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作者： Hu, Mengyun Nan, Junyi Yuan, Shuai Zeng, Heping Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal University Chongqing401120 China State Key Laboratory of Precision Spectroscopy East China Normal University Shanghai200062 China Shanghai Research Center for Quantum Sciences Shanghai201315 China Jinan Institute of Quantum Technology Jinan China

Stable propagation of multifilament arrays in transparent bulk media with adjustable separation distances between adjacent child filaments has always been desired for advanced manufacturing. Here, we report on the generation of an ionization-induced volume plasma grating (VPG) by the interaction of two batches of noncollinearly propagating arrays of multiple filaments (AMF). The VPG can externally arrange the propagation of the pulses along regular plasma waveguides via spatial reconstruction of electrical fields, which is compared with the self-formation of randomly distributed multiple filamentation originated from noises. The separation distances of filaments in VPG are controllable by readily changing the crossing angle of the excitation beams. In addition, an innovative method to efficiently fabricate multidimensional grating structures in transparent bulk media through laser modification using VPG was demonstrated. © 2023 Optica Publishing Group.

关键词： Band structure

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An Optimal Control Based Trajectory Planning Method for Cooperative Merging Maneuvers

An Optimal Control Based Trajectory Planning Method for Coop...

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Intelligent Robotics and systems (ISoIRS), International Symposium on

作者： Qi Wang Hao Ge Xuting Duan Jianshan Zhou State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control State Key Laboratory of Air Traffic Management System and Technology School of Transportation Science and Engineering Beihang University Beijing China State Key Laboratory of Air Traffic Management System and Technology Nanjing Research Institute of Electronic Engineering Nanjing China State Key Lab of Intelligent Transportation System Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China

Due to the prediction errors and diverse driving styles of surrounding vehicles, traffic congestion and even accidents can occur in the traffic bottleneck area where vehicles merge into the main road. To ensure that connected and automated vehicles (CAVs) can quickly and efficiently merge into the main road, this paper proposes a cooperative trajectory planning method based on optimal control for vehicle cut-in scenarios. A optimal control model is constructed, and a quintic polynomial is used to generate the initial solution of the optimal control problem to accelerate the solution process and improve the success rate. Finally, The obtained merging trajectory is then synchronized with the movement of vehicles on the main road to optimize their actions and ensure a safe merging process. Joint simulation with Robot Operating system (ROS) and RVIZ is utilized, and the results show that the proposed algorithm can quickly obtain cooperative results, the locally successful merging trajectory meets the requirements of smoothness and safety, and if the solution does not allow merging, the merging vehicle is instructed to terminate the execution.

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Aligning Recommendation and Conversation via Dual Imitation

Aligning Recommendation and Conversation via Dual Imitation

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Zhou, Jinfeng Wang, Bo Huang, Minlie Zhao, Dongming Huang, Kun He, Ruifang Hou, Yuexian College of Intelligence and Computing Tianjin University Tianjin China State Key Laboratory of Communication Content Cognition People's Daily Online Beijing China The CoAI Group DCST Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China AI Lab China Mobile Communication Group Tianjin Co. Ltd. China

Human conversations of recommendation naturally involve the shift of interests which can align the recommendation actions and conversation process to make accurate recommendations with rich explanations. However, existing conversational recommendation systems (CRS) ignore the advantage of user interest shift in connecting recommendation and conversation, which leads to an ineffective loose coupling structure of CRS. To address this issue, by modeling the recommendation actions as recommendation paths in a knowledge graph (KG), we propose DICR (Dual Imitation for Conversational Recommendation), which designs a dual imitation to explicitly align the recommendation paths and user interest shift paths in a recommendation module and a conversation module, respectively. By exchanging alignment signals, DICR achieves bidirectional promotion between recommendation and conversation modules and generates high-quality responses with accurate recommendations and coherent explanations. Experiments demonstrate that DICR outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics. © 2022 Association for Computational Linguistics.

关键词： Knowledge graph

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Optimization of a Piezoelectric Bending Beam-based Human Knee Energy Harvester

Optimization of a Piezoelectric Bending Beam-based Human Kne...

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IEEE International Conference on Mechatronics and Automation

作者： Haisu Liao Yidi Wang Qiuyu Shi Ruisen Huang Fei Gao Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy System

The concept of extracting energy from the human body to recharge depleted energy sources has gained significant attention. Recently, scientists have focused on harnessing biomechanical energy from the human body while walking. The aim of this study is to optimize a piezoelectric bending beam-based human knee energy harvester to enhance performance. Specifically, the shape of the bending beam (the substrate: a carbon fiber plate) is optimized to modulate the deformation, such that the average strain of the piezoelectric macro fiber composites (MFC) attached to the bending beam is improved to generate more electricity. In addition, the effects of the substrate’s thickness and the hinge base’s height for the bending beam on the output power are also analyzed. Experimental testing on a subject is conducted to validate our design and test the performance. The harvester generates an average power output of 24.55 mW when walking at 4 km/h on a treadmill. The harvester’s total weight is 278g.

