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arXiv 2023年

作者： Patterson, Zach J. Xiao, Wei Sologuren, Emily Rus, Daniela Computer Science and Artificial Intelligence Laboratory MIT United States

Incorporating both flexible and rigid components in robot designs offers a unique solution to the limitations of traditional rigid robotics by enabling both compliance and strength. This paper explores the challenges and solutions for controlling soft-rigid hybrid robots, particularly addressing the issue of self-contact. Conventional control methods prioritize precise state tracking, inadvertently increasing the system's overall stiffness, which is not always desirable in interactions with the environment or within the robot itself. To address this, we investigate the application of Control Barrier Functions (CBFs) and High Order CBFs to manage self-contact scenarios in serially connected soft-rigid hybrid robots. Through an analysis based on Piecewise Constant Curvature (PCC) kinematics, we establish CBFs within a classical control framework for self-contact dynamics. Our methodology is rigorously evaluated in both simulation environments and physical hardware systems. The findings demonstrate that our proposed control strategy effectively regulates self-contact in soft-rigid hybrid robotic systems, marking a significant advancement in the field of robotics. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine design

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Lightweight Ethylene-Vinyl Acetate Copolymer/Low-density Polyethylene/Carbon Nanotube Foams via Supercritical Carbon Dioxide Foaming for Piezoresistive Sensors

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Chinese Journal of Polymer science 2025年

作者： Hao-Hao Hou Xin He Hui Ma Hong-Fu Zhou Bian-Ying Wen Xiang-Dong Wang Ya-Feng Deng Key Laboratory of Processing and Application of Polymeric Foams of China National Light Industry Council School of Light Industry Science and EngineeringBeijing Technology and Business University School of Computer and Artificial Intelligence Beijing Technology and Business University

Flexible polymer-based foam sensors have significant potential for application in wearable electronics and motion monitoring. However, these prospects are hindered by the complex and unenvironmentally friendly manufacturing processes. In this study, we employed melt blending and supercritical carbon dioxide foaming to fabricate an ethylene-vinyl acetate copolymer（EVA）/low-density polyethylene（LDPE）/carbon nanotube（CNT） piezoresistive foam sensor. The cross-linking agent bis（tert-butyldioxyisopropyl） benzene and the conductive filler CNT were incorporated into the EVA/LDPE composite, successfully achieving a chemically cross-linked and physically entangled composite structure that significantly enhanced the storage modulus and complex viscosity. Additionally, the compressive strength of EVA/LDPE/CNT foam with 10 parts per hundred rubber（phr） CNT reached 1.37 MPa at 50% compression, marking a 340% increase compared to the 0.31 MPa of the CNT-free sample. Furthermore, the EVA/LDPE/CNT composite foams, which incorporated 10 phr CNT, were prepared under specific foaming conditions, resulting in an ultra-low density of 0.11 g/cm3and a higher sensitivity, with a gauge factor of –2.3. The piezoresistive foam sensors developed in this work could accurately detect human motion, thereby expanding their applications in the field of piezoresistive foam sensors and providing an effective strategy for the advancement of high-performance piezoresistive foam sensors.

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A Novel Adaptive 360° Livestreaming with Graph Representation Learning based FoV Prediction

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IEEE Transactions on Emerging Topics in Computing 2024年第2期13卷 1-14页

作者： Chen, Xingyan Du, Huaming Wang, Mu Zhao, Yu Shu, Xiaoyang Xu, Changqiao Muntean, Gabriel-Miro Financial Intelligence and Financial Engineering Key Laboratory of Sichuan Province Institute of Digital Economy and Interdisciplinary Science Innovation School of Computer and Artificial Intelligence Southwestern University of Finance and Economics Chengdu P. R. China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing P. R. China School of Electronic Engineering Dublin City University Glasnevin Dublin 9 Ireland

The exceptionally high bandwidth requirements associated with the delivery of live 360° video content pose significant challenges in the current network context. An avenue for addressing this bandwidth challenge is to use the limited network resources for sending the user's Field-of-View (FoV) tiles at a high resolution, instead of transmitting all frame components at high quality. However, precisely forecasting the FoV for 360° live video content distribution remains a complex endeavor due to the lack of pre-knowledge on user viewing behaviors. In this paper, we present GL360, a novel 360° transmission framework, which employs Graph Representation Learning for FoV prediction. First, we analyze the interaction between users and tiles in panoramic videos utilizing a dynamic heterogeneous Relational Graph Convolutional Network (RGCN), which facilitates efficient user and tile embedding representation learning. Secondly, we propose an online dynamic heterogeneous graph learning (DHGL)-based algorithm to dynamically capture the time-varying features of the user's viewing behaviors with limited prior knowledge. Further, we design a FoV-aware content delivery algorithm that allows the edge servers to determine the video tiles' resolution for each accessed user. Experimental results based on real traces demonstrate how our solution outperforms four other solutions in terms of FoV prediction and network performance IEEE

关键词： Clustering algorithms

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Sparse Color Fourier Ptychographic Microscopy With Implicit Neural Representations

Sparse Color Fourier Ptychographic Microscopy With Implicit ...

