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arXiv 2023年

作者： Peng, Xiongfeng Liu, Zhihua Li, Weiming Tan, Ping Cho, SoonYong Wang, Qiang SAIT-China Lab Samsung R&D Institute China-Beijing China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology China Multimedia System TU Samsung Advanced Institute of Technology Korea Republic of

Recent deep learning based visual simultaneous localization and mapping (SLAM) methods have made significant progress. However, how to make full use of visual information as well as better integrate with inertial measurement unit (IMU) in visual SLAM has potential research value. This paper proposes a novel deep SLAM network with dual visual factors. The basic idea is to integrate both photometric factor and re-projection factor into the end-to-end differentiable structure through multi-factor data association module. We show that the proposed network dynamically learns and adjusts the confidence maps of both visual factors and it can be further extended to include the IMU factors as well. Extensive experiments validate that our proposed method significantly outperforms the state-of-the-art methods on several public datasets, including TartanAir, EuRoC and ETH3D-SLAM. Specifically, when dynamically fusing the three factors together, the absolute trajectory error for both monocular and stereo configurations on EuRoC dataset has reduced by 45.3% and 36.2% respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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CauSSL: Causality-inspired Semi-supervised Learning for Medical Image Segmentation

CauSSL: Causality-inspired Semi-supervised Learning for Medi...

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International Conference on computer Vision (ICCV)

作者： Juzheng Miao Cheng Chen Furui Liu Hao Wei Pheng-Ann Heng Department of Computer Science and Engineering The Chinese University of Hong Kong Center for Advanced Medical Computing and Analysis Harvard Medical School and Massachusetts General Hospital Zhejiang Lab Department of Biomedical Engineering The Chinese University of Hong Kong Institute of Medical Intelligence and XR The Chinese University of Hong Kong

Semi-supervised learning (SSL) has recently demonstrated great success in medical image segmentation, significantly enhancing data efficiency with limited annotations. However, despite its empirical benefits, there are still concerns in the literature about the theoretical foundation and explanation of semi-supervised segmentation. To explore this problem, this study first proposes a novel causal diagram to provide a theoretical foundation for the mainstream semi-supervised segmentation methods. Our causal diagram takes two additional intermediate variables into account, which are neglected in previous work. Drawing from this proposed causal diagram, we then introduce a causality-inspired SSL approach on top of co-training frameworks called CauSSL, to improve SSL for medical image segmentation. Specifically, we first point out the importance of algorithmic independence between two networks or branches in SSL, which is often overlooked in the literature. We then propose a novel statistical quantification of the uncomputable algorithmic independence and further enhance the independence via a min-max optimization process. Our method can be flexibly incorporated into different existing SSL methods to improve their performance. Our method has been evaluated on three challenging medical image segmentation tasks using both 2D and 3D network architectures and has shown consistent improvements over state-of-the-art methods. Our code is publicly available at: https://***/JuzhengMiao/CauSSL.

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Block-Wise Index Modulation and Receiver Design for High-Mobility OTFS Communications

arXiv

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arXiv 2023年

作者： Qian, Mi Ji, Fei Ge, Yao Wen, Miaowen Cheng, Xiang Poor, H. Vincent The School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China The Continental-NTU Corporate Lab Nanyang Technological University Singapore The State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics School of Electronics Engineering and Computer Science Peking University Beijing100871 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proved to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is the development of effective IM schemes and efficient receivers for practical OTFS systems that must operate in the presence of channel delays and Doppler shifts. In this paper, we propose two novel block-wise IM schemes for OTFS systems, named delay-IM with OTFS (DeIM-OTFS) and Doppler-IM with OTFS (DoIM-OTFS), where a block of delay/Doppler resource bins are activated simultaneously. Based on a maximum likelihood (ML) detector, we analyze upper bounds on the average bit error rates for the proposed DeIM-OTFS and DoIM-OTFS schemes, and verify their performance advantages over the existing IM-OTFS systems. We also develop a multi-layer joint symbol and activation pattern detection (MLJSAPD) algorithm and a customized message passing detection (CMPD) algorithm for our proposed DeIM-OTFS and DoIM-OTFS systems with low complexity. Simulation results demonstrate that our proposed MLJSAPD and CMPD algorithms can achieve desired performance with robustness to the imperfect channel state information (CSI). © 2023, CC BY.

关键词： Message passing

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Dynamic Modeling and Identification of a Robotic Intracardiac Echo Catheter

Dynamic Modeling and Identification of a Robotic Intracardia...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Mohammad Salehizadeh Filipe Pedrosa Harmanpreet Bassan Rajni Patel Jayender Jagadeesan Department of Radiology at Brigham and Women's Hospital Harvard Medical School Boston MA USA Department of Electrical and Computer Engineering Canadian Surgical Technologies & Advanced Robotics (CSTAR) Western University London ON Canada Electronics Engineering Division - Accelerator Directorate SLAC National Accelerator Lab CA

Catheter-based cardiac ablation is the preferred method of treating atrial fibrillation. Conventionally, the catheter is navigated in the heart using X-ray fluoroscopy imaging and an electroanatomical map. Although successful, these imaging modalities do not provide real-time feedback on the quality of lesions created, which in turn could lead to recurrence of arrhythmia. Intracardiac echo (ICE) catheter provides real-time imaging within the heart to visualize both the ablation catheter and lesions created. However, manipulating the ablation and ICE catheters simultaneously is tedious and time consuming. As a first step towards developing a robotic ICE catheter that can autonomously follow the ablation catheter and monitor the lesions, we have developed a dynamic model for the ICE catheter. The model is based on the Cosserat theory for flexible rods that relies on strain parametrization. The model also accounts for frictional forces between the catheter sheath and tendons, external loads and fluid forces acting on the catheter. A good nominal model for describing the catheter dynamics is essential to develop a robust control scheme for the robotic ICE catheter. The parameters of the ICE catheter are estimated using weight release, tendon-driven actuation and fluid flow experiments. To the best of our knowledge, this is the first dynamic model for the ICE catheter that accurately reflects the dynamics of the catheter under pulsatile fluid flow within a heart phantom.

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BiKT: Unleashing the potential of GNNs via Bi-directional Knowledge Transfer

arXiv

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arXiv 2023年

作者： Zheng, Shuai Liu, Zhizhe Zhu, Zhenfeng Zhang, Xingxing Li, Jianxin Zhao, Yao The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Qiyuan Lab Beijing China The Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing100083 China

Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. However, less focus has been put on feature transformation, another major operation of the message-passing framework. In this paper, we first empirically investigate the performance of the feature transformation operation in several typical GNNs. Unexpectedly, we notice that GNNs do not completely free up the power of the inherent feature transformation operation. By this observation, we propose the Bi-directional Knowledge Transfer (BiKT), a plug-and-play approach to unleash the potential of the feature transformation operations without modifying the original architecture. Taking the feature transformation operation as a derived representation learning model that shares parameters with the original GNN, the direct prediction by this model provides a topological-agnostic knowledge feedback that can further instruct the learning of GNN and the feature transformations therein. On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other. In addition, a theoretical analysis is further provided to demonstrate that BiKT improves the generalization bound of the GNNs from the perspective of domain adaption. An extensive group of experiments on up to 7 datasets with 5 typical GNNs demonstrates that BiKT brings up to 0.5% - 4% performance gain over the original GNN, which means a boosted GNN is obtained. Meanwhile, the derived model also shows