols, each consisting of a fixed number of shortmers. Thus, when information is read, it is possible that one of the shortmers that forms part of the composition of a symbol is missing and therefore the symbol cannot be determined. In this paper, we model this type of error as a type of asymmetric error and propose code constructions that can correct such errors in this setup. We also provide a lower bound on the redundancy of such error-correcting codes and give an explicit encoder and decoder for our construction. Our suggested error model is also supported by an analysis of data from actual experiments that produced DNA according to the combinatorial scheme. lastly, we also provide a statistical evaluation of the probability of observing such error events, as a function of read depth.

关键词： Buffer storage Redundancy DNA Symbols Memory Probability Data models

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Orchestrating the Resilience of Cloud Microservices Using Task-Based Reliability and Dynamic Costing

Orchestrating the Resilience of Cloud Microservices Using Ta...

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computer science and Data engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Vyas O'Neill Ben Soh Department of Computer Science and Information Technology La Trobe University Melbourne Australia

In recent years, cloud computing has grown in popularity as enterprises realize the technical and economic benefits of managing resources using an on-demand model. With this growth come user expectations of quality of service, availability and reliability. As the trend in cloud systems architecture moves from monolithic systems to microservices, opportunities exist to develop granular solutions for improving the reliability of the overall system. In this paper we propose an approach to the resilience of microservices-based cloud systems using task-based reliability, quantifying of the reliability requirement with respect to the marginal cost of additional redundancy and the cost of failure. Our approach enables the cloud orchestrator to manage replication of microservice containers to reach an optimal, cost-effective level of redundancy. Empirical testing showed our approach achieved a lower overall cost in comparison with a fixed-reliability approach under an exponential failure function.

关键词： Cloud computing Costs Costing Redundancy Microservice architectures Reliability engineering Reliability

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Dynamic Resource Allocation in Network Slicing with Deep Reinforcement Learning

Dynamic Resource Allocation in Network Slicing with Deep Rei...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yue Cai Peng Cheng Zhuo Chen Wei Xiang Branka Vucetic Yonghui Li School of Electrical and Information Engineering The University of Sydney Australia Department of Computer Science and Information Technology La Trobe University Australia CSIRO Data61 Australia

Network slicing is key to enabling 6G and beyond networks to simultaneously meet the diverse quality of service (QoS) requirements of various services. In network slicing, radio access network (RAN) slicing is essential to establish a functional network slice by connecting mobile devices and mapping virtualized resource units to different slices. This demands an efficient resource allocation scheme that maximizes resource utilization while meeting diverse QoS requirements. In this paper, we propose a new dynamic resource allocation framework that encompasses three types of services. We formulate a dynamic resource allocation problem that features a mixed action space and has both long-term power and instantaneously available resource unit constraints. We propose a deep reinforcement learning (DRL)-based approach referred to as prediction-aided weighted DRL (PW-DRL), which infers the power allocation and user acceptance decisions to maximize a predefined reward function. Additionally, we propose a prediction network that significantly improves the DRL learning process under limited resources by supplying future state information. Simulation results validate that our proposed PW-DRL significantly outperforms state-of-art DRL approaches by achieving the highest long-term reward and fastest convergence.

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Automated Machine Learning in Critical Energy Infrastructure for Net Zero Carbon Emissions

Automated Machine Learning in Critical Energy Infrastructure...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Harsha Moraliyage Dilantha Haputhanthri Chamod Samarajeewa Nishan Mills Daswin De Silva Milos Manic Andrew Jennings Centre for Data Analytics and Cognition La Trobe University Melbourne Australia Department of Computer Science Virginia Commonwealth University Richmond USA

Increasing energy consumption and diversified energy generation have led to the proliferation of energy management (EM) systems for optimized grid operations and net zero carbon emissions. Artificial Intelligence (AI) is centric to EM systems to process large volumes of high velocity data for anomalies, predictions, optimization and other actionable insights that unravel the complexities of grid operations. Managing Energy AI capabilities itself is becoming an increasingly complex task that requires extensive resourcing and expertise. In this paper, we aim to address this gap by formalizing the role of Automated Machine Learning (AutoML) by proposing a novel framework for its key functionalities in critical energy infrastructure. This framework provides a generic and cohesive abstraction to assist with integrating and managing the complexities of AI capabilities. The framework is empirically evaluated in the microgrid setting of a multi-campus, multi-functional tertiary education institution. Results from these experiments confirm the performance contributions of the proposed framework in addressing the complexities of AI capabilities of EM systems as they transition towards microgrid energy optimisation and net zero carbon emissions.

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