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Computational Optical Sensing and Imaging, COSI 2024 - Part of Optica Imaging Congress

作者： Chan, Matthew A. Zhou, Haowen Feng, Brandon Y. Metzler, Christopher A. Department of Computer Science University of Maryland College ParkMD20742 United States Department of Electrical Engineering California Institute of Technology PasadenaCA91125 United States Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA02139 United States

We apply implicit neural representations—which naturally capture spectral regularity—to reconstruct color Fourier ptychographic microscopy images from spectrally-sparse measurements. We conduct experiments on real-world specimens and demonstrate reconstruction quality comparable with fully sampled methods. © 2024 The Author(s).

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What planning problems can a relational neural network solve? 23

What planning problems can a relational neural network solve...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jiayuan Mao Tomás Lozano-Pérez Joshua B. Tenenbaum Leslie Pack Kaelbling MIT Computer Science & Artificial Intelligence Laboratory MIT Computer Science & Artificial Intelligence Laboratory and MIT Department of Brain and Cognitive Sciences and Center for Brains Minds and Machines

Goal-conditioned policies are generally understood to be "feed-forward" circuits, in the form of neural networks that map from the current state and the goal specification to the next action to take. However, under what circumstances such a policy can be learned and how efficient the policy will be are not well understood. In this paper, we present a circuit complexity analysis for relational neural networks (such as graph neural networks and transformers) representing policies for planning problems, by drawing connections with serialized goal regression search (S-GRS). We show that there are three general classes of planning problems, in terms of the growth of circuit width and depth as a function of the number of objects and planning horizon, providing constructive proofs. We also illustrate the utility of this analysis for designing neural networks for policy learning.

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PCRTAM-Net:A Novel Pre-Activated Convolution Residual and Triple Attention Mechanism Network for Retinal Vessel Segmentation

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Journal of computer science & Technology 2023年第3期38卷 567-581页

作者：汪华登李紫正保罗黎兵兵潘细朋刘振丙蓝如师罗笑南 Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin 541004China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin 541004China Department of Pathology Ganzhou Municipal HospitalGanzhou 341000China

Retinal images play an essential role in the early diagnosis of ophthalmic *** segmentation of retinal vessels in color fundus images is challenging due to the morphological differences between the retinal vessels and the low-contrast *** the same time,automated models struggle to capture representative and discriminative retinal vascular *** fully utilize the structural information of the retinal blood vessels,we propose a novel deep learning network called Pre-Activated Convolution Residual and Triple Attention Mechanism Network(PCRTAM-Net).PCRTAM-Net uses the pre-activated dropout convolution residual method to improve the feature learning ability of the *** addition,the residual atrous convolution spatial pyramid is integrated into both ends of the network encoder to extract multiscale information and improve blood vessel information flow.A triple attention mechanism is proposed to extract the structural information between vessel contexts and to learn long-range feature *** evaluate the proposed PCRTAM-Net on four publicly available datasets,DRIVE,CHASE_DB1,STARE,and *** model achieves state-of-the-art performance of 97.10%,97.70%,97.68%,and 97.14%for ACC and 83.05%,82.26%,84.64%,and 81.16%for F1,respectively.

关键词： retinal image segmentation triple attention mechanism atrous convolution residual network

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Parallel Graph Learning with Temporal Stamp Encoding for Fraudulent Transactions Detections

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IEEE Transactions on Big Data 2024年

作者： Ma, Jiacheng Xiang, Sheng Li, Qiang Yuan, Liangyu Cheng, Dawei Jiang, Changjun University of Technology Sydney Australian Artificial Intelligence Institute Sydney Australia Tongji University Department of Computer Science and Technology Shanghai China Shanghai Artificial Intelligence Laboratory Shanghai China

Financial transaction systems have become the critical backbone of modern society, and the sharp increase in fraudulent transactions has become an unavoidable significant topic. Their presence poses a severe threat to financial markets, impacting the health of the economic and social welfare systems of various countries. However, most existing fraud detection methods are limited to detecting individual fraudulent entities within static transaction networks, which are neither suitable for continuously changing dynamic transaction networks nor capable of detecting the increasingly prevalent organized fraud crimes. This paper introduces a novel approach, Parallel Graph Learning with Temporal Stamp Encoding (PGLTSE). On the one hand, it designs a history information module to perform temporal dimension feature learning to adapt to the continuous changes in transaction information in Continuous-Time Dynamic Graphs (CTDG). On the other hand, it designs a gang-aware risk propagation algorithm to infer the risk of organized fraudulent activities in the global transaction relation graph. By simultaneously conducting parallel graph representation learning in both homogeneous global transaction relation graphs and heterogeneous local entity interaction graphs, it aggregates local interaction and global association information for end-to-end training. Extensive experiments on diverse real-world datasets substantiate the superior performance of PGLTSE over existing methods, demonstrating its practical efficacy in detecting complex and evolving fraudulent behaviors in financial networks. © 2015 IEEE.