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Coordinated frequency modulation strategy considering source–load uncertainty

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Frontiers in Energy Research 2024年 12卷

作者： Dong, Xiaohong Wang, Shuo Ai, Dengxin Ma, Yang Yu, Xiaodan State Key Lab of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China Tianjin Electric Power Science and Research Institute Tianjin China Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin China

Introduction: Wind turbine generators (WTGs) and electric vehicles (EVs) are used as source-side and load-side resources, respectively. And the uncertainties of WTGs output and EVs charging load seriously affect the frequency stability of the power system. For the wind speed prediction error and the uncertainty of EV off-grid time, a cooperative frequency modulation (FM) strategy is proposed. Methods: Firstly, the wind speed interval is divided based on the operating characteristics of WTGs and the load reduction rate. On the basis, a load reduction operation strategy based on rotor speed control and pitch Angle control is proposed to enable WTGs to have bidirectional FM capability. The adjustable capacity of WTGs is determined based on the wind speed prediction error and the operation strategy of load reduction;Secondly, based on the controllable domain model of an EV considering the off-grid time uncertainty, the adjustable capacity of EV clusters is determined by state grouping of the state of charge (SOC) according to its charging urgency. By defining EV FM capability parameters and charging urgency parameters, the EV priority list for FM is determined and the power allocation strategy is proposed;Then, based on the urgency of EV charging and the economy of WTGs load reduction operation, a cooperative FM task assignment strategy is proposed. Results: Simulations demonstrate the strategy enhances FM capability and improves FM effect by 6.05% compared to fixed-proportion task allocation. It strengthens frequency stability by leveraging complementary strengths. Discussion: Consideration of wind speed prediction errors can improve WTG adjustable capacity estimation, boosting FM accuracy;Coordinated task allocation minimizes WTGs intermittent FM output impacts, ensuring stable grid frequency. This dual-source-load approach offers a robust solution for modern power systems with high renewable penetration. Copyright © 2025 Dong, Wang, Ai, Ma and Yu.

关键词： Frequency stability

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TCP: Textual-Based Class-Aware Prompt Tuning for Visual-Language Model

TCP: Textual-Based Class-Aware Prompt Tuning for Visual-Lang...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hantao Yao Rui Zhang Changsheng Xu State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Prompt tuning represents a valuable technique for adapting pre-trained visual-language models (VLM) to various downstream tasks. Recent advancements in CoOp-based methods propose a set of learnable domain-shared or image-conditional textual tokens to facilitate the generation of task-specific textual classifiers. However, those textual tokens have a limited generalization ability regarding unseen domains, as they cannot dynamically adjust to the distribution of testing classes. To tackle this issue, we present a novel Textual-based Class-aware Prompt tuning(TCP) that explicitly incorporates prior knowledge about classes to en-hance their discriminability, The critical concept of TCP in-volves leveraging Textual Knowledge Embedding (TKE) to map the high generalizability of class-level textual knowledge into class-aware textual tokens. By seamlessly inte-grating these class-aware prompts into the Text Encoder, a dynamic class-aware classifier is generated to enhance dis-criminability for unseen domains. During inference, TKE dynamically generates class-aware prompts related to the unseen classes. Comprehensive evaluations demonstrate that TKE serves as a plug-and-play module effortlessly combinable with existing methods. Furthermore, TCP con-sistently achieves superior performance while demanding less training time 1 1 https://***/htyao89/Textual-based_Class-aware_prompt_tuning.

关键词： Training Computer vision Adaptation models Benchmark testing Pattern recognition Tuning

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Stochastic Optimal Configuration of Shared Energy Storage and Wind Farms for Primary Frequency Support Considering Wind Speed Correlations 4

Stochastic Optimal Configuration of Shared Energy Storage an...

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4th Energy Conversion and Economics Annual Forum, ECE Forum 2024

作者： Liu, Jiajie Jia, Yanbing Shi, Junyi Zhang, Guoqiang Li, Changlin Jia, Ying Key Laboratory of Cleaner Intelligent Control on Coal & Electricity Ministry of Education澿 Taiyuan China Shanxi Key Lab. of Power System Operation and Control Taiyuan University of Technology Taiyuan China State Grid Sichuan Electric Power Company Ziyang Power Supply Company Ziyang China

ISBN: (纸本)9781837242627

To address the issue of frequency stability caused by the integration of renewable energy into the grid, the shared energy storage (SES) is coordinated with multiple wind farms (WFs). The scientific PFR reserve capacity configuration of them is essential to balance reliability and cost. Nevertheless, due to the desynchronization of wind resources between WFs and on-site wind turbines (WTs), there are differences in the reserve capacity and allocated SES capacity of different WFs. The traditional unified capacity configuration method easily increases costs. Therefore, this paper proposes a stochastic optimal configuration method considering wind speed (WS) correlations. Firstly, the high-dimensional vine Copula is used and the effects of wake, terrain, and time-delay are considered to portray the WS correlation of multiple WFs and multiple WTs, respectively. Subsequently, the PFR reserve capacity optimal strategy is established to minimize the total cost of the WFs and the SES. In particular, the p-efficient point algorithm based on the Copula function is utilized to solve the chance constraint. The simulation results show that the proposed method reasonably configures each WT's deloading capacity and the SES capacity with consideration of WS correlations and uncertainties, meeting the PFR demand and reducing the reserve cost. © The Institution of Engineering & technology 2024.

