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arXiv 2024年

作者： Zhang, Zhipeng Zhang, Yanjun Sun, Jian State Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

This paper introduces an innovative singularityfree output feedback model reference adaptive control (MRAC) method applicable to a wide range of continuous-time linear time-invariant (LTI) systems with general relative degrees. Unlike existing solutions such as Nussbaum and multiple-model-based methods, which manage unknown high-frequency gains through persistent switching and repeated parameter estimation, the proposed method circumvents these issues without prior knowledge of the high-frequency gain or additional design conditions. The key innovation of this method lies in transforming the estimation error equation into a linear regression form via a modified MRAC law with a piecewise constant tuning gain developed in this work. This represents a significant departure from existing MRAC systems, where the estimation error equation is typically in a bilinear regression form. The linear regression form facilitates the direct estimation of all unknown parameters, thereby simplifying the adaptive control process. The proposed method preserves closed-loop stability and ensures asymptotic output tracking, overcoming some of the limitations associated with existing methods like Nussbaum and multiple-model based methods. The practical efficacy of the developed MRAC method is demonstrated through detailed simulation results within an aircraft control system *** Codes 93A10, 93B52, 93C40, 93D20 Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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Instruction Pre-Training: Language Models are Supervised Multitask Learners

arXiv

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arXiv 2024年

作者： Cheng, Daixuan Gu, Yuxian Huang, Shaohan Bi, Junyu Huang, Minlie Wei, Furu Microsoft Research The CoAI Group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Tsinghua University China

Unsupervised multitask pre-training has been the critical method behind the recent success of language models (LMs). However, supervised multitask learning still holds significant promise, as scaling it in the post-training stage trends towards better generalization. In this paper, we explore supervised multitask pretraining by proposing Instruction Pre-Training, a framework that scalab.y augments massive raw corpora with instruction-response pairs to pre-train LMs. The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models. In our experiments, we synthesize 200M instruction-response pairs covering 40+ task categories to verify the effectiveness of Instruction Pre-Training. In pre-training from scratch, Instruction Pre-Training not only consistently enhances pre-trained base models but also benefits more from further instruction tuning. In continual pre-training, Instruction Pre-Training enables Llama3-8B to be comparable to or even outperform Llama3-70B. Our model, code, and data are availab.e at https://***/microsoft/LMOps. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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Reconstructing Three-Degree-of-Freedom Pose through Temporal Phase-Shifted Low-Coherence Spatial Interferograms 16

Reconstructing Three-Degree-of-Freedom Pose through Temporal...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Shi, Liheng Zhang, Jinxu Gu, Yingying Gai, Fangqin Liu, Jing Wu, Guanhao Tsinghua University State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instrument Beijing100084 China Yangtze Delta Region Institute of Tsinghua University Zhejiang Research Center of Opto-electronic Sensing Zhejiang 314006 China Beijing Institute of Control Engineering Space Optoelectronic Measurement and Perception Lab. Beijing100190 China China Academy of Space Technology National Key Laboratory of Space Intelligent Control Beijing100094 China

We introduce a method for measuring three degrees of freedom using low-coherence light and temporal phase-shifting to discern position and orientation from the coherence envelope shape, enabling direct measurement wit... 详细信息

ISBN: (纸本)9798350372076

关键词： Degrees of freedom (mechanics)

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Root Pose Decomposition Towards Generic Non-rigid 3D Reconstruction with Monocular Videos

Root Pose Decomposition Towards Generic Non-rigid 3D Reconst...

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International Conference on Computer Vision (ICCV)

作者： Yikai Wang Yinpeng Dong Fuchun Sun Xiao Yang Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology (BNRist) State Key Lab on Intelligent Technology and Systems Tsinghua University RealAI

This work focuses on the 3D reconstruction of non-rigid objects based on monocular RGB video sequences. Concretely, we aim at building high-fidelity models for generic object categories and casually captured scenes. To this end, we do not assume known root poses of objects, and do not utilize category-specific templates or dense pose priors. The key idea of our method, Root Pose Decomposition (RPD), is to maintain a per-frame root pose transformation, meanwhile building a dense field with local transformations to rectify the root pose. The optimization of local transformations is performed by point registration to the canonical space. We also adapt RPD to multi-object scenarios with object occlusions and individual differences. As a result, RPD allows non-rigid 3D reconstruction for complicated scenarios containing objects with large deformations, complex motion patterns, occlusions, and scale diversities of different individuals. Such a pipeline potentially scales to diverse sets of objects in the wild. We experimentally show that RPD surpasses state-of-the-art methods on the challenging DAVIS, OVIS, and AMA datasets. We provide video results in https://***.

