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34th AAAI Conference on Artificial Intelligence, AAAI 2020

作者： Gao, Zihang Lin, Fangzhen Zhou, Yi Zhang, Hao KaishunWu, K. Zhang, Haodi College of Computer Science and Software Engineering Shenzhen University China Shenzhen University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Shanghai Research Center for Brain Science and Brain-Inspired Intelligence Zhangjiang Laboratory China Dorabot Inc. China

ISBN: (纸本)9781577358350

Deep reinforcement learning has been successfully applied in many decision making scenarios. However, the slow training process and difficulty in explaining limit its application. In this paper, we attempt to address some of these problems by proposing a framework of Rule-interposing Learning (RIL) that embeds knowledge into deep reinforcement learning. In this framework, the rules dynamically effect the training progress, and accelerate the learning. The embedded knowledge in form of rule not only improves learning efficiency, but also prevents unnecessary or disastrous explorations at early stage of training. Moreover, the modularity of the framework makes it straightforward to transfer high-level knowledge among similar tasks. © 2020, Association for the Advancement of Artificial Intelligence.

关键词： Reinforcement learning

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks 18

Machine Learning-based Energy-Aware Offloading in Edge-Cloud...

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18th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy Information Technology, SEIT 2021

作者： Ismail, Leila Materwala, Huned Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications' Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings. © 2021 Elsevier B.V.. All rights reserved.

关键词： Energy efficiency

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Conditional Generative Modeling for Amorphous Multi-Element Materials

arXiv

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arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

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Research and application of intelligent voice food ordering system 8

Research and application of intelligent voice food ordering ...

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8th International Conference on Orange Technology, ICOT 2020

作者： Wang, Xiaogang Song, Peng Li, Lingli Changzhou College of Information Technology Software and Big Data Department Changzhou China Information Center Rizhao City Urban Drainage Management Office Rizhao China Beijing Huasheng Jingshi Information Technology Co. Ltd Computer and Software Engineering Department Beijing China

ISBN: (纸本)9781665418522

With the rapid development and popularization of Internet applications, online food ordering systems can make people's lives more convenient. However, how to choose the favorite food among various foods conveniently has become the focus of the development of online food ordering systems. Due to the rapid development of artificial intelligence technology and popularization of intelligent applications, more and more intelligent systems have been proposed. However, artificial intelligence exploiting both speech recognition and semantic understanding technology is rarely used to develop intelligent food ordering systems. We hereby propose the intelligent voice food ordering system (IVFOS), which is designed and implemented on the basis of Iflytek AIUI platform, combining speech recognition and semantic understanding technology so as to make the food ordering more convenient. We have further introduced a personalized recommendation mechanism to serve customers based on their historical ordering records. The practical application shows that our intelligent voice food ordering system has the characteristics of convenient usage and highly personalized food ordering experience. © 2020 IEEE.

关键词： Speech recognition

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Electro-optic tuning of a single-frequencyultranarrow linewidth microdisk laser

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Advanced Photonics 2022年第3期4卷 105-113页

作者： Jintian Lin Saeed Farajollahi Zhiwei Fang Ni Yao Renhong Gao Jianglin Guan Li Deng Tao Lu Min Wang Haisu Zhang Wei Fang Lingling Qiao Ya Cheng Chinese Academy of Sciences(CAS) Shanghai Institute of Optics and Fine Mechanics(SIOM)State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-Intense Laser ScienceShanghaiChina University of Chinese Academy of Sciences Center of Materials Science and Optoelectronics EngineeringBeijingChina University of Victoria Department of Electrical and Computer EngineeringVictoriaBritish ColumbiaCanada East China Normal University School of Physics and Electronic ScienceXXL—The Extreme Optoelectromechanics LaboratoryShanghaiChina Research Center for Intelligent Sensing Zhejiang LabHangzhouChina Zhejiang University College of Optical Science and EngineeringThe Interdisciplinary Center for Quantum InformationState Key Laboratory of Modern Optical InstrumentationHangzhouChina East China Normal University State Key Laboratory of Precision SpectroscopyShanghaiChina Jiaxing Institute of Zhejiang University Intelligent Optics&Photonics Research CenterJiaxingChina Shanxi University Collaborative Innovation Center of Extreme OpticsTaiyuanChina Shandong Normal University Collaborative Innovation Center of Light Manipulations and ApplicationsJinanChina Shanghai Research Center for Quantum Sciences Shang

