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Combining symbolic execution and model checking to verify mpi programs

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arXiv 2018年

作者： Yu, Hengbiao Chen, Zhenbang Fu, Xianjin Wang, Ji Su, Zhendong Sun, Jun Huang, Chun Dong, Wei College of Computer National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Department of Computer Science ETH Zurich Switzerland School of Information Systems Singapore Management University Singapore

Message passing is the standard paradigm of programming in high-performance computing. However, verifying Message Passing Interface (MPI) programs is challenging, due to the complex program features (such as non-determinism and non-blocking operations). In this work, we present MPI symbolic verifier (MPI-SV), the first symbolic execution based tool for automatically verifying MPI programs with non-blocking operations. MPI-SV combines symbolic execution and model checking in a synergistic way to tackle the challenges in MPI program verification. The synergy improves the scalability and enlarges the scope of verifiable properties. We have implemented MPI-SV1 and evaluated it with 111 real-world MPI verification tasks. The pure symbolic execution-based technique successfully verifies 61 out of the 111 tasks (55%) within one hour, while in comparison, MPI-SV verifies 100 tasks (90%). On average, compared with pure symbolic execution, MPI-SV achieves 19x speedups on verifying the satisfaction of the critical property and 5x speedups on finding violations. Copyright © 2018, The Authors. All rights reserved.

关键词： Model checking

Reprogrammable 3D Mesostructures Through Compressive Buckling of Thin Films with Prestrained Shape Memory Polymer

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Acta Mechanica Solida Sinica 2018年第5期31卷 589-598页

作者： Xiaogang Guo Zheng Xu Fan Zhang Xueju Wang Yanyang Zi John A. Rogers Yonggang Huang Yihui Zhang Department of Engineering Mechanics Center for Mechanics and Materials and Center for Flexible Electronics Technology AML Tsinghua University Beijing 100084 China State Key Laboratory for Manufacturing and Systems Engineering School of Mechanical Engineering Xi'an Jiaotong University Xi'an 710049 China Simpson Querrey Institute and Feinberg Medical School Center for Bio-Integrated Electronics Northwestern University Evanston IL 60208 USA Departments of Materials Science and Engineering Biomedical Engineering Chemistry Mechanical Engineering Electrical Engineering and Computer Science and Neurological Surgery Center for Bio-Integrated Electronics Simpson Querrey Institute for Nano/biotechnology Northwestern University Evanston IL 60208 USA Departments of Civil and Environmental Engineering Mechanical Engineering and Materials Science and Engineering Center for Bio-Integrated Electronics Northwestern University Evanston IL 60208 USA

The mechanically guided assembly that relies on the compressive buckling of strate- gically patterned 2D thin films represents a robust route to complex 3D mesostructures in advanced materials and even functional micro-devices. Based on this approach, formation of complex 3D configurations with suspended curvy features or hierarchical geometries remains a challenge. In this paper, we incorporate the prestrained shape memory polymer in the 2D precur- sor design to enable local rolling deformations after the mechanical assembly through compressive buckling. A theoretical model captures quantitatively the effect of key design parameters on local rolling deformations. The combination of precisely controlled global buckling and local rolling expands substantially the range of accessible 3D configurations. The combined experimental and theoretical studies over a dozen of examples demonstrate the utility of the proposed strategy in achieving complex reprogrammable 3D mesostructures.

关键词： Mechanically guided 3D assembly Reprogrammable 3D mesostructures Shapememory polymer Buckling Rolling

Stability Analysis and Bifurcation Control of a Delayed Fractional Order GRNs Model

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Stability Analysis and Bifurcation Control of a Delayed Frac...

第37届中国控制会议

作者： Feng Liu Jie Ren Guang Ling Longsheng Wei Shiqi Zheng Hua Wang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Science Wuhan University of Technology Department of Mechanical Engineering Boston University

In this paper, a fractional order genetic regulatory network system（GRNs） with delay is considered. Firstly, the stability is investigated and the conditions of the existence for Hopf bifurcation are attained by analyzing it’s characteristic equation. Then combining the analysis we can derive that the fractional order GRNs will generate Hopf bifurcation as the GRNs with specific parameter values. A fractional PD controller can be used to control the bifurcation behaviors of the delayed fractional order GRNs. Finally, some numerical examples are exploited to illustrate the validity of theoretical analysis.

