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Science China(Information Sciences) 2024年第2期67卷 20-40页

作者： Peng YANG Laoming ZHANG Haifeng LIU Guiying LI department of Statistics and data Science Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and Technology Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Guangdong OPPO Mobile Telecommunications Corp. Ltd Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.

关键词： evolutionary reinforcement learning evolutionary multi-objective optimization load balance cloud computing

Theoretical Quantization of Exact Wave Turbulence in Exponential Oscillons and Pulsons

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American Journal of Computational Mathematics 2024年第2期14卷 203-239页

作者： Victor A. Miroshnikov department of Mathematics and data Analytics University of Mount Saint Vincent New York USA

In a preceding paper, the theoretical and experimental, deterministic and random, scalar and vector, kinematic structures, the theoretical and experimental, deterministic-deterministic, deterministic-random, random-deterministic, random-random, scalar and vector, dynamic structures have been developed to compute the exact solution for wave turbulence of exponential pulsons and oscillons that is governed by the nonstationary three-dimensional Navier-Stokes equations. The rectangular, diagonal, and triangular summations of matrices of the turbulent kinetic energy and general terms of numerous sums have been used in the current paper to develop theoretical quantization of the kinetic energy of exact wave turbulence. Nested structures of a cumulative energy pulson, a deterministic energy pulson, a deterministic internal energy oscillon, a deterministic-random internal energy oscillon, a random internal energy oscillon, a random energy pulson, a deterministic diagonal energy oscillon, a deterministic external energy oscillon, a deterministic-random external energy oscillon, a random external energy oscillon, and a random diagonal energy oscillon have been established. In turn, the energy pulsons and oscillons include deterministic group pulsons, deterministic internal group oscillons, deterministic-random internal group oscillons, random internal group oscillons, random group pulsons, deterministic diagonal group oscillons, deterministic external group oscillons, deterministic-random external group oscillons, random external group oscillons, and random diagonal group oscillons. Sequentially, the group pulsons and oscillons contain deterministic wave pulsons, deterministic internal wave oscillons, deterministic-random internal wave oscillons, random internal wave oscillons, random wave pulsons, deterministic diagonal wave oscillons, deterministic external wave oscillons, deterministic-random external wave oscillons, random external wave oscillons, random diagonal wave osc

关键词： The Navier-Stokes Equations Deterministic-Random Internal Energy Oscillon Deterministic-Random External Energy Oscillon Deterministic-Random Internal Group Oscillons Deterministic-Random External Group Oscillons Deterministic-Random Internal Wave Oscillons Deterministic-Random External Wave Oscillons Deterministic-Random Internal Elementary Oscillons Deterministic-Random External Elementary Oscillons Random-Deterministic External Elementary Oscillons

Investigating environmental quality among G20 nations: The impacts of environmental goods and low-carbon technologies in mitigating environmental degradation

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Geoscience Frontiers 2024年第1期15卷 84-96页

作者： Ernest Baba Ali Samira Shayanmehr Riza Radmehr Richard Bayitse Ebenezer Agbozo department of Economics University for Development StudiesTamale P.O.Box TL1350Ghana department of Agricultural Economics Ferdowsi University of MashhadIran Council for Scientific and Industrial Research Institute of Industrial ResearchAccra P.O.Box LG 576Ghana department of big data Analytics and Methods of Video Analysis Ural Federal University19 Mira Str60002 EkaterinburgRussia

Environmental goods and low-carbon technologies have long been identified as having the potential to drive long-term economic progress without compromising environmental quality. However, their exact role in mitigating environmental degradation are yet to be unravelled. In addressing this shortfall, the extant literature relied on research funding and patent application as proxies for green technologies. Having established the weaknesses in the use of these variables as proxies for green technologies, this study explored the role of trade in environmental goods and low-carbon technologies in boosting environmental quality among G20 nation using a panel dataset from 1994 to 2018. The study employed the Method of Moment quantile regression for the model estimation and the Ridge regression, Discroll-Kraay standard error, and the Newey-West standard error estimators to test the robustness of our findings. Our findings indicate that whereas environmental goods promote environmental quality, low-carbon technologies decrease same. Also, the study found economic growth to exert an aggravating effect on environmental quality, while foreign direct investments, natural resource rents, human capital development, and renewable energy consumption exert positive influence on environmental quality. Based on the findings of the study, G20 nations are encouraged to improve green market structures to improve the trade in environmental goods and low-carbon technologies. Also the share of renewable energy sources in the overall energy basket must be improved to help improve environmental quality.