Single-frequency ultranarrow linewidth on-chip microlasers with a fast wavelength tunability play a game-changing role in a broad spectrum of applications ranging from coherent communication,light detection and ranging,to metrology and *** and fabrication of such light sources remain a challenge due to the difficulties in making a laser cavity that has an ultrahigh optical quality(Q)factor and supports only a single lasing frequency ***,we demonstrate a unique single-frequency ultranarrow linewidth lasing mechanism on an erbium ion-doped lithium niobate(LN)microdisk through simultaneous excitation of high-Q polygon modes at both pump and laser *** the polygon modes are sparse within the optical gain bandwidth compared with the whispering gallery mode counterpart,while their Q factors(above 10 million)are even higher due to the significantly reduced scattering on their propagation paths,single-frequency lasing with a linewidth as narrow as 322 Hz is *** measured linewidth is three orders of magnitude narrower than the previous record in on-chip LN ***,enabled by the strong linear electro-optic effect of LN,real-time electro-optical tuning of the microlaser with a high tuning efficiency of∼50 pm∕100 V is demonstrated.

关键词： lasers lithium niobate microcavities integrated opti

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Quantum Linear System Solvers: A Survey of Algorithms and Applications

arXiv

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arXiv 2024年

作者： Morales, Mauro E.S. Pira, Lirandë Schleich, Philipp Koor, Kelvin Costa, Pedro C.S. An, Dong Aspuru-Guzik, Alán Lin, Lin Rebentrost, Patrick Berry, Dominic W. Joint Center for Quantum Information and Computer Science University of Maryland United States Centre for Quantum Software and Information University of Technology Sydney Australia Centre for Quantum Technologies National University of Singapore Singapore Department of Computer Science University of Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada ContinoQuantum Sydney Australia Beijing International Center for Mathematical Research Peking University Beijing China Department of Chemistry University of Toronto Canada Canadian Institute for Advanced Research Toronto Canada Acceleration Consortium Toronto Canada Department of Chemical Engineering & Applied Chemistry University of Toronto Toronto Canada Department of Materials Science & Engineering University of Toronto Toronto Canada Lebovic Fellow Canadian Institute for Advanced Research Toronto Canada Department of Mathematics University of California Berkeley United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley United States Department of Computer Science National University of Singapore Singapore School of Mathematical and Physical Sciences Macquarie University Sydney Australia

Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating the feasibility of solving linear systems problems using quantum computers. In this work, we provide a survey of the main advances in quantum linear systems algorithms, together with some applications. We summarize and analyze the main ideas behind some of the algorithms for the quantum linear systems problem in the literature. The analysis begins by examining the Harrow-Hassidim-Lloyd (HHL) solver. We note its limitations and reliance on computationally expensive quantum methods, then highlight subsequent research efforts which aimed to address these limitations and optimize runtime efficiency and precision via various paradigms. We focus in particular on the post-HHL enhancements which have paved the way towards optimal lower bounds with respect to error tolerance and condition number. By doing so, we propose a taxonomy that categorizes these studies. Furthermore, by contextualizing these developments within the broader landscape of quantum computing, we explore the foundational work that have inspired and informed their development, as well as subsequent refinements. Finally, we discuss the potential applications of these algorithms in differential equations, quantum machine learning, and many-body physics. © 2024, CC BY.

关键词： Quantum computers

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Unveiling the Efficacy of Unani Medicine in Female Disorders Through Machine Learning: Current Challenges and Opportunities

Unveiling the Efficacy of Unani Medicine in Female Disorders...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Arshiya Sultana Faijan Akhtar Md Belal Bin Heyat Priya Singh Saba Parveen Shailendra Kumar Eram Sayeed Soha Akhtar Bandana Singh Khaleequr Rahmani Jianping Li Department of Amraze Niswan wa Ilmul Qabalat National Institute of Unani Medicine Ministry of AYUSH Bengaluru Karnataka India School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China CenBRAIN Neurotech Center of Excellence School of Engineering Westlake University Hangzhou Zhejiang China Dreamvcssels Technology UP India College of Electronic and Information Engineering Shenzhen University Shenzhen Guangdong China Department of Electronics and Communication Engineering Integral University Lucknow India Government Co-ed Sarvodaya Vidyalaya Madanpur Khadar New Delhi India Hamdard Public School New Delhi India School of Media and Communication Shenzhen University Shenzhen China Department of Ilmul Saidla National Institute of Unani Medicine Ministry of AYUSH Bengaluru Karnataka India

