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Evaluating and Constraining Hardware Assertions with Absent Scenarios

Journal of computer Science & Technology 2020年第5期35卷 1198-1216页

作者： Hui-Na Chao Hua-Wei Li Xiaoyu Song Tian-Cheng Wang Xiao-Wei Li State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518052China Department of Electrical and Computing Engineering Portland State UniversityPortlandOR 97207U.S.A.

Mining from simulation data of the golden model in hardware design verification is an effective solution to assertion *** the simulation data is inherently incomplete,it is necessary to evaluate the truth values of the mined *** paper presents an approach to evaluating and constraining hardware assertions with absent scenarios.A Belief-fail Rate metric is proposed to predict the truth/falseness of generated *** considering both the occurrences of free variable assignments and the conflicts of absent scenarios,we use the metric to sort true assertions in higher ranking and false assertions in lower *** Belief-failRate guided assertion constraining method leverages the quality of generated *** experimental results show that the Belief-failRate framework performs better than the existing *** addition,the assertion evaluating and constraining procedure can find more assertions that cover new design functionality in comparison with the previous methods.

关键词： hardware formal verification assertion generation data mining assertion evaluation assertion coverage

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Auto-MatRegressor材料性能自动预测器:解放材料机器学习"调参师"

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Science Bulletin 2023年第12期68卷 1259-1270,M0004页

作者：刘悦王双燕杨正伟 Maxim Avdeev 施思齐 School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai 200444China Materials Genome Institute Shanghai UniversityShanghai 200444China Zhejiang Laboratory Hangzhou 311100China Australian Nuclear Science and Technology Organisation Sydney 2232Australia School of Chemistry The University of SydneySydney 2006Australia

机器学习因其能够快速、精准拟合数据的潜在模式而被广泛应用于材料构效关系研究。然而,材料科学家往往需要进行繁琐的模型选择及参数寻优才能构建出高精度预测模型,为了解放材料机器学习"调参师",本文研发了基于元学习的材... 详细信息

机器学习因其能够快速、精准拟合数据的潜在模式而被广泛应用于材料构效关系研究。然而,材料科学家往往需要进行繁琐的模型选择及参数寻优才能构建出高精度预测模型,为了解放材料机器学习"调参师",本文研发了基于元学习的材料性能自动预测器,采集了60份文献公开数据集与60份标准数据集,基于此训练18种常用回归算法并获得其预测性能,定义与计算了27个刻画数据集特点的元特征,以此构建了一份蕴含建模经验的元数据集;同时,创建了表征数据集所属材料类型的类别树,将其嵌入基于距离的元学习算法,进一步耦合贝叶斯优化算法,实现领域知识和元数据协同驱动下的自动算法推荐和模型参数确定,实验结果表明,材料科学家仅需为新材料性能预测任务提供数据集,便可利用该预测器高效地构建具有与文献报道相当或更高预测精度的机器学习模型.

关键词：机器学习贝叶斯优化算法预测器材料科学家元数据参数寻优预测性能材料性能

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A Benchmark of Ocular Disease Intelligent Recognition: One Shot for Multi-disease Detection 3rd

A Benchmark of Ocular Disease Intelligent Recognition: One S...

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3rd BenchCouncil International Symposium on Benchmarking, Measuring, and Optimizing, Bench 2020

作者： Li, Ning Li, Tao Hu, Chunyu Wang, Kai Kang, Hong College of Computer Science Nankai university Tianjin300350 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Science Beijing100190 China Beijing Shanggong Medical Technology Co. Ltd. Beijing100176 China

ISBN: (纸本)9783030710576

In ophthalmology, early fundus screening is an economic and effective way to prevent blindness caused by ophthalmic diseases. Clinically, due to the lack of medical resources, manual diagnosis is time-consuming and may delay the condition. With the development of deep learning, some researches on ophthalmic diseases have achieved good results, however, most of them are just based on one disease. During fundus screening, ophthalmologists usually give diagnoses of multi-disease on binocular fundus image, so we release a dataset with 8 diseases to meet the real medical scene, which contains 10,000 fundus images from both eyes of 5,000 patients. We did some benchmark experiments on it through some state-of-the-art deep neural networks. We found simply increasing the scale of network cannot bring good results for multi-disease classification, and a well-structured feature fusion method combines characteristics of multi-disease is needed. Through this work, we hope to advance the research of related fields. © 2021, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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A Risk-Averse Just-In-Time Scheme for Learning-Based Operation of Microgrids With Coupled Electricity-Hydrogen-Ammonia Under Uncertainties

