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Submilliwatt, widely tunable coherent microcomb generation with feedback-free operation

Advanced Photonics 2023年第3期5卷 144-154页

作者： Haowen Shu Lin Chang Chenghao Lao Bitao Shen Weiqiang Xie Xuguang Zhang Ming Jin Yuansheng Tao Ruixuan Chen Zihan Tao Huajin Chang Shaohua Yu Qifan Yang Xingjun Wang John E.Bowers Peking University School of ElectronicsState Key Laboratory of Advanced Optical Communication Systems and NetworksBeijingChina University of California Santa BarbaraDepartment of Electrical and Computer EngineeringSanta BarbaraCaliforniaUnited States Peking University School of PhysicsState Key Laboratory for Artificial Microstructure and Mesoscopic PhysicsBeijingChina Peng Cheng Laboratory ShenzhenChina Peking University Frontiers Science Center for Nano-OptoelectronicsBeijingChina

Microcombs are revolutionizing optoelectronics by providing parallel, mutually coherent wavelengthchannels for time-frequency metrology and information processing. To implement this essential function inintegrated photonic systems, it is desirable to drive microcombs directly with an on-chip laser in a simpleand flexible way. However, two major difficulties have prevented this goal: (1) generating mode-lockedcomb states usually requires a significant amount of pump power and (2) the requirement to align laser andresonator frequency significantly complicates operation and limits the tunability of the comb lines. Here, weaddress these problems by using microresonators on an AlGaAs on-insulator platform to generate dark-pulsemicrocombs. This highly nonlinear platform dramatically relaxes fabrication requirements and leads to arecord-low pump power of <1 mW for coherent comb generation. Dark-pulse microcombs facilitated bythermally controlled avoided mode crossings are accessed by direct distributed feedback laser *** any feedback or control circuitries, the comb shows good coherence and stability. With around150 mW on-chip power, this approach also leads to an unprecedentedly wide tuning range of over one freespectral range (97.5 GHz). Our work provides a route to realize power-efficient, simple, and reconfigurablemicrocombs that can be seamlessly integrated with a wide range of photonic systems.

关键词： dark-pulse microcomb direct pumping scheme widely tunable source

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Exploring Data Geometry for Continual Learning

Exploring Data Geometry for Continual Learning

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhi Gao Chen Xu Feng Li Yunde Jia Mehrtash Harandi Yuwei Wu Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Electrical and Computer Systems Eng. Monash University and Data61 Australia

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to in-ferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and pre-vents forgetting by keeping geometric structures of old data into account. In doing so, making use of the mixed cur-vature space, we propose an incremental search scheme, through which the growing geometric structures are en-coded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capa-ble of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space.

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Symbiotic Control of Uncertain Dynamical systems*

Symbiotic Control of Uncertain Dynamical Systems*

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IEEE Conference on Decision and Control

作者： Tansel Yucelen Selahattin Burak Sarsılmaz Emre Yildirim Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems (LACIS http://lacis.eng.usf.edu/) University of South Florida Tampa Florida United States of America Department of Electrical and Computer Engineering Utah State University Logan Utah United States of America

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we consider both the fixed-gain control and adaptive learning architectures to suppress the effects of uncertainties. We note that fixed-gain control provides more predictable closed-loop system behavior, but it comes at the cost of knowing uncertainty bounds. On the other hand, adaptive learning removes the requirement of this knowledge at the expense of less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that integrates the advantages of both fixed-gain control and adaptive learning architectures. In particular, the proposed framework utilizes both control architectures to suppress the negative effects of uncertainties with more predictable closed-loop system behavior and without the knowledge of uncertainty bounds. Both parametric and nonparametric uncertainties are considered, where we use neural networks to approximate the unknown uncertainty basis for the latter case. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach.

关键词： Symbiosis Hands Adaptive learning Uncertainty Upper bound Costs Neurons Closed loop systems Dynamical systems Biological neural networks

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Analytical-Chemistry-Informed Transformer for Infrared Spectra Modeling 39