关键词： Health risks

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On-chain is not enough:Ensuring pre-data on the chain credibility for blockchain-based source-tracing systems

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Digital Communications and Networks 2023年第5期9卷 1053-1060页

作者： Yilei Wang Zhaojie Wang Guoyu Yang Shan Ai Xiaoyu Xiang Chang Chen Minghao Zhao School of Computer Science Qufu Normal UniversityRizhao276826China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic TechnologyGuilin541004China Institute of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou510006China School of Software Tsinghua UniversityTsinghua UniversityBeijing100191China

The blockchain provides a reliable and scalable method for enabling source-tracing functionality in large-scale Internet of Things(IoT)*** blockchain-based source tracing applications are generally based on the hypothesis that the raw data collected by each IoT node are credible and consistent,which however may not always be the *** no mechanism ensures the reliability of the original data collected from the IoT devices,these data may be accidently screwed up or maliciously tampered with before they are uploaded *** address this issue,we propose the Multi-dimensional Certificates of Origin(MCO)method to filter out the potentially incredible data-till all the data uploaded to the chain are *** achieve this,we devise the Multidimensional Information Cross-Verification(MICV)and Multi-source Data Matching Calculation(MDMC)*** verifies whether a to-be-uploaded datum is consistent or credible,and MDMC determines which data should be discarded and which data should be kept to retain the most likely credible/untampered ones in the circumstance when data inconsistency ***-scale experiments show that our scheme ensures on the credibility of data and off the chain with an affordable overhead.

关键词： Blockchain Traceability Blockchain security

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Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning 41

Towards Efficient Deep Spiking Neural Networks Construction ...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Yaxin Xu, Qi Shen, Jiangrong Xu, Hongming Chen, Long Pan, Gang School of Computer Science and Technology Dalian University of Technology Dalian China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China College of Computer Science and Technology Zhejiang University Hangzhou China Centre for Systems Engineering and Innovation Imperial College London London United Kingdom

The emergence of deep and large-scale spiking neural networks (SNNs) exhibiting high performance across diverse complex datasets has led to a need for compressing network models due to the presence of a significant number of redundant structural units, aiming to more effectively leverage their low-power consumption and biological interpretability advantages. Currently, most model compression techniques for SNNs are based on unstructured pruning of individual connections, which requires specific hardware support. Hence, we propose a structured pruning approach based on the activity levels of convolutional kernels named Spiking Channel Activity-based (SCA) network pruning framework. Inspired by synaptic plasticity mechanisms, our method dynamically adjusts the network's structure by pruning and regenerating convolutional kernels during training, enhancing the model's adaptation to the current target task. While maintaining model performance, this approach refines the network architecture, ultimately reducing computational load and accelerating the inference process. This indicates that structured dynamic sparse learning methods can better facilitate the application of deep SNNs in low-power and high-efficiency scenarios. Copyright 2024 by the author(s)

关键词： Convolutional neural networks

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SHMT: self-supervised hierarchical makeup transfer via latent diffusion models 24

SHMT: self-supervised hierarchical makeup transfer via laten...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhaoyang Sun Shengwu Xiong Yaxiong Chen Fei Du Weihua Chen Fan Wang Yi Rong School of Computer Science and Artificial Intelligence Wuhan University of Technology and DAMO Academy Alibaba Group School of Computer Science and Artificial Intelligence Wuhan University of Technology and Sanya Science and Education Innovation Park Wuhan University of Technology and Shanghai AI Laboratory School of Computer Science and Artificial Intelligence Wuhan University of Technology DAMO Academy Alibaba Group and Hupan Laboratory School of Computer Science and Artificial Intelligence Wuhan University of Technology and Sanya Science and Education Innovation Park Wuhan University of Technology

ISBN: (纸本)9798331314385

This paper studies the challenging task of makeup transfer, which aims to apply diverse makeup styles precisely and naturally to a given facial image. Due to the absence of paired data, current methods typically synthesize sub-optimal pseudo ground truths to guide the model training, resulting in low makeup fidelity. Additionally, different makeup styles generally have varying effects on the person face, but existing methods struggle to deal with this diversity. To address these issues, we propose a novel Self-supervised Hierarchical Makeup Transfer (SHMT) method via latent diffusion models. Following a "decoupling-and-reconstruction" paradigm, SHMT works in a self-supervised manner, freeing itself from the misguidance of imprecise pseudo-paired data. Furthermore, to accommodate a variety of makeup styles, hierarchical texture details are decomposed via a Laplacian pyramid and selectively introduced to the content representation. Finally, we design a novel Iterative Dual Alignment (IDA) module that dynamically adjusts the injection condition of the diffusion model, allowing the alignment errors caused by the domain gap between content and makeup representations to be corrected. Extensive quantitative and qualitative analyses demonstrate the effectiveness of our method. Our code is available at https://***/Snowfallingplum/SHMT.

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