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IEEE Transactions on Sustainable Energy 2025年

作者： Li, Longyan Ning, Chao Pan, Guangsheng Zhang, Leiqi Gu, Wei Zhao, Liang Du, Wenli Shahidehpour, Mohammad Shanghai Jiao Tong University Department of Automation Shanghai200240 China Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China School of Electrical Engineering Southeast University Nanjing210096 China Zhejiang Key Laboratory of Distributed Generations and Microgrid Technology State Grid Zhejiang Electric Power Research Institute Hangzhou310014 China Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology China Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

This paper proposes a Risk-Averse Just-In-Time (RAJIT) operation scheme for Ammonia-Hydrogen-based Micro-Grids (AHMGs) to boost electricity-hydrogen-ammonia coupling under uncertainties. First, an off-grid AHMG model is developed, featuring a novel multi-mode ammonia synthesis process and a hydrogen-ammonia dual gas turbine with tunable feed-in ratios. Subsequently, a state-behavior mapping strategy linking hydrogen storage levels with the operation modes of ammonia synthesis is established to prevent cost-ineffective shutdowns. The proposed model substantially improves operational flexibility but results in a challenging nonlinear fractional program. Based upon this model, a data-driven RAJIT scheme is developed for the real-time rolling optimization of AHMGs. Unlike conventional one-size-fits-all schemes using one optimization method throughout, the data-driven RAJIT intelligently switches between cost-effective deterministic optimization and risk-averse online-learning distributionally robust optimization depending on actual risk profiles, thus capitalizing on the respective strengths of these two optimization methods. To facilitate the solution of the resulting nonlinear program, we develop an equivalent-reformulation-based solution methodology by leveraging a constraint-tightening technique. Numerical simulations demonstrate that the proposed scheme guarantees safety and yields an overall cost reduction up to 14.6% compared with several state-of-the-art methods. © 2025 IEEE.

关键词： Ammonia

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AuNP-Modulated qPCR: An Optimized system for Detecting MIR Biophotons Released in DNA Replication

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CHEMISTRY-A EUROPEAN JOURNAL 2023年第8期29卷 e202203513-e202203513页

作者： Yang, Yu Peng, Daoling Gu, Zhenglong Jiang, Lei Song, Bo State Key Laboratory of Genetic Engineering Collaborative Innovation Center of Genetics and Development School of Life Sciences Fudan University Shanghai P. R. China Key Laboratory of Theoretical Chemistry of Environment Ministry of Education School of Environment South China Normal University Guangzhou P. R. China Key Laboratory of Optical Technology and Instrument for Medicine Ministry of Education Shanghai Key Lab of Modern Optical System School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Shanghai P. R. China Key Laboratory of Bio-inspired Materials and Interfacial Science Technical Institute of Physics and Chemistry Chinese Academy of Sciences Beijing P. R. China School of Future Technology University of Chinese Academy of Sciences Beijing P. R. China Key Laboratory of Optical Technology and Instrument for Medicine Ministry of Education Shanghai Key Lab of Modern Optical System School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Shanghai P. R. China

Living systems can utilize energy with a high efficiency. Biophotons are proposed to modulate biological functions with such efficiency;however, the underlying mechanism remains unexplored, especially due to the challenge of ultraweak mid-infrared (MIR) light detection and the theoretical perturbation from spontaneous MIR emission. Here, we proposed an optimized system to detect MIR biophotons generated in the hydrolysis of deoxynucleotide triphosphates (dNTPs, energy-storing molecules similar to ATP). The system used a quantitative polymerase chain reaction (qPCR) that was modulated by gold nanoparticle (AuNP) concentration and thus by the inter-AuNP distance, which depends on the concentration above. The measurements indicate that 33- and 84-THz photons are released by dNTP hydrolysis, which can drive DNA replication. Our findings provide a novel chain-reaction-based method for detecting MIR photons in solution, and pave a way for photon-based insights to understand the highly efficient energy utilization of biology.