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SOUND ADVERSARIAL AUDIO-VISUAL NAVIGATION 10

SOUND ADVERSARIAL AUDIO-VISUAL NAVIGATION

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10th International Conference on Learning Representations, ICLR 2022

作者： Yu, Yinfeng Huang, Wenbing Sun, Fuchun Chen, Changan Wang, Yikai Liu, Xiaohong State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China Tsinghua University China College of Information Science and Engineering Xinjiang University China UT Austin United States JD Explore Academy ***

Audio-visual navigation task requires an agent to find a sound source in a realistic, unmapped 3D environment by utilizing egocentric audio-visual observations. Existing audio-visual navigation works assume a clean environment that solely contains the target sound, which, however, would not be suitable in most real-world applications due to the unexpected sound noise or intentional interference. In this work, we design an acoustically complex environment in which, besides the target sound, there exists a sound attacker playing a zero-sum game with the agent. More specifically, the attacker can move and change the volume and category of the sound to make the agent suffer from finding the sounding object, while the agent tries to dodge the attack and navigate to the goal under the intervention. Under certain constraints to the attacker, we can improve the robustness of the agent towards unexpected sound attacks in audio-visual navigation. For better convergence, we develop a joint training mechanism by employing the property of a centralized critic with decentralized actors. Experiments on two real-world 3D scan datasets (Replica and Matterport3D) verify the effectiveness and the robustness of the agent trained under our designed environment when transferred to the clean environment or the one containing sound attackers with random policy. Project: https://***/SAAVN. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Navigation

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Design of Variance-Constrained H∞state Estimation Algorithm for Delayed Memristive Neural Networks with Attacks: An Adaptive Event-Triggered Approach 8

Design of Variance-Constrained H∞State Estimation Algorithm...

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8th IEEE International Conference on Cloud Computing and Intelligence systems, CCIS 2022

作者： Gao, Yan Hu, Jun Yu, Hui Jia, Chaoqing Harbin University of Science and Technology Department of Mathematics Harbin150080 China Harbin University of Science and Technology School of Automation Harbin150080 China Harbin University of Science and Technology Heilongjiang Prov. Key Lab. of Optimization Control and Intelligent Analysis for Complex Systems Harbin150080 China

ISBN: (纸本)9781665477352

This paper studies the algorithm design of variance-constrained H∞ state estimation problem for delayed memristive neural networks with adaptive event-triggered mechanism. The denial-of-service attacks are modeled by a series of random variables obeying the Bernoulli distribution with known probability. In addition, the adaptive event-triggered mechanism is introduced into the sensor-to-estimator to avoid unnecessary resource consumption. Our purpose is to construct a finite-horizon state estimation algorithm, and sufficient condition is obtained for the estimation error system satisfying the H∞ performance requirement and the error variance boundedness. Finally, a numerical example is used to illustrate the feasibility of the presented H∞ state estimation algorithm. © 2022 IEEE.

关键词： state estimation

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CAT: a coarse-to-fine attention tree for semantic change detection

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Visual Intelligence 2023年第1期1卷 1-12页

作者： Wei, Xiu-Shen Xu, Yu-Yan Zhang, Chen-Lin Xia, Gui-Song Peng, Yu-Xin School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing China 4Paradigm Inc Beijing China School of Computer Science and Institute of Artificial Intelligence Wuhan University Wuhan China Wangxuan Institute of Computer Technology Peking University Beijing China

Semantic change detection (SCD) and land cover mapping (LCM) are always treated as a dual task in the field of remote sensing. However, due to diverse real-world scenarios, many SCD categories are not easy to be clearly recognized, such as “water-vegetation” and “water-tree”, which can be regarded as fine-grained differences. In addition, even a single LCM category is usually difficult to define. For instance, some “vegetation” categories with litter vegetation coverage are easily confused with the general “ground” category. SCD/LCM becomes challenging under both challenges of its fine-grained nature and lab.l ambiguity. In this paper, we tackle the SCD and LCM tasks simultaneously by proposing a coarse-to-fine attention tree (CAT) model. Specifically, it consists of an encoder, a decoder and a coarse-to-fine attention tree module. The encoder-decoder structure extracts the high-level features from input multi-temporal images first and then reconstructs them to return SCD and LCM predictions. Our coarse-to-fine attention tree, on the one hand, utilizes the tree structure to better model a hierarchy of categories by predicting the coarse-grained lab.ls first and then predicting the fine-grained lab.ls later. On the other hand, it applies the attention mechanism to capture discriminative pixel regions. Furthermore, to address lab.l ambiguity in SCD/LCM, we also equip a lab.l distribution learning loss upon our model. Experiments on the large-scale SECOND dataset justify that the proposed CAT model outperforms state-of-the-art models. Moreover, various ablation studies have demonstrated the effectiveness of tailored designs in the CAT model for solving semantic change detection problems. © The Author(s) 2023.