关键词： Stability Hopf bifurcation fractional order GRNs control

Chinese poetry generation with a salient-clue mechanism

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arXiv 2018年

作者： Yi, Xiaoyuan Li, Ruoyu Sun, Maosong Department of Computer Science and Technology Tsinghua University Institute for Artificial Intelligence Tsinghua University State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing Advanced Innovation Center for Imaging Technology Capital Normal University 6ESTATES PTE LTD Singapore Singapore

As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations. Automatic poetry composition is a challenge for AI. In recent years, significant progress has been made in this area benefiting from the development of neural networks. However, the coherence in meaning, theme or even artistic conception for a generated poem as a whole still remains a big problem. In this paper, we propose a novel Salient-Clue mechanism for Chinese poetry generation. Different from previous work which tried to exploit all the context information, our model selects the most salient characters automatically from each so-far generated line to gradually form a salient clue, which is utilized to guide successive poem generation process so as to eliminate interruptions and improve coherence. Besides, our model can be flexibly extended to control the generated poem in different aspects, for example, poetry style, which further enhances the coherence. Experimental results show that our model is very effective, outperforming three strong baselines. Copyright © 2018, The Authors. All rights reserved.

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Advancing tabu and restart in local search for maximum weight cliques

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arXiv 2018年

作者： Fan, Yi Li, Nan Li, Chengqian Ma, Zongjie Latecki, Longin Jan Su, Kaile Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China Department of Computer Science Jinan University Guangzhou China Institute for Integrated and Intelligent Systems Griffith University BrisbaneQLD Australia Department of Computer and Information Sciences Temple University PhiladelphiaPA United States Department of Computer Science Sun Yat-sen University Guangzhou China

The tabu and restart are two fundamental strategies for local search. In this paper, we improve the local search algorithms for solving the Maximum Weight Clique (MWC) problem by introducing new tabu and restart strategies. Both the tabu and restart strategies proposed are based on the notion of a local search scenario, which involves not only a candidate solution but also the tabu status and unlocking relationship. Compared to the strategy of configuration checking, our tabu mechanism discourages forming a cycle of unlocking operations. Our new restart strategy is based on the re-occurrence of a local search scenario instead of that of a candidate solution. Experimental results show that the resulting MWC solver outperforms several state-of-theart solvers on the DIMACS, BHOSLIB, and two benchmarks from practical applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking

Incorporating Chinese characters of words for lexical sememe prediction

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arXiv 2018年

作者： Jin, Huiming Zhu, Hao Liu, Zhiyuan Xie, Ruobing Sun, Maosong Lin, Fen Lin, Leyu Shenyuan Honors College Beihang University Beijing China Beijing National Research Center for Information Science and Technology State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Jiangsu Collaborative Innovation Center for Language Ability Jiangsu Normal University Xuzhou221009 China Search Product Center WeChat Search Application Department Tencent China

Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their sememes by linguists, and form linguistic commonsense knowledge bases widely used in various NLP tasks. Recently, the lexical sememe prediction task has been introduced. It consists of automatically recommending sememes for words, which is expected to improve annotation efficiency and consistency. However, existing methods of lexical sememe prediction typically rely on the external context of words to represent the meaning, which usually fails to deal with low-frequency and out-of-vocabulary words. To address this issue for Chinese, we propose a novel framework to take advantage of both internal character information and external context information of words. We experiment on HowNet, a Chinese sememe knowledge base, and demonstrate that our framework outperforms state-of-the-art baselines by a large margin, and maintains a robust performance even for low-frequency words. Copyright © 2018, The Authors. All rights reserved.

