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11th International Conference on Software and computer Applications, ICSCA 2022

作者： Cao, Lingli Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China School of Computer Science and Technology Anhui University China State Key Lab for Novel Software Technology Nanjing University China

ISBN: (纸本)9781450385770

The features of microservices, such as scalability and maintainability, have attracted the industry to migrate monolithic projects to microservices. However, how to decompose microservices during migration is a tricky problem. At present, microservices decomposition mainly relies on architects or domain experts, which is more subjective and time-consuming. Followed by semi-automated or automated microservice decomposition, such methods produce coarse-grained results affected by different system characteristics, which cannot make desired decomposition according to the migration requirements of domains. Therefore, this paper proposes a domain-oriented fine-grained microservices decomposition resolution scheme. It uses dynamic and static analysis to obtain the invocation relationships and invocation times between entity methods and the response time of entity methods to represent three main system characteristics concerned during the migration: function, inter-service communications, and performance. And express the above information of monolith by the node-attributed network. Then it uses the community detection algorithm and the proposed similar hierarchical clustering algorithm to complete objective and effective decomposition. Finally, the rationality and feasibility of the proposed approach are verified using the JPetStore case. © 2022 ACM.

关键词： Dynamic analysis Static analysis Microservices Decomposition Node-attributed Network Domain

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How Can We Cope with the Impact of Microservice Architecture Smells? 2022

How Can We Cope with the Impact of Microservice Architecture...

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11th International Conference on Software and computer Applications, ICSCA 2022

作者： Ding, Xiang Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China School of Computer Science and Technology Anhui University China State Key Lab for Novel Software Technology Nanjing University China

ISBN: (纸本)9781450385770

Context: Software Architecture Smells (AS) are potential software structure problems and always impact software quality negatively. And with the development of Microservice architecture, the Microservice Architecture Smells (MAS) have been attracting more and more attention. The software scholars and developers have discovered the influence of MAS and performed some researches on them. However, the definition and category of MAS are still ambiguous. Objects: This paper aims to clarify the specific MAS categories and their definitions, and then further explores the issues caused by MAS in the migration process from a monolithic system to Microservice. Method: We performed a comprehensive systematic literature review about MAS. Specifically, we explored 13 white and 10 grey literature in detail to get MAS information by using the quantitative research method. To explore the issues that influence the migration process from a monolithic system to Microservice, we used the meta-ethnography qualitative research method to extract relevant information and get six three-order translations. Results: This study defined 22 Microservice Architecture Smells and classified them into five categories, namely Design, Deployment, Monitor & Log, Communication and Team & Tool, based on their characteristics. Simultaneously, the issues that influence the migration process are proposed, including service cutting, databases, communication, team and techniques. Finally, we matched the MAS to the issues they caused in the migration process and recommended solutions to these issues. © 2022 ACM.

关键词： meta-ethnography Microservice Architecture Smells smell impact Systematic Literature Review

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Lightweight Approaches to DNN Regression Error Reduction: An Uncertainty Alignment Perspective

Lightweight Approaches to DNN Regression Error Reduction: An...

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International Conference on Software Engineering (ICSE)

作者： Zenan Li Maorun Zhang Jingwei Xu Yuan Yao Chun Cao Taolue Chen Xiaoxing Ma Jian Lü Department of Computer Science and Technology State Key Lab of Novel Software Technology Nanjing University China Department of Computer Science Birkbeck University of London UK

Regression errors of Deep Neural Network (DNN) models refer to the case that predictions were correct by the old-version model but wrong by the new-version model. They frequently occur when upgrading DNN models in production systems, causing disproportionate user experience degradation. In this paper, we propose a lightweight regression error reduction approach with two goals: 1) requiring no model retraining and even data, and 2) not sacrificing the accuracy. The proposed approach is built upon the key insight rooted in the unmanaged model uncertainty, which is intrinsic to DNN models, but has not been thoroughly explored especially in the context of quality assurance of DNN models. Specifically, we propose a simple yet effective ensemble strategy that estimates and aligns the two models' uncertainty. We show that a Pareto improvement that reduces the regression errors without compromising the overall accuracy can be guaranteed in theory and largely achieved in practice. Comprehensive experiments with various representative models and datasets confirm that our approaches significantly outperform the state-of-the-art alternatives.