关键词： DNA replication energy utilization efficiency gold nanoparticles MIR biophoton detection qPCR

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SAT-based Formal Verification of Fault Injection Countermeasures for Cryptographic Circuits∗

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded systems 2024年第4期2024卷 1-39页

作者： Tan, Huiyu Gao, Pengfei Song, Fu Chen, Taolue Wu, Zhilin ShanghaiTech University Shanghai201210 China Wingsemi Technology Co. Ltd. Shanghai201203 China Bytedance Beijing100098 China Key Laboratory of System Software Chinese Academy of Sciences and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing100190 China Nanjing Institute of Software Technology Nanjing211135 China Birkbeck University of London WC1E 7HX United Kingdom

Fault injection attacks represent a type of active, physical attack against cryptographic circuits. Various countermeasures have been proposed to thwart such attacks, however, the design and implementation of which are intricate, error-prone, and laborious. The current formal fault-resistance verification approaches are limited in efficiency and scalability. In this paper, we formalize the fault-resistance verification problem and show that it is coNP-complete. We then devise a novel approach for encoding the fault-resistance verification problem as the Boolean satisfiability (SAT) problem so that modern off-the-shelf SAT solvers can be utilized. The approach is implemented in an open-source tool FIRMER which is evaluated extensively on realistic cryptographic circuit benchmarks. The experimental results show that FIRMER is able to verify fault-resistance of almost all (72/76) benchmarks in 3 minutes (the other three are verified in 35 minutes and the hardest one is verified in 4 hours). In contrast, the prior approach fails on 31 fault-resistance verification tasks even after 24 hours (per task). © 2024, Ruhr-University of Bochum. All rights reserved.

关键词： Formal verification

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A Parallel and Pipelined Hardware architecture for Fractional-Pixel Motion Estimation in AVS3

A Parallel and Pipelined Hardware Architecture for Fractiona...

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2022 IEEE International Conference on Consumer Electronics, ICCE 2022

作者： Zeng, Yuning Zhu, Xizhong Xiang, Guoqing Hao, Zhijian Zhang, Peng Huang, Xiaofeng Yan, Wei Peking University School of Software and Microelectronics Beijing China Advanced Institute of Information Technology Peking University Beijing China Peking University Shenzhen Guraduate School School of Electronic and Computer Engineering Shenzhen China Fudan University State Key Laboratory of ASIC System Shanghai China Hangzhou Dianzi University School of Communication Engineering Hangzhou China

ISBN: (纸本)9781665441544

The latest generation of video coding standards has significantly improved video coding performance. The third generation of audio video coding standards (AVS3) is one of these latest standards. However, due to the more flexible block partition mechanism in AVS3, the computational overhead brought by fractional-pixel motion estimation (FME) increases significantly, making it more difficult to implement a real-time FME hardware. In this paper, a parallel and pipelined hardware architecture supporting block sizes from 4x4 to 64x64 is proposed for FME in AVS3, and it is specially designed for coding tree unit (CTU)-level pipelined architecture of AVS3 encoder. Three necessary modules are developed in this architecture. The first module concerns derivation of motion vector predictors (MVPs). The second module is used to derive fractional-pixel motion vectors (FMVs) of coding units (CUs) partitioned by Binary-tree (BT) and Quad-Tree (QT) mechanisms. And a motion vector substitution (MVS) module is proposed to substitute the FMVs of the remaining CUs with the FMVs derived in the second module. Experimental results show that the proposed methods suffer only 0.62% performance degradation in BD-Rate. Furthermore, the architecture is implemented on XILINX ALVEO U250 FPGA at 400 MHz and the presented FME hardware design can support the real-time encoding of 4K@30fps. © 2022 IEEE.

关键词： Pipelines

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HMAMP: Hypervolume-Driven Multi-Objective Antimicrobial Peptides Design

arXiv

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arXiv 2024年

作者： Wang, Li Liu, Yiping Fu, Xiangzheng Ye, Xiucai Shi, Junfeng Yen, Gary G. Zeng, Xiangxiang The College of Computer Scienceand Electronic Engineering Hunan University Hunan Changsha China The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States The School of System Information and Engineering University of Tsukuba Ibaraki Japan Neher’s Biophysics Laboratory for Innovative Drug Discovery State Key Laboratory of Quality Research in Chinese Medicine Macau Institute for Applied Research in Medicine and Health Macau University of Science and Technology 999078 China

