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Sixth International Conference on Semantics,Knowledge and Grids(第六届语义、知识与网格国际会议 SKG 2010)

作者： Hai Zhuge Knowledge Grid Research Group Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences 100190 Beijing China

Natural physical space provides material basis for the birth and evolution of human beings and *** progress of human society has created the cyber space. With the rapid development of information technology, the cyber space is connecting physical space, social space and mental space to form a new world - Cyber Physical Society. The way to explore the cyber physical society is different from the way to explore the natural physical space and society. This paper describes the ideal of the Cyber Physical Society, and presents its distinguished characteristics and scientific issues. Research on the Cyber Physical Society could lead to the revolution of society, science and technology.

关键词： Semantics Space technology Aerospace electronics Humans Materials Communities Sensors

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Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms

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Genomics, Proteomics & Bioinformatics 2023年第5期21卷 913-925页

作者： Bin Huang Lupeng Kong Chao Wang Fusong Ju Qi Zhang Jianwei Zhu Tiansu Gong Haicang Zhang Chungong Yu Wei-Mou Zheng Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Changping Laboratory Beijing 102206China Microsoft Research AI4Science Beijing 100080China Huawei Noah’s Ark Lab Wuhan 430206China Zhongke Big Data Academy Zhengzhou 450046China Institute of Theoretical Physics Chinese Academy of SciencesBeijing 100190China

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer *** researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native *** research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic ***,we have also witnessed the great success of deep learning in protein structure *** this review,we present a survey of the efforts for protein structure *** compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep *** short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

关键词： Protein folding Protein structure prediction Deep learning Transformer Language model

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A flexible stem-based local search algorithm for predicting RNA secondary structures including pseudoknots

A flexible stem-based local search algorithm for predicting ...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Chen, Xiang He, Si-Min Bu, Dong-Bo Chen, Run-Sheng Gao, Wen Key Lab. of Intelligent Information Processing Chinese Academy of Sciences Beijing 100080 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Insitute of Biophysics Chinese Academy of Sciences Beijing 100101 China Graduate University Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)1424415098

We demonstrate a new algorithm named FlexStem to predict RNA secondary structures with pseudoknots. Our approach is based on the free energy minimization criterion, and utilizes a sophisticated energy model that is more accurate and supports more types of pseudoknots. By using a "maximal stem" and "stem merging" strategy, the search space for RNA structure prediction is significantly reduced. We have also developed a greedy search algorithm with perturbation on stems. The FlexStem algorithm is applied to a large number of sequences taken from Sprinzl, Pseudobase and other reliable resource. We find that FlexStem outperforms the well-known optimal and heuristic algorithms such as Mfold, PKNOTS, HotKnots and ILM in overall sensitivity and has comparable results to those algorithms in overall specificity. Performance evaluation demonstrate that FlexStem requires significantly less time than the optimal algorithm PKNOTS. In addition, our algorithm has better prediction results on pseudoknot-free sequences compared to competing algorithms. ©2007 IEEE.

关键词： Heuristic algorithms

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Revisiting Adversarial Robustness Distillation: Robust Soft labels Make Student Better

Revisiting Adversarial Robustness Distillation: Robust Soft ...

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International Conference on Computer Vision (ICCV)

作者： Bojia Zi Shihao Zhao Xingjun Ma Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan Univeristy Shanghai Collaborative Innovation Center on Intelligent Visual Computing School of Information Technology Deakin University Geelong Australia

ISBN: (纸本)9781665428132

Adversarial training is one effective approach for training robust deep neural networks against adversarial attacks. While being able to bring reliable robustness, adversarial training (AT) methods in general favor high capacity models, i.e., the larger the model the better the robustness. This tends to limit their effectiveness on small models, which are more preferable in scenarios where storage or computing resources are very limited (e.g., mobile devices). In this paper, we leverage the concept of knowledge distillation to improve the robustness of small models by distilling from adversarially trained large models. We first revisit several state-of-the-art AT methods from a distillation perspective and identify one common technique that can lead to improved robustness: the use of robust soft labels – predictions of a robust model. Following this observation, we propose a novel adversarial robustness distillation method called Robust Soft label Adversarial Distillation (RSLAD) to train robust small student models. RSLAD fully exploits the robust soft labels produced by a robust (adversarially-trained) large teacher model to guide the student’s learning on both natural and adversarial examples in all loss terms. We empirically demonstrate the effectiveness of our RSLAD approach over existing adversarial training and distillation methods in improving the robustness of small models against state-of-the-art attacks including the AutoAttack. We also provide a set of understandings on our RSLAD and the importance of robust soft labels for adversarial robustness distillation. Code: https://***/zibojia/RSLAD.

