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International Conference on Applied Nonlinear Dynamics, Vibration and Control, ICANDVC 2021

作者： Ma, Guoliang Xu, Minglong Li, Hongguang School of Mechatronic Engineering Xi’an Technological University No. 2 Xuefuzhonglu Road Xi’an710021 China State Key Laboratory for Strength and Vibration of Mechanical Structures School of Aerospace Xi’an Jiaotong University No. 28 Xianning West Road Xi’an 710049 China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University No. 800 Dongchuan Road Shanghai200240 China

ISBN: (纸本)9789811659119

This paper proposes a scaled model to investigate the dynamic characteristics of suspension string and LMS adaptive control of hoop flexible structure. Firstly, the lateral vibration equation of micro segment string is established, along with the vibration frequency calculated. Also, the state space equation is theoretically obtained when the LMS adaptive algorithm is used to calculate the control force. Then, the scaled model is established by the finite element method before and after suspension, and there exist shaking mode and nodding mode. With this, a vibration control simulation is conducted for the hoop flexible structure to obtain the effect of LMS adaptive control. In the program, fixed step and variable step solvers are used to solve the state space equation, and Pulse, Square, Sawtooth and Random signals are applied as excitations. The simulation results show that the response curves are all obviously decreased after suspension. Moreover, the LMS adaptive control has a significant effect on steady-state response. It is concluded that the control results can be used as a reference for the large hoop truss antenna structure. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： adaptive algorithms

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Luckiness in Multiscale Online Learning 36

Luckiness in Multiscale Online Learning

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36th Conference on Neural Information Processing Systems (NeurIPS)

作者： Perez-Ortiz, Muriel Felipe Koolen, Wouter M. Ctr Wiskunde & Informat CWI Amsterdam Netherlands Univ Twente Enschede Netherlands

ISBN: (纸本)9781713871088

algorithms for full-information online learning are classically tuned to minimize their worst-case regret. Modern algorithms additionally provide tighter guarantees outside the adversarial regime, most notably in the form of constant pseudoregret bounds under statistical margin assumptions. We investigate the multiscale extension of the problem where the loss ranges of the experts are vastly different. Here, the regret with respect to each expert needs to scale with its range, instead of the maximum overall range. We develop new multiscale algorithms, tuning schemes and analysis techniques to show that worst-case robustness and adaptation to easy data can be combined at a negligible cost. We further develop an extension with optimism and apply it to solve multiscale two-player zero-sum games. We demonstrate experimentally the superior performance of our scale-adaptive algorithm and discuss the subtle relationship of our results to Freund's 2016 open problem.

关键词： adaptive algorithms

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BadPrompt: Backdoor Attacks on Continuous Prompts 36

BadPrompt: Backdoor Attacks on Continuous Prompts

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36th Conference on Neural Information Processing Systems (NeurIPS)

作者： Cai, Xiangrui Xu, Haidong Xu, Sihan Zhang, Ying Yuan, Xiaojie Nankai Univ Coll Comp Sci TKLNDST TMCC Tianjin Peoples R China Nankai Univ Coll Cyber Sci TKLNDST Tianjin Peoples R China Nankai Univ Coll Cyber Sci TKLNDST TMCC Tianjin Peoples R China Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China

ISBN: (纸本)9781713871088

The prompt-based learning paradigm has gained much research attention recently. It has achieved state-of-the-art performance on several NLP tasks, especially in the few-shot scenarios. While steering the downstream tasks, few works have been reported to investigate the security problems of the prompt-based models. In this paper, we conduct the first study on the vulnerability of the continuous prompt learning algorithm to backdoor attacks. We observe that the few-shot scenarios have posed a great challenge to backdoor attacks on the prompt-based models, limiting the usability of existing NLP backdoor methods. To address this challenge, we propose BadPrompt, a lightweight and task-adaptive algorithm, to backdoor attack continuous prompts. Specially, BadPrompt first generates candidate triggers which are indicative for predicting the targeted label and dissimilar to the samples of the non-targeted labels. Then, it automatically selects the most effective and invisible trigger for each sample with an adaptive trigger optimization algorithm. We evaluate the performance of BadPrompt on five datasets and two continuous prompt models. The results exhibit the abilities of BadPrompt to effectively attack continuous prompts while maintaining high performance on the clean test sets, outperforming the baseline models by a large margin. The source code of BadPrompt is publicly available (1).