In the past decade, the healthcare sector has seen a noteworthy increase in the application of artificial intelligence (AI) technologies across various therapeutic domains. AI-powered technologies have been increasingly used in the context of female diseases within Unani medicine. This study unveils the efficacy of Unani medicine in female diseases through machine learning. This review abides by the preferred reporting items for systematic reviews and meta-analysis extensions for scoping reviews. Studies published after January 1, 2000, were included in 2000 to 2023. A total of 402 full-length articles were searched from the PubMed and Science direct. Two studies from published articles and two from the proposed dataset exclusively focused on the efficacy of Unani medicine, along with one cross-sectional study. This review demonstrates that this research has been conducted using various machine-learning techniques to detect the efficacy of Unani treatment in female disoder. Machine learning was also applied in a cross-sectional study on abnormal vaginal discharge and Mizaj. Machine learning models were used. While research on machine learning models in Unani herbal medicine is in its preliminary stages, the findings from the selected research suggest that ML methods can enhance the services provided by Unani practitioners to their patients. Nevertheless, future advancements should involve the application of diverse ML models across a broad spectrum of Unani treatments.

关键词：

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A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification

A Hybrid Active-Passive Approach to Imbalanced Nonstationary...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Kleanthis Malialis Manuel Roveri Cesare Alippi Christos G. Panayiotou Marios M. Polycarpou KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Università della Svizzera italiana Lugano Switzerland

ISBN: (纸本)9781665487696

In real-world applications, the process generating the data might suffer from nonstationary effects (e.g., due to seasonality, faults affecting sensors or actuators, and changes in the users' behaviour). These changes, often called concept drift, might induce severe (potentially catastrophic) impacts on trained learning models that become obsolete over time, and inadequate to solve the task at hand. Learning in presence of concept drift aims at designing machine and deep learning models that are able to track and adapt to concept drift. Typically, techniques to handle concept drift are either active or passive, and traditionally, these have been considered to be mutually exclusive. Active techniques use an explicit drift detection mechanism, and re-train the learning algorithm when concept drift is detected. Passive techniques use an implicit method to deal with drift, and continually update the model using incremental learning. Differently from what present in the literature, we propose a hybrid alternative which merges the two approaches, hence, leveraging on their advantages. The proposed method called Hybrid-Adaptive REBAlancing (HAREBA) significantly outperforms strong baselines and state-of-the-art methods in terms of learning quality and speed; we experiment how it is effective under severe class imbalance levels too.

关键词： Deep learning Adaptation models Actuators Sensors Data mining Task analysis Computational intelligence

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Research on Rainfall Estimation Based on Improved Kalman Filter Algorithm

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Journal of Quantum Computing 2022年第1期4卷 23-37页

作者： Wen Zhang Wei Fang Xue leiJia Victor S.Sheng Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer&SoftwareNanjing University of Information Science&TechnologyChina Nanjing Xinda Institute of Meteorological Science and Technology Co. Ltd.Nanjing210044JiangsuChina State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Department of Computer Texas Tech UniversityLubbockTX 79409USA

In order to solve the rainfall estimation error caused by various noise factors such as clutter,super refraction,and raindrops during the detection process of Doppler weather *** paper proposes to improve the rainfall estimation model of radar combined with rain gauge which calibrated by common Kalman *** data preprocessing,the radar data should be classified according to the precipitation *** then,they are respectively substituted into the improved filter for *** state noise variance Q(k)and the measurement noise variance R(k)can be adaptively calculated and updated according to the input observation data during this *** the optimal parameter value of each type of precipitation intensity can be *** state noise variance Q(k)and the measurement noise variance R(k)could be assigned optimal values when filtering the remaining *** rainfall estimation based on semiadaptive Kalman filter calibration not only improves the accuracy of rainfall estimation,but also greatly reduces the amount of *** avoids errors caused by repeated calculations,and improves the efficiency of the rainfall estimation at the same time.

关键词： Kalman filter doppler radar rainfall estimation radar-rain gauge joint calibration

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MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

SSRN

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SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

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