关键词： Training Deep learning Computer vision Codes Computational modeling Neural networks Predictive models

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Weighted ROC curve in cost space: extending AUC to cost-sensitive learning 23

Weighted ROC curve in cost space: extending AUC to cost-sens...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Huiyang Shao Qianqian Xu Zhiyong Yang Peisong Wen Peifeng Gao Qingming Huang Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and BDKM University of Chinese Academy of Sciences

In this paper, we aim to tackle flexible cost requirements for long-tail datasets, where we need to construct a (1) cost-sensitive and (2) class-distribution robust learning framework. The misclassification cost and the area under the ROC curve (AUC) are popular metrics for (1) and (2), respectively. However, limited by their formulations, models trained with AUC are not well-suited for cost-sensitive decision problems, and models trained with fixed costs are sensitive to the class distribution shift. To address this issue, we present a new setting where costs are treated like a dataset to deal with arbitrarily unknown cost distributions. Moreover, we propose a novel weighted version of AUC where the cost distribution can be integrated into its calculation through decision thresholds. To formulate this setting, we propose a novel bilevel paradigm to bridge weighted AUC (WAUC) and cost. The inner-level problem approximates the optimal threshold from sampling costs, and the outer-level problem minimizes the WAUC loss over the optimal threshold distribution. To optimize this bilevel paradigm, we employ a stochastic optimization algorithm (SACCL) which enjoys the same convergence rate (O(ε-4)) with the SGD. Finally, experiment results show that our algorithm performs better than existing cost-sensitive learning methods and two-stage AUC decisions approach.

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Revisiting adversarial robustness distillation: Robust soft labels make student better

arXiv

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arXiv 2021年

作者： Zi, Bojia Zhao, Shihao Ma, Xingjun Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan Univeristy Shanghai Collaborative Innovation Center on Intelligent Visual Computing School of Information Technology Deakin University Geelong Australia

Adversarial training is one effective approach for training robust deep neural networks against adversarial attacks. While being able to bring reliable robustness, adversarial training (AT) methods in general favor high capacity models, i.e., the larger the model the better the robustness. This tends to limit their effectiveness on small models, which are more preferable in scenarios where storage or computing resources are very limited (e.g., mobile devices). In this paper, we leverage the concept of knowledge distillation to improve the robustness of small models by distilling from adversarially trained large models. We first revisit several state-of-the-art AT methods from a distillation perspective and identify one common technique that can lead to improved robustness: the use of robust soft labels - predictions of a robust model. Following this observation, we propose a novel adversarial robustness distillation method called Robust Soft label Adversarial Distillation (RSLAD) to train robust small student models. RSLAD fully exploits the robust soft labels produced by a robust (adversarially-trained) large teacher model to guide the student's learning on both natural and adversarial examples in all loss terms. We empirically demonstrate the effectiveness of our RSLAD approach over existing adversarial training and distillation methods in improving the robustness of small models against state-of-the-art attacks including the AutoAttack. We also provide a set of understandings on our RSLAD and the importance of robust soft labels for adversarial robustness distillation. Code: https://***/zibojia/RSLAD. © 2021, CC BY.