关键词： Windmill

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FEDGT: FEDERATED NODE CLASSIFICATION WITH SCAlabLE GRAPH TRANSFORMER

arXiv

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arXiv 2024年

作者： Zhang, Zaixi Hu, Qingyong Yu, Yang Gao, Weibo Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Hong Kong University of Science and Technology Hong Kong

Graphs are widely used to model relational data. As graphs are getting larger and larger in real-world scenarios, there is a trend to store and compute subgraphs in multiple local systems. For example, recently proposed subgraph federated learning methods train Graph Neural Networks (GNNs) distributively on local subgraphs and aggregate GNN parameters with a central server. However, existing methods have the following limitations: (1) The links between local subgraphs are missing in subgraph federated learning. This could severely damage the performance of GNNs that follow message-passing paradigms to update node/edge features. (2) Most existing methods overlook the subgraph heterogeneity issue, brought by subgraphs being from different parts of the whole graph. To address the aforementioned challenges, we propose a scalable Federated Graph Transformer (FedGT) in the paper. Firstly, we design a hybrid attention scheme to reduce the complexity of the Graph Transformer to linear while ensuring a global receptive field with theoretical bounds. Specifically, each node attends to the sampled local neighbors and a set of curated global nodes to learn both local and global information and be robust to missing links. The global nodes are dynamically updated during training with an online clustering algorithm to capture the data distribution of the corresponding local subgraph. Secondly, FedGT computes clients' similarity based on the aligned global nodes with optimal transport. The similarity is then used to perform weighted averaging for personalized aggregation, which well addresses the data heterogeneity problem. Finally, extensive experimental results on 6 datasets and 2 subgraph settings demonstrate the superiority of FedGT. © 2024, CC BY.

关键词： Graph neural networks

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Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer

arXiv

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arXiv 2024年

作者： Zhu, Yongxin Su, Dan He, Liqiang Xu, Linli Yu, Dong School of Data Science University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Tencent AI Lab China

While recent advancements in speech language models have achieved significant progress, they face remarkable challenges in modeling the long acoustic sequences of neural audio codecs. In this paper, we introduce Generative Pre-trained Speech Transformer (GPST), a hierarchical transformer designed for efficient speech language modeling. GPST quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a hierarchical transformer architecture, allowing for a unified one-stage generation process and enhancing Hi-Res audio generation capabilities. By training on large corpora of speeches in an end-to-end unsupervised manner, GPST can generate syntactically consistent speech with diverse speaker identities. Given a brief 3-second prompt, GPST can produce natural and coherent personalized speech, demonstrating in-context learning abilities. Moreover, our approach can be easily extended to spoken cross-lingual speech generation by incorporating multi-lingual semantic tokens and universal acoustic tokens. Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity. The code is available at https://***/youngsheen/GPST. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Non-volatile optical switch based on Sb2Se3-loaded three-mode interference

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Optics letters 2025年第12期50卷 4022-4025页

作者： Xinxin Zhao Xiaolong Liu Yujiao Tao Lei Yang Kai Xu Hongtao Lin Peipeng Xu Wei Zhang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 315211 China Key Laboratory of Photoelectric Detecting Materials and Devices of Zhejiang Province Ningbo 315211 China The State Key Lab of Brain-Machine Intelligence Key Laboratory of Micro-Nano Electronics and Smart System of Zhejiang Province College of Information Science and Electronic Engineering Zhejiang University Hangzhou 310027 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai 200050 China

In this Letter, we propose a non-volatile switch based on multimode interference in an SbSe-loaded waveguide. By implementing low-loss phase control within a multimode interference coupler, this design effectively manages the self-imaging phase shift length during a single phase change cycle. The fabricated multiplexed optical switch exhibits impressive performance characteristics, including a low insertion loss of ∼ 1.5 dB, a compact length of ∼35 μm, and minimal cross talk below -6 dB over a 35 nm bandwidth for both the amorphous and crystalline states. The ability to reconfigure light routing via SbSe phase transitions opens new avenues for optical field-programmable gate arrays and high-density photonic processors.

关键词： Crosstalk Effective refractive index Field programmable gate arrays Multimode interference Optical neural systems Phase shift

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