关键词： Environmental quality Environmental goods Low-carbon technology G20 nations

Variable Selection for High-dimensional Cox Model with Error Rate Control

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Journal of Systems Science & Complexity 2024年

作者： HE Baihua SHI Hongwei GUO Xu ZOU Changliang ZHU Lixing International Institute of Finance School of Management University of Science and Technology of China School of Statistics Beijing Normal University School of Statistics and data Science LPMC KLMDASR and LEBPS Nankai University Center for Statistics and data Science Beijing Normal University at Zhuhai department of Mathematics Hong Kong Baptist University

Simultaneously finding active predictors and controlling the false discovery rate(FDR) for high-dimensional survival data is an important but challenging statistical problem. In this paper, the authors propose a novel variable selection procedure with error rate control for the high-dimensional Cox model. By adopting a data-splitting strategy, the authors construct a series of symmetric statistics and then utilize the symmetry property to derive a data-driven threshold to achieve error rate *** authors establish finite-sample and asymptotic FDR control results under some mild *** results as well as a real data application show that the proposed approach successfully controls FDR and is often more powerful than the competing approaches.

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Geometric prior guided hybrid deep neural network for facial beauty analysis

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CAai Transactions on Intelligence Technology 2024年第2期9卷 467-480页

作者： Tianhao Peng Mu Li Fangmei Chen Yong Xu David Zhang The School of Computer Science and Technology Guizhou UniversityGuiyangChina department of Automation Moutai InstituteRenhuaiGuizhouChina The School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhenChina The Information and Communication Engineering department Dalian Minzu UniversityDalianChina The School of data Science The Chinese University of Hong KongShenzhenShenzhenChina

Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

关键词： deep neural networks face analysis face biometrics image analysis

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

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Computers, Materials & Continua 2024年第8期80卷 2199-2219页

作者： Huanhuan Li Hongchang Wei Zheliang Chen Yue Xu School of Mathematics and Computer Sciences Nanchang UniversityNanchang330031China department of Electrical Engineering Jiangxi Vocational College of Mechanical&Electrical TechnologyNanchang330013China Kehua data Co. Ltd.Xiamen361006China

The current resource allocation in 5G vehicular networks for mobile cloud communication faces several challenges,such as low user utilization,unbalanced resource allocation,and extended adaptive allocation *** propose an adaptive allocation algorithm for mobile cloud communication resources in 5G vehicular networks to address these *** study analyzes the components of the 5G vehicular network architecture to determine the performance of different *** is ascertained that the communication modes in 5G vehicular networks for mobile cloud communication include in-band and out-of-band ***,this study analyzes the single-hop and multi-hop modes in mobile cloud communication and calculates the resource transmission rate and bandwidth in different communication *** study also determines the scenario of one-way and two-way vehicle lane cloud communication network connectivity,calculates the probability of vehicle network connectivity under different mobile cloud communication radii,and determines the amount of cloud communication resources required by vehicles in different lane *** on the communication status of users in 5G vehicular networks,this study calculates the bandwidth and transmission rate of the allocated channels using Shannon’s *** determines the adaptive allocation of cloud communication resources,introduces an objective function to obtain the optimal solution after allocation,and completes the adaptive allocation *** experimental results demonstrate that,with the application of the proposed method,the maximum utilization of user communication resources reaches approximately 99%.The balance coefficient curve approaches 1,and the allocation time remains under 2 *** indicates that the proposed method has higher adaptive allocation efficiency.

关键词： 5G vehicular networks mobile cloud communication resource allocation channel capacity network connectivity communication radius objective function

GAN‐MD:A myocarditis detection using multi‐channel convolutional neural networks and generative adversarial network‐based data augmentation

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CAai Transactions on Intelligence Technology 2024年第4期9卷 866-878页

作者： Hengame Ahmadi Golilarz Alireza Azadbar Roohallah Alizadehsani Juan Manuel Gorriz department of Medical Radiation Engineering Faculty of EngineeringLahijan BranchIslamic Azad UniversityLahijanIran department of Medical Radiation Engineering Lahijan BranchIslamic Azad UniversityLahijanIran Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a significant public health concern because of its potential to cause heart failure and sudden *** standard invasive diagnostic method,endomyocardial bi-opsy,is typically reserved for cases with severe complications,limiting its widespread ***,non‐invasive cardiac magnetic resonance(CMR)imaging presents a promising alternative for detecting and monitoring myocarditis,because of its high signal contrast that reveals myocardial *** assist medical professionals via artificial intelligence,the authors introduce generative adversarial networks‐multi discriminator(GAN‐MD),a deep learning model that uses binary classification to diagnose myocarditis from CMR *** approach employs a series of convolutional neural networks(CNNs)that extract and combine feature vectors for accurate *** authors suggest a novel technique for improving the classification precision of *** generative adversarial networks(GANs)to create synthetic images for data augmentation,the authors address challenges such as mode collapse and unstable *** a reconstruction loss into the GAN loss function requires the generator to produce images reflecting the discriminator features,thus enhancing the generated images'quality to more accurately replicate authentic data ***,combining this loss function with other reg-ularisation methods,such as gradient penalty,has proven to further improve the perfor-mance of diverse GAN models.A significant challenge in myocarditis diagnosis is the imbalance of classification,where one class dominates over the *** mitigate this,the authors introduce a focal loss‐based training method that effectively trains the model on the minority class *** GAN‐MD approach,evaluated on the Z‐Alizadeh Sani myocarditis dataset,achieves superior results(F‐measure 86.2%;geometric mean 91.0%)compared with other deep learning models and traditional machine learning methods.