关键词： Distillation

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Risk analysis of system security based on evidence theory

Risk analysis of system security based on evidence theory

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2010 International Conference on Computer Design and Applications, ICCDA 2010

作者： Xu, Peida Xu, Jianling Deng, Yong Su, Xiaoyan Key Laboratory of Digital Agricultural Early-warning Technology Ministry of Agriculture of China Beijing China Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University China School of Electronics and Information Technology Shanghai Jiao Tong University Shanghai China Grain Economics Institute Nanjing University of Finance and Economics Nanjing China Hangzhou Key Lab. of E-Business and Information Security Hangzhou Normal University Hanzhou Zhejiang China Hangzhou Key Lab. of E-Business and Information Security Hangzhou Normal University Shanghai China

ISBN: (纸本)9781424471638

Risk evaluation is very important to the design and improvement of physical protection systems. In this paper, an evaluation method of multi-source information fusion is proposed based on the D-S evidence theory. In the proposed method, each individual component of the protection system in the simulated plane is modeled. Then, the threat report of each component according to the specific tactics is determined based on its real environment. Finally, the comprehensive threat distribution is obtained based on through the D-S evidence theory to combine multi-sources information. The proposed method can easily applied to the evaluation of the effectiveness of the protection system. We make the total threat of the protection system lowest through changes of the protection resources allocation. A numerical example is used to illustrate the efficiency of the proposed method. © 2010 IEEE.

关键词： information fusion

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A HSC-based sample selection method for Support Vector Machine

A HSC-based sample selection method for Support Vector Machi...

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International Conference on Machine Learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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SFTA: Spiking Neural Networks Vulnerable to Spiking Feature Transferable Attack

SFTA: Spiking Neural Networks Vulnerable to Spiking Feature ...

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IEEE International Symposium on Parallel and Distributed processing with Applications and IEEE International Conference on Ubiquitous computing and Communications (ISPA/IUCC)

作者： Xuanwei Lin Chen Dong Ximeng Liu College of Computer and Data Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University) Key Lab of Information Security of Network Systems (Fuzhou University)

Many recent works have shown that spiking neural networks (SNNs) are vulnerable to well-crafted adversarial attacks in the white-box setting, which may lead to misclassification of the trained model. However, the information about the model is in a black-box setting and is usually unknown to the attacker, e.g., the parameters and structure of the model. So it is more challenging to counter the attack in the black-box setting, and there is still little research on this aspect in SNNs. Therefore, this work describes a transfer-based black box adversarial attack against spike feature space representations, providing insights for robust spike features in SNNs models. The attack is explicitly designed to be transferable, and more robust feature gradients are obtained by accumulating the feature gradients of the intermediate layers. Then we can successfully attack the black-box model by suppressing robust positive features and enhancing negative features. Extensive experimental evaluation demonstrates the proposed method’s effectiveness and superior performance on static and neuromorphic datasets. i.e., The attack success is 89.72% and 90% on the static MNIST and CIFAR10 datasets, and 85.97% and 69.09% on the neuromorphic N-MNIST and CIFARO-DVS datasets.

关键词： Correlation Neuromorphics Neural networks Closed box Ubiquitous computing Optimization Glass box

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Design of low-profile wide-band high-gain circularly polarized antenna based on metasurface

Design of low-profile wide-band high-gain circularly polariz...

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International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz Applications (NEMO)

作者： Wei Zhang Liang Zhang Xianliang Wu key lab of intelligent computing & signal processing ministry of education anhui university Anhui University Hefei China key lab of intelligent computing & signal processing ministry of education anhui university AnHui Province Key Laboratory of simulation and design for Electronic information system Hefei Normal university Hefei Normal University Hefei China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

This paper proposes a wide-band low-profile high-gain microstrip slot antenna loaded with a mushroom-type metasurface structure. By directly loading the metamaterial structure on the upper surface of the slot antenna, low profile loading characteristics are realized and the antenna bandwidth is expanded. And by inserting the metal line array in the metamaterial dielectric board to improve the antenna gain. Change the gap width through the metasurface element with corner cut to produce a phase difference and realize circularly polarized. Simulation test and analysis of antenna through HFSS simulation software. The results show that the antenna with this metasurface has a profile height of only 0.09×λ(λ =27 mm), a relative impedance band-width of 20.4% (S 11

关键词： Slot antennas Microstrip antennas Metasurfaces Metamaterials Microstrip Loaded antennas Gain

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