关键词： Knowledge based systems

Remark on some π variants 3rd

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Remark on some π variants

3rd International Symposium on Dependable Software Engineering: Theories, Tools and Applications, SETTA 2017

作者： Xue, Jianxin Long, Huan Fu, Yuxi College of Computer and Information Engineering Shanghai Polytechnic University Shanghai China BASICS MOE-MS Key Laboratory for Intelligent Computing and Intelligent Systems Department of Computer Science Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783319694825

Two π variants that restrict the use of received names are studied. For either variant the external characterization of the absolute equality is given using a family of bisimulations;the expressive completeness of the calculus is established;and a complete equational proof system is constructed. The relative expressiveness between the two variants and their relationship to π are revealed in terms of subbisimilarity. © 2017, Springer International Publishing AG.

关键词： Calculations

Tightly-coupled Convolutional Neural Network with Spatial-temporal Memory for Text Classification

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Tightly-coupled Convolutional Neural Network with Spatial-te...

International Joint Conference on Neural Networks

作者： Shiyao Wang Zhidong Deng State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science Tsinghua University

ISBN: (纸本)9781509061839

Although several traditional models like bag of words (BOW), n-grams, and their variants of TFIDF exhibit high performance in the field of text classification, neural network methods such as LSTM, GRU and convolutional neural network (CNN) are recently attracting increasing attention. Considering that CNN has surprising capabilities of extracting hierarchical features, combination of LSTM/GRU with CNN seems to be quite reasonable for semantic representation and sequence analysis. On the other hand, it is also a promising subject to enable CNN to have memory embeddings and/or recurrent pathway. In this paper, we propose a novel tightly-coupled convolutional neural network with spatial-temporal memory (TCNN-SM). It comprises feature-representation and memory functional columns. Feature-representation functional column in our TCNN-SM actually performs hierarchical feature extraction as regular CNN does while memory functional column retains memories of different granularity and fulfills selective memory for historical information. In order to validate effectiveness and efficiency of the proposed TCNN-SM, we conduct extensive experiments on AG's News public dataset. The experimental results show that our new TCNN-SM achieves 7.99% test error, which has the best performance among other existing deep learning methods and is very close to state of the art results yielded using classical n-grams algorithm.

关键词： Text categorization Memory tight coupling Memory Neural network test error Feature extraction PURSE optimum performance

Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge

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Research Square

Research Square 2021年 rs3rs-571332页

作者： Roth, Holger R. Xu, Ziyue Diez, Carlos Tor Jacob, Ramon Sanchez Zember, Jonathan Molto, Jose Li, Wenqi Xu, Sheng Turkbey, Baris Turkbey, Evrim Yang, Dong Harouni, Ahmed Rieke, Nicola Hu, Shishuai Isensee, Fabian Tang, Claire Yu, Qinji Sölter, Jan Zheng, Tong Liauchuk, Vitali Zhou, Ziqi Moltz, Jan Hendrik Oliveira, Bruno Xia, Yong Maier-Hein, Klaus H. Li, Qikai Husch, Andreas Zhang, Luyang Kovalev, Vassili Kang, Li Hering, Alessa Vilaça, João L. Flores, Mona Xu, Daguang Wood, Bradford Linguraru, Marius George NVIDIA BethesdaMD United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Division of Diagnostic Imaging and Radiology Children’s National Hospital WashingtonDC United States Radiology and Imaging Sciences National Institutes of Health Clinical Center BethesdaMD United States School of Computer Science and Engineering Northwestern Polytechnical University China Heidelberg Germany Lynbrook High School San JoseCA United States Shanghai Jiao Tong University China Luxembourg Centre for Systems Biomedicine University of Luxembourg Luxembourg School of Informatics Nagoya University Japan Biomedical Image Analysis Department United Institute of Informatics Problems Belarus Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany School of Medicine University of Minho Braga Portugal ICVS/3B’s - PT Government Associate Laboratory Braga/Guimarães Portugal Algoritmi Center School of Engineering University of Minho Guimarães Portugal Heidelberg Germany Fraunhofer Institute for Digital Medicine MEVIS Lübeck Germany 2Ai – Polytechnic Institute of Cávado and Ave Barcelos Portugal School of Medicine and Health Sciences George Washington University WashingtonDC United States

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A;n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020. © 2021, CC BY.

关键词： COVID-19