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Simulating the Dilution of Sulfuric Acid on Mixed Reality Platform 8

Simulating the Dilution of Sulfuric Acid on Mixed Reality Pl...

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8th International Conference on Virtual Reality, ICVR 2022

作者： Pan, Zhipeng Miao, Jinda Cai, Ning Luo, Tianren Zhang, Mingmin Pan, Zhigeng Research Institute of Virtual Reality and Intelligent Systems Hangzhou Normal University Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Beijing China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Augmented reality technology has been widely used in experimental education. Augmented reality provides virtual-real integration, real-Time interaction and three-dimensional immersion, which provides a new development direction for simulating the teaching environment and promoting learning interaction. We have developed an MR experiment of diluting concentrated sulfuric acid, which helps students learn experimental manipulations and scientific concepts before conducting real chemical experiments. Virtual experiments can avoid the risks of real chemical experiments and reduce the waste of chemical materials. At the same time, we correctly render the occlusion relationship between the hand, the beaker and the virtual liquid, providing a realistic experimental effect. The tracking of multiple cameras allows students to interact more naturally. We compared the availability and students' attitudes of three virtual experiments about diluting concentrated sulfuric acid. The results show that the usability of the MR experimental system reaches the level of user satisfaction. The realistic visual effects and natural interaction method of the MR experiment have been recognized by the students. © 2022 IEEE.

关键词： Students

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ALADE-SNN: Adaptive Logit Alignment in Dynamically Expandable Spiking Neural Networks for Class Incremental Learning

arXiv

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arXiv 2024年

作者： Ni, Wenyao Shen, Jiangrong Xu, Qi Tang, Huajin State Key Lab of Brain-Machine Intelligence Zhejiang University China College of Computer Science and Technology Zhejiang University China School of Computer Science and Technology Xi’an Jiaotong University China School of Computer Science and Technology Dalian University of Technology China

Inspired by the human brain’s ability to adapt to new tasks without erasing prior knowledge, we develop spiking neural networks (SNNs) with dynamic structures for Class Incremental Learning (CIL). Our comparative experiments reveal that limited datasets introduce biases in logits distributions among tasks. Fixed features from frozen past-task extractors can cause overfitting and hinder the learning of new tasks. To address these challenges, we propose the ALADE-SNN framework, which includes adaptive logit alignment for balanced feature representation and OtoN suppression to manage weights mapping frozen old features to new classes during training, releasing them during fine-tuning. This approach dynamically adjusts the network architecture based on analytical observations, improving feature extraction and balancing performance between new and old tasks. Experiment results show that ALADE-SNN achieves an average incremental accuracy of 75.42 ± 0.74% on the CIFAR100-B0 benchmark over 10 incremental steps. ALADE-SNN not only matches the performance of DNN-based methods but also surpasses state-of-the-art SNN-based continual learning algorithms. This advancement enhances continual learning in neuromorphic computing, offering a brain-inspired, energy-efficient solution for real-time data processing. © 2024, CC BY-NC-ND.

关键词： Contrastive Learning

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EcoVis:visual analysis of industrial-level spatio-temporal correlations in electricity consumption

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Frontiers of computer science 2022年第2期16卷 98-108页

作者： Yong XIAO Kaihong ZHENG Supaporn LONAPALAWONG Wenjie LU Zexian CHEN Bin QIAN Tianye ZHANG Xin WANG Wei CHEN Electric Power Research Institute China Southern Power GridGuangzhou 510663China Digital Grid Research Institute China Southern Power GridGuangzhou 510663China The State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China School of Computer Science Zhejiang UniversityHangzhou 310058China

Closely related to the economy,the analysis and management of electricity consumption has been widely *** approaches mainly focus on the prediction and anomaly detection of electricity consumption,which fails to reveal the in-depth relationships between electricity consumption and various factors such as industry,weather etc..In the meantime,the lack of analysis tools has increased the difficulty in analytical tasks such as correlation analysis and comparative *** this paper,we introduce EcoVis,a visual analysis system that supports the industrial-level spatio-temporal correlation analysis in the electricity consumption *** not only propose a novel approach to model spatio-temporal data into a graph structure for easier correlation analysis,but also introduce a novel visual representation to display the distributions of multiple instances in a single *** implement the system with the cooperation with domain *** are conducted to demonstrate the effectiveness of our method.