Analytical-Chemistry-Informed Transformer for Infrared Spect...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Shiluo Jin, Yining Jin, Wei Mu, Ying School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada Research Centre for Analytical Instrumentation State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Infrared (IR) spectroscopy is a fundamental technique in analytical chemistry. Recently, deep learning (DL) has drawn great interest as the modeling method of infrared spectral data. However, unlike vision or language tasks, IR spectral data modeling is faced with the problem of calibration transfer and has distinctive characteristics. Introducing the prior knowledge of IR spectroscopy could guide the DL methods to learn representations aligned with the domain-invariant characteristics of spectra, and thus improve the performance. Despite such potential, there is a notable absence of DL methods that incorporate such inductive bias. To this end, we propose Analytical-Chemistry-Informed Transformer (ACT) with two modules informed by the field knowledge in analytical chemistry. First, ACT includes learnable spectral processing inspired by chemometrics, which comprises spectral pre-processing, tokenization, and post-processing. Second, a straightforward yet effective representation learning mechanism, namely spectral-attention, is incorporated into ACT. Spectral-attention utilizes the intra-spectral and inter-spectral correlations to extract spectral representations. Empirical results show that ACT has achieved competitive results in 9 analytical tasks covering applications across pharmacy, chemistry, and agriculture. Compared with existing networks, ACT reduces the root mean square error of prediction (RMSEP) by more than 20% in calibration transfer tasks. These results indicate that DL methods in IR spectroscopy could benefit from the integration of prior knowledge in analytical chemistry. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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An Improved Machine Learning Technique with Effective Heart Disease Prediction System

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computers, Materials & Continua 2021年第12期69卷 4169-4181页

作者： Mohammad Tabrez Quasim Saad Alhuwaimel Asadullah Shaikh Yousef Asiri Khairan Rajab Rihem Farkh Khaled Al Jaloud College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death *** heart disease is challenging because it requires substantial experience and *** research studies have found that the diagnostic accuracy of heart disease is *** coronary heart disorder determines the state that influences the heart valves,causing heart *** indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep *** work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial *** first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)*** this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was *** CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods *** proposed method offers an illustrative framework that helps predict heart attacks with high *** proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

关键词： Machine learning deep recurrent neural network effective heart disease prediction framework

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks

Agricultural Water Conservation through AI-Enhanced Solar Io...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： S. P. Maniraj Monisha J R. Amutha R. Kavitha P. J. Shriidhar C. Srinivasan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha Universityi Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future.

关键词： Irrigation Accuracy Food security Artificial neural networks Water conservation Real-time systems Internet of Things Sustainable development Water resources Intelligent sensors

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Prototype Design and Experimental Test for A Hydraulic-Driven Soft Robotic Arm

Prototype Design and Experimental Test for A Hydraulic-Drive...

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International Conference on Biomimetic Intelligence and Robotics & Medical Robotics Forum, ICBIR+MRF 2024

作者： Lei, Yu Du, Pengkun Xie, Minghao Chen, Zheng Gu, Jason Ocean College Zhejiang University Zhoushan310058 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China Hainan Institute Zhejiang University Sanya572025 China Leicester International Institute Dalian University of Technology Panjin124221 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada

Soft robotics has gained considerable attention in recent years for its structural flexibility and inherent safety in environmental interactions. To address the pitfalls of pneumatic actuation systems, namely sluggish response and inefficiencies, this paper introduces the design and fabrication of a Hydraulic-Driven Soft Robotic Arm (HDSRA). The study establishes a comprehensive platform that integrates actuation, sensing, and control software to provide an experimental prototype for validating control algorithms. A novel fabrication technique utilizing water-soluble PVA for single-step mold creation enhances the structural reliability of soft actuators. Through closed-loop control experiments, the HDSRA demonstrates rapid and precise tracking of 1Hz signals, encompassing sine, square, and ramp waves, thus confirming the platform's reliability. This foundational work lays a robust foundation for future research and the verification of control algorithms in HDSRAs. © 2024 The Authors.

关键词： Robotic arms

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

arXiv

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arXiv 2024年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab The College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore

The rapid development of generative AI technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 2024, CC BY.

关键词： Generative adversarial networks

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Risk-Averse Self-Scheduling of Storage in Decentralized Markets

Risk-Averse Self-Scheduling of Storage in Decentralized Mark...

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IEEE General Meeting Power& Energy Society

作者： Ogun Yurdakul Farhad Billimoria Energy Systems and Infrastructure Analysis Division Argonne National Laboratory Lemont IL USA Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Department of Engineering Science University of Oxford Oxford United Kingdom

Storage is expected to be a critical source of firming in low-carbon grids. A common concern raised from ex-post assessments is that storage resources can fail to respond to strong price signals during times of scarcity. While commonly attributed to forecast error or failures in operations, we posit that this behavior can be explained from the perspective of risk-averse scheduling. Using a stochastic self-scheduling framework and real-world data harvested from the Australian National Electricity Market, we demonstrate that risk-averse storage resources tend to have a myopic operational perspective, that is, they typically engage in near-term price arbitrage and chase only few extreme price spikes and troughs, thus remaining idle in several time periods with markedly high and low prices. This has important policy implications given the non-transparency of unit risk aversion and apparent risk in intervention decision-making.

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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