关键词： adaptive algorithms

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adaptive Time-Delay Control for Cable-Driven Manipulators With Enhanced Nonsingular Fast Terminal Sliding Mode

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2021年第3期68卷 2356-2367页

作者： Wang, Yaoyao Li, Shizhen Wang, Dan Ju, Feng Chen, Bai Wu, Hongtao Nanjing Univ Aeronaut & Astronaut Coll Mech & Elect Engn Nanjing 210016 Peoples R China Zhejiang Univ State Key Lab Fluid Power & Mechatron Sys Hangzhou 310027 Peoples R China Shandong Univ Inst Marine Sci & Technol Qingdao 266237 Peoples R China

For high control performance of cable-driven manipulators, we design a new adaptive time-delay control (ATDC) using enhanced nonsingular fast terminal sliding mode (NFTSM). The proposed ATDC uses time-delay estimation (TDE) to acquire the lumped dynamics in a simple way and founds a practical model-free structure. Then, a new enhanced NFTSM surface is developed to ensure fast convergence and high control accuracy. To acquire good comprehensive performance under lumped uncertainties, in this article we propose a novel adaptive algorithm for the control gain, which can regulate itself based on the control errors timely and accurately. Benefitting from the TDE and the proposed enhanced NFTSM surface and adaptive control gain, our proposed ATDC is model-free, fast response, and accurate. Theoretical analysis concerning system stability, and control precision and convergence speed are given based on Lyapunov theory. Finally, the advantages of our ATDC over existing methods are verified with comparative experiments.

关键词： Convergence adaptive algorithms Uncertainty Manipulator dynamics Estimation adaptive time-delay control (ATDC) cable-driven manipulators nonsingular fast terminal sliding mode (NFTSM) time-delay estimation (TDE)

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adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation

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OPERATIONS RESEARCH 2024年第6期72卷 2612-2630页

作者： He, Shengyi Jiang, Guangxin Lam, Henry Fu, Michael C. Columbia Univ Dept Ind Engn & Operat Res New York NY 10027 USA Harbin Inst Technol Sch Management Harbin 150001 Peoples R China Univ Maryland Syst Res Inst College Pk MD 20740 USA Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA

In solving simulation-based stochastic root-finding or optimization problems that involve rare events, such as in extreme quantile estimation, running crude Monte Carlo can be prohibitively inefficient. To address this issue, importance sampling can be employed to drive down the sampling error to a desirable level. However, selecting a good importance sampler requires knowledge of the solution to the problem at hand, which is the goal to begin with and thus forms a circular challenge. We investigate the use of adaptive importance sampling to untie this circularity. Our procedure sequentially updates the importance sampler to reach the optimal sampler and the optimal solution simultaneously, and can be embedded in both sample-average-approximation-type algorithms and stochastic-approximation-type algorithms. Our theoretical analysis establishes strong consistency and asymptotic normality of the resulting estimators. We also demonstrate, via a minimax perspective, the key role of using adaptivity in controlling asymptotic errors. Finally, we illustrate the effectiveness of our approach via numerical experiments.