关键词： spatio-temporal data electricity consumption correlation analysis visual analysis visualization

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Toward Optimal Filler Cell Insertion with Complex Implant Layer Constraints 23

Toward Optimal Filler Cell Insertion with Complex Implant La...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Peng Zou Guohao Chen Zhifeng Lin Jun Yu Jianli Chen State Key Lab of ASIC & System Fudan University Shanghai China Center for Discrete Mathematics and Theoretical Computer Science Fuzhou University Fuzhou China

ISBN: (纸本)9798350323481

Modern circuits often contain standard cells of different threshold voltages (multi-VTs) to achieve a better trade-off between timing and power consumption. Due to the heterogeneous cell structures, the multi-VTs cells impose various implant layer constraints, further complicating the already time-consuming filler cell insertion process. In this paper, we present a fast and near-optimal algorithm to solve the filler insertion problem with complex implant layer rules and minimum filler width constraints. We first propose an inference-driven detecting algorithm to identify each design rule violation accurately. Then, a dynamic-programming-based insertion method is developed to reduce the implant layer violations. Finally, we design a contour-driven violation refinement strategy to further improve manufacturability. Experimental results show that our algorithm can reduce the number of violations significantly compared with state-of-the-art works. Besides, with our identifier in the legalization stage, we can avoid conflicts in advance and solve almost all violations after filler insertion in industrial cases.

关键词： filler insertion

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MACRO: Multi-agent Reinforcement Learning-based Cross-layer Optimization of Operational Amplifier

MACRO: Multi-agent Reinforcement Learning-based Cross-layer ...

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Asia and South Pacific Design Automation Conference

作者： Zihao Chen Songlei Meng Fan Yang Li Shang Xuan Zeng State Key Lab of Integrated Chips and Systems School of Microelectronics Fudan University Shanghai China China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

The optimization of operational amplifiers, including topology design and parameter tuning, is significantly challenging, given the high-dimensional and heterogeneous characteristics of the design space. This paper presents MACRO, a novel approach to operational amplifier design that employs multi-agent reinforcement learning for cross-layer optimization. We model the sequentially executed topology design and parameter tuning tasks as a Markov decision process, where the high-dimensional design space is effectively transformed into a series of manageable action spaces at each step. Two agents are meticulously tailored to specialize in these two distinct tasks respectively. The co-evolution of the agents is ensured by sharing design information and customizing the policy-gradient training method. Experimental results show that, compared with state-of-the-art methods, MACRO can produce superior-performing circuits while maintaining competitive design efficiency.

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The long-term health outcomes, pathophysiological mechanisms and multidisciplinary management of long COVID

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Signal Transduction and Targeted Therapy 2023年第12期8卷 5584-5602页

作者： Jingwei Li Yun Zhou Jiechao Ma Qin Zhang Jun Shao Shufan Liang Yizhou Yu Weimin Li Chengdi Wang Department of Pulmonary and Critical Care Medicine State Key Laboratory of Respiratory Health and MultimorbidityMed-X Center for ManufacturingFrontiers Science Center for Disease-related Molecular NetworkWest China HospitalSichuan UniversityChengduChina AI Lab Deepwise HealthcareBeijingChina Department of Postgraduate Student West China HospitalWest China School of MedicineSichuan UniversityChengduChina Department of Computer Science The University of Hong KongHong KongChina

There have been hundreds of millions of cases of coronavirus disease 2019(COVID-19),which is caused by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).With the growing population of recovered patients,it is crucial to understand the long-term consequences of the disease and management *** COVID-19 was initially considered an acute respiratory illness,recent evidence suggests that manifestations including but not limited to those of the cardiovascular,respiratory,neuropsychiatric,gastrointestinal,reproductive,and musculoskeletal systems may persist long after the acute *** persistent manifestations,also referred to as long COVID,could impact all patients with COVID-19 across the full spectrum of illness ***,we comprehensively review the current literature on long COVID,highlighting its epidemiological understanding,the impact of vaccinations,organ-specific sequelae,pathophysiological mechanisms,and multidisciplinary management *** addition,the impact of psychological and psychosomatic factors is also *** these crucial findings on long COVID,the current diagnostic and therapeutic strategies based on previous experience and pilot studies remain inadequate,and well-designed clinical trials should be prioritized to validate existing ***,we propose the primary challenges concerning biological knowledge gaps and efficient remedies as well as discuss the corresponding recommendations.

关键词： mechanisms consequences hundreds

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