关键词： Attention tree Coarse-to-fine Fine-grained recognition lab.l ambiguity Semantic change detection

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Meta-device:advanced manufacturing

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Light(Advanced Manufacturing) 2024年第1期5卷 115-130页

作者： Borui Leng Yao Zhang Din Ping Tsai Shumin Xiao Department of Electrical Engineering City University of Hong KongHong Kong999077China Centre for Biosystems Neuroscience and NanotechnologyCity University of Hong KongHong Kong999077China The State Key Laboratory of Terahertz and Millimeter Waves City University of Hong KongHong Kong999077China Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic SystemsHarbin Institute of TechnologyShenzhen 518055China Pengcheng Laboratory Shenzhen 518055China

Metasurfaces are one of the most promising devices to break through the limitations of bulky optical *** offering a new method of light manipulation based on the light-matter interaction in subwavelength nanostructures,metasurfaces enable the efficient manipulation of the amplitude,phase,polarization,and frequency of light and derive a series of possibilities for important ***,one key challenge for the realization of applications for meta-devices is how to fabricate large-scale,uniform nanostructures with high *** this review,we review the state-of-the-art nanofabrication techniques compatible with the manufacture of *** lithography,masked lithography,and other nanofabrication techniques are highlighted in *** also delve into the constraints and limitations of the current fabrication methods while providing some insights on solutions to overcome these challenges for advanced nanophotonic applications.

关键词： Meta-device Metasurface Nanofabrication COMS-compatible fabrication

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Scalab.e Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning

arXiv

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arXiv 2024年

作者： Zhou, Xuan Shi, Xiang Zhang, Lele Chen, Chen Li, Hongbo Ma, Lin Deng, Fang Chen, Jie The State Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing100081 China The Department of Automation Tsinghua University Beijing10084 China The Beijing Geek+ Technology Co. Ltd Beijing100000 China Zhejiang Cainiao Supply Chain Management Company Ltd. Hangzhou311101 China The State Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing100081 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China

To improve the efficiency of warehousing system and meet huge customer orders, we aim to solve the challenges of dimension disaster and dynamic properties in hyper scale multirobot task planning (MRTP) for robotic mobile fulfillment system (RMFS). Existing research indicates that hierarchical reinforcement learning (HRL) is an effective method to reduce these challenges. Based on that, we construct an efficient multi-stage HRL-based multi-robot task planner for hyper scale MRTP in RMFS, and the planning process is represented with a special temporal graph topology. Following its temporal logic, only critical events deserve attention, so system can sample efficiently when training and hold dynamic response in execution. To ensure optimality, the planner is designed with a centralized architecture, but it also brings the challenges of scaling up and generalization that require policies to maintain performance for various unlearned scales and maps. To tackle these difficulties, we first construct a hierarchical temporal attention network (HTAN) to ensure basic ability of handling inputs with unfixed lengths, and then design multi-stage curricula for hierarchical policy learning to further improve the scaling up and generalization ability while avoiding catastrophic forgetting. Additionally, we notice that policies with hierarchical structure suffer from unfair credit assignment that is similar to that in multi-agent reinforcement learning, inspired of which, we propose a hierarchical reinforcement learning algorithm with counterfactual rollout baseline to improve learning performance. Experimental results demonstrate that our planner outperform other state-of-the-art methods on various MRTP instances in both simulated and real-world RMFS. Also, our planner can successfully scale up to hyper scale MRTP instances in RMFS with up to 200 robots and 1000 retrieval racks on unlearned maps while keeping superior performance over other methods. Copyright © 2024, The Authors. A

关键词： Multipurpose robots

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Negative Conductivity Induced Reconfigurable Gain Metasurfaces and Their Nonlinearity

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Physical Review Letters 2024年第11期133卷 113801页

作者： Xiaoyue Zhu Chao Qian Erping Li Hongsheng Chen ZJU-UIUC Institute Interdisciplinary Center for Quantum Information State Key Laboratory of Extreme Photonics and Instrumentation ZJU-Hangzhou Global Science and Technology Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang Jinhua Institute of Zhejiang University

The past decades have witnessed the rapid development of metamaterials and metasurfaces. However, loss is still a challenging problem limiting numerous practical applications, including long-range wireless communications, superscattering, and non-Hermitian physics. Recently, great effort has been made to minimize the loss, however, they are too complicated for practical implementation and still restricted by the theoretical limit. Here, we propose and experimentally realize a tunable gain metasurface induced by negative conductivity, with deep theoretical analysis from scattering theory and equivalent circuits. In the experiment, we create metasurface samples embedded with tunable negative (or positive) conductivity to achieve adjustable gain (or loss). By varying the control bias voltages, the metasurfaces can reflect incident waves with additional controllab.e gain. Interestingly, we find the gain metasurfaces inherently pose nonlinearities, which are beneficial for nonlinear optics and microwave applications, particularly for the nonlinear activation of wave-based neural networks.

关键词： Electromagnetic waves & oscillations Gain properties of lasers Metamaterials Metasurfaces Nonlinear optics

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