关键词： Monte Carlo simulation importance sampling adaptive algorithms quantile estimation stochastic root finding stochastic optimization central limit theorem

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Approximate Function Evaluation via Multi-Armed Bandits 25

Approximate Function Evaluation via Multi-Armed Bandits

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International Conference on Artificial Intelligence and Statistics

作者： Baharav, Tavor Z. Cheng, Gary Pilanci, Mert Tse, David Stanford Univ Stanford CA 94305 USA

We study the problem of estimating the value of a known smooth function f at an unknown point mu is an element of R-n, where each component mu(i) can be sampled via a noisy oracle. Sampling more frequently components of mu corresponding to directions of the function with larger directional derivatives is more sample-efficient. However, as mu is unknown, the optimal sampling frequencies are also unknown. We design an instance-adaptive algorithm that learns to sample according to the importance of each coordinate, and with probability at least 1 - delta returns an epsilon accurate estimate of f(mu). We generalize our algorithm to adapt to heteroskedastic noise, and prove asymptotic optimality when f is linear. We corroborate our theoretical results with numerical experiments, showing the dramatic gains afforded by adaptivity.

关键词： adaptive algorithms

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Complex Adaptation in Audio Feedback Networks for the Synthesis of Music and Sounds

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COMPUTER MUSIC JOURNAL 2021年第1期45卷 6-23页

作者： Sanfilippo, Dario Piazza Federico di Svevia 1 I-95121 Catania Italy

This article presents recent outcomes of the author's research on musical complex adaptive systems (CASs). The first part focuses on the concepts of adaptation and complexity within the framework of CASs and suggests a rigorous placing of the concepts within the musical domain. This analysis involves a distinction of the notions of context and information between the engineering field of information theory and the philosophical one of radical constructivism. I conclude this section by showing that, in this approach, information and context are mutually determining. Then, I introduce a technique related to the notion of evolvability in biology and genetic algorithms and that has significantly increased the complexity and long-term variety in music systems during autonomous evolutions. This technique distributes adaptation across higher levels and allows the system to reorganize the relationships among its agents and their structure circularly while interpreting and constructing its context. To conclude, an autonomous live performance piece from 2019-2020, "Constructing Realities (Homage to Heinz von Foerster)," which implements the theories mentioned above, is described, showing DSP processes and techniques that relate to evolvability, autopoiesis, fitness, and complexity through agent-based modeling. This article is accompanied by a companion article discussing the technical aspects of information processing algorithms, which are an essential part for the implementation of music CASs: "Time-Domain adaptive algorithms for Low- and High-Level Audio Information Processing."

关键词： adaptive algorithms

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An adaptive approach for generating Voronoi diagrams for residential areas containing adjacent polygons

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INTERNATIONAL JOURNAL OF DIGITAL EARTH 2024年第1期17卷

作者： Wu, Jianhua Dai, Peng Hu, Xiang Zhao, Yu Xiong, Jiaqi Hu, Lieyun Wei, Ning Tu, Haowen Jiangxi Normal Univ Sch Geog & Environm Nanchang 330022 Peoples R China Shanghai Surveying & Mapping Inst Shanghai Peoples R China Shanggao Cty Nat Resources Bur Shanggao Peoples R China Daye City Nat Resources & Planning Bur Daye Peoples R China Xian Meihang Remote Sensing Informat Co Ltd Xian Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China

This study proposes AVARA, an innovative adaptive Voronoi diagram algorithm explicitly designed for residential areas containing adjacent polygons. AVARA addresses the complex task of balancing computational efficiency and boundary position accuracy in existing Voronoi algorithms based on occurrence element discretization and difficulties constructing Voronoi diagrams for topologically adjacent polygonal objects. AVARA leverages interpolation strategies established on the classification result of neighbor pairs using co-edge relationships of residential area centroids. Additionally, AVARA incorporates graphical Boolean operations and node merging. The experiments used residential area datasets at 1:50,000, 1:10,000, and 1:2,000 map scales. The results showed that AVARA outperformed the Voronoi diagram algorithms based on intervisible points (VBIVP) and the equal-interval dense point method (VBEDP). AVARA achieved the highest accuracy rate in constructing Voronoi diagrams, with a 50% improvement. It also exhibited superior local position accuracy, demonstrating significant time performance improvements of 349.12% and 90.7% using datasets at map scales of 1:50,000 and 1:10,000, respectively. In the experiment using a dataset at a map scale of 1:2,000, AVARA consumed slightly less time than VBEDP with a 0.6 m interval. These findings highlight the significant overall advantage of AVARA. Additionally, the effectiveness of the algorithm was further validated using a large-scale building dataset.

关键词： Areal residential area delaunay triangulation network Voronoi diagram adaptive algorithms

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Design and Analysis of the Fractional-Order Complex Least Mean Square (FoCLMS) Algorithm

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2021年第10期40卷 5152-5181页

作者： Ahmad, Jawwad Zubair, Muhammad Rizvi, Syed Sajjad Hussain Shaikh, Muhammad Shafique Usman Inst Technol Dept Elect Engn ST-13Block-7Abu Hasan Isphahani Rd Karachi 75300 Pakistan IQRA Univ Fac Engn Sci & Technol North Karachi CampusSect 7b North Karachi Twp Karachi 75850 Pakistan Shaheed Zulfikar Ali Bhutto Inst Sci & Technol Fac Comp Sci SZABIST Block 5 Clifton Karachi 75600 Pakistan King Abdulaziz Univ Elect & Comp Engn Dept Al Ehtifalat St Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Al Ehtifalat St Jeddah 21589 Saudi Arabia

In this work, a new class of stochastic gradient algorithm is developed based on fractional calculus. Unlike the existing algorithms, the concept of complex fractional gradient is introduced by employing Caputo's fractional derivative which results in a fractional steepest descent algorithm and a fractional-order complex LMS (FoCLMS) algorithm. We demonstrate that with the Caputo's fractional gradient definition, the Weiner solution remains invariant. Convergence analysis of the proposed FoCLMS algorithm is presented for both transient and steady state scenarios. Consequently, expressions for the learning curves and steady state EMSE are derived. Our theoretical developments are validated by simulation experiments. Extensive simulations are presented to investigate all possible scenarios: channel with negative weights and real input data, channel with positive weights and complex input data, and channel with complex weights and complex input data.

关键词： adaptive algorithms Least mean squares algorithm Fractional calculus Fractional complex derivative FoCLMS algorithm Transient analysis Steady-state analysis Channel identification problem

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Stochastic Approximation With Iterate-Dependent Markov Noise Under Verifiable Conditions in Compact State Space With the Stability of Iterates Not Ensured

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2021年第12期66卷 5941-5954页

作者： Karmakar, Prasenjit Bhatnagar, Shalabh Indian Inst Sci Bangalore Bangalore 560012 Karnataka India Technion Israel Inst Technol Dept Elect Engn IL-3200003 Haifa Israel Indian Inst Sci Dept Comp Sci & Automat Bangalore 560012 Karnataka India Indian Inst Sci Robert Bosch Ctr Cyber Phys Syst Bangalore 560012 Karnataka India

This article compiles several aspects of the dynamics of stochastic approximation algorithms with Markov iterate-dependent noise when the iterates are not known to be stable beforehand. We achieve the same by extending the lock-in probability (i.e., the probability of convergence of the iterates to a specific attractor of the limiting ordinary differential equation (o.d.e.) given that the iterates are in its domain of attraction after a sufficiently large number of iterations (say) n(0)) framework to such recursions. Specifically, with the more restrictive assumption of Markov iterate-dependent noise supported on a bounded subset of the Euclidean space, we give a lower bound for the lock-in probability. We use these results to prove almost sure convergence of the iterates to the specified attractor when the iterates satisfy an asymptotic tightness condition. The novelty of our approach is that if the state space of the Markov process is compact, we prove almost sure convergence under much weaker assumptions compared to the work by Andrieu et al., which solves the general state-space case under much restrictive assumptions by providing sufficient conditions for stability of the iterates. We also extend our single-timescale results to the case where there are two separate recursions over two different timescales. This, in turn, is shown to be useful in analyzing the tracking ability of general adaptive algorithms. Additionally, we show that our results can be used to derive a sample complexity estimate of such recursions, which then can be used for step-size selection.

关键词： adaptive algorithms lock-in probability Markov noise sample complexity

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