Antimicrobial peptides (AMPs) have exhibited unprecedented potential as biomaterials in combating multidrugresistant bacteria. Despite the increasing adoption of artificial intelligence for novel AMP design, challenges pertaining to conflicting attributes such as activity, hemolysis, and toxicity have significantly impeded the progress of researchers. This paper introduces a paradigm shift by considering multiple attributes in AMP design. Presented herein is a novel approach termed Hypervolume-driven Multi-objective Antimicrobial Peptide Design (HMAMP), which prioritizes the simultaneous optimization of multiple attributes of AMPs. By synergizing reinforcement learning and a gradient descent algorithm rooted in the hypervolume maximization concept, HMAMP effectively expands exploration space and mitigates the issue of pattern collapse. This method generates a wide array of prospective AMP candidates that strike a balance among diverse attributes. Furthermore, we pinpoint knee points along the Pareto front of these candidate AMPs. Empirical results across five benchmark models substantiate that HMAMP-designed AMPs exhibit competitive performance and heightened diversity. A detailed analysis of the helical structures and molecular dynamics simulations for ten potential candidate AMPs validates the superiority of HMAMP in the realm of multiobjective AMP design. The ability of HMAMP to systematically craft AMPs considering multiple attributes marks a pioneering milestone, establishing a universal computational framework for the multi-objective design of AMPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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An Adaptive Bionic Sensor: Enhancing Ankle Joint Tracking with High Sensitivity and Superior Cushioning Performance

SSRN

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SSRN 2024年

作者： Jin, Jianqiao Zhang, Chen Zhao, Jianyuan Yu, Minghan Lei, Ming Jin, Chun Yin, Rui Zhao, Weiwei Sauvage Laboratory for Smart Materials The School of Integrated Circuit Harbin Institute of Technology Shenzhen518055 China Shenzhen Key Laboratory of Flexible Printed Electronics Technology Harbin Institute of Technology Shenzhen518055 China State Key Laboratory of Advanced Welding & Joining Harbin Institute of Technology Harbin150001 China Human-Computer Interaction Design Lab School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China

Flexible sensors are renowned for their rapid responsiveness, high flexibility, and outstanding mechanical properties, making them ideal for applications in wearable devices, sports and health monitoring, and human-machine interfaces. To enhance the sensing performance of these flexible sensors, researchers have drawn inspiration from nature, creating various bionic microstructures. However, these structures often manifest in the macroscopic form of thin films, which do little to improve impact resistance and joint protection. In response, this work achieves a highly sensitive and impact-resistant pressure sensor by integrating bionic principles at both micro and macro levels. Specifically, at the micro level, a muscle-like hydrogel system consisting of MXene nanosheets incorporated into the double network of polyvinyl alcohol and sodium alginate matrix is selected. At the macro level, the bionic prototypes of mimosa leaves and durian spikes with good energy absorption structure, together with octopus’ sucker with good adsorption capacity are selected, and the synergistic construction of the three structures is realized through 3D printing methods. The obtained sensor has an appropriate response range, sensitivity and stability. More importantly, the displacement deformation of the hydrogel can be reduced by 91.22%, while its adhesion strength can be increased by 57.5% compared to the unstructured hydrogel. As a proof-of-concept, the proposed bionic flexible sensor can be used to monitor the motion status of the human ankle joint in real-time, thereby assisting users in making real-time judgments on gait health. © 2024, The Authors. All rights reserved.

关键词： Sodium alginate

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Chatting with GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing

arXiv

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arXiv 2023年

作者： Liu, Zhe Chen, Chunyang Wang, Junjie Chen, Mengzhuo Wu, Boyu Che, Xing Wang, Dandan Wang, Qing Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Sciences Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Monash University Melbourne Australia

Mobile apps are indispensable for people’s daily life, and automated GUI (Graphical User Interface) testing is widely used for app quality assurance. There is a growing interest in using learning-based techniques for automated GUI testing which aims at generating human-like actions and interactions. However, the limitations such as low testing coverage, weak generalization, and heavy reliance on training data, make an urgent need for a more effective approach to generate human-like actions to thoroughly test mobile apps. Inspired by the success of the Large Language Model (LLM), e.g., GPT-3 and ChatGPT, in natural language understanding and question answering, we formulate the mobile GUI testing problem as a Q&A task. We propose GPTDroid, asking LLM to chat with the mobile apps by passing the GUI page information to LLM to elicit testing scripts, and executing them to keep passing the app feedback to LLM, iterating the whole process. Within it, we extract the static context of the GUI page and the dynamic context of the iterative testing process, design prompts for inputting this information to LLM, and develop a neural matching network to decode the LLM’s output into actionable steps to execute the app. We evaluate GPTDroid on 86 apps from Google Play, and its activity coverage is 71%, with 32% higher than the best baseline, and can detect 36% more bugs with faster speed than the best baseline. GPTDroid also detects 48 new bugs on the Google Play with 25 of them being confirmed/fixed. We further summarize the capabilities of GPTDroid behind the superior performance, including semantic text input, compound action, long meaningful test trace, and test case prioritization. © 2023, CC0.

关键词： Graphical user interfaces

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