关键词： 2‐D 3‐D

Research Progress in Enantioselective Radical Desymmetrization Reactions

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CCS Chemistry 2024年第7期6卷 1612-1627页

作者： Chang-Jiang Yang Lin Liu Qiang-Shuai Gu Xin-Yuan Liu department of Chemistry and Dongguan Key Laboratory for data Science and Intelligent Medicine Great Bay UniversityDongguan 523000 Shenzhen Grubbs Institute Department of Chemistryand Guangming Advanced Research InstituteSouthern University of Science and TechnologyShenzhen 518055 Academy for Advanced Interdisciplinary Studies and department of Chemistry Southern University of Science and TechnologyShenzhen 518055

Enantioselective radical desymmetrization is a highly effective approach for rapidly creating enantioenriched molecules,introducing dramatically increased structural complexity from readily available prochiral or meso compound *** strategic modes have been developed for these reactions,which differ in the nature of the stereo-determining *** first category deals primarily with the stereoselective desymmetrization of closed-shell radical precursors or functional reagents,whereas the second category achieves desymmetrization by stereoselectively functionalizing open-shell radical *** mini-review explores the research progress in this growing field,aiming to elucidate mechanistic scenarios related to stereochemical ***,it offers insights into the challenges and opportunities that lie ahead for further development.

关键词： radical desymmetrization enantioselective desymmetrization radical functionalization transition metal catalysis asymmetric catalysis

从信号到知识——基于人工智能的医学影像裸数据诊断价值初探

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Engineering 2024年第3期34卷 60-69页

作者： Bingxi He Yu Guo Yongbei Zhu Lixia Tong Boyu Kong Kun Wang Caixia Sun Hailin Li Feng Huang Liwei Wu Meng Wang Fanyang Meng Le Dou Kai Sun Tong Tong Zhenyu Liu Ziqi Wei Wei Mu Shuo Wang Zhenchao Tang Shuaitong Zhang Jingwei Wei Lizhi Shao Mengjie Fang Juntao Li Shouping Zhu Lili Zhou Shuo Wang Di Dong Huimao Zhang Jie Tian Beijing Advanced Innovation Center for big data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijing 100191China Key Laboratory of big data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijing 100191China CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China department of Radiology The First Hospital of Jilin UniversityChangchun 130021China Neusoft Medical Systems Co.Ltd. Shenyang 110167China Neusoft Research of Intelligent Healthcare Technology Co.Ltd. Shenyang 110167China School of Life Science and Technology Xidian UniversityXi’an 710126China Engineering Research Center of Molecular and Neuro Imaging Ministry of EducationXi’an 710126China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Encouraging and astonishing developments have recently been achieved in image-based diagnostic *** medical care and imaging technology are becoming increasingly ***,the current diagnosis pattern of signal to image to knowledge inevitably leads to information distortion and noise introduction in the procedure of image reconstruction(from signal to image).Artificial intelligence(ai)technologies that can mine knowledge from vast amounts of data offer opportunities to disrupt established *** this prospective study,for the first time,we develop an ai-based signal-toknowledge diagnostic scheme for lung nodule classification directly from the computed tomography(CT)raw data(the signal).We find that the raw data achieves almost comparable performance with CT,indicating that it is possible to diagnose diseases without reconstructing ***,the incorporation of raw data through three common convolutional network structures greatly improves the performance of the CT models in all cohorts(with a gain ranging from 0.01 to 0.12),demonstrating that raw data contains diagnostic information that CT does not *** results break new ground and demonstrate the potential for direct signal-to-knowledge domain analysis.

关键词： Computed tomography Diagnosis Deep learning Lung cancer Raw data

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热浪加剧中国空气污染:气候、能源与环境的交织机制（英文）

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Science Bulletin 2024年第17期 2765-2775页

作者：陈天舒王韬薛丽坤 Guy Brasseur department of Civil and Environmental Engineering The Hong Kong Polytechnic University Environment Research Institute Shandong University big data Research Center for Ecology and Environment Shandong University Environmental Modelling Group Max Planck Institute for Meteorology National Center for Atmospheric Research

Climate change is increasing the frequency and intensity of heatwaves, raising concerns about their detrimental effects on air quality. However, a role for heatwave-human-environment interactions in air pollution exacerbation has not been established. In the summer of 2022, record-breaking heatwaves struck China and Europe. In this study, we use integrated observational data and machine learning to elucidate the formation mechanism underlying one of the most severe ozone pollution seasons on record in central eastern China, an area that encompasses approximately half of China's total population and sown *** findings reveal that the worsened ozone and nitrogen dioxide pollution resulted from a mismatch between energy demand and supply, which was driven by both heatwaves and energy policy-related factors. The observed adverse heatwave-energy-environment feedback loop highlights the need for the diversification of clean energy sources, more resilient energy structures and power policies, and further emission control to confront the escalating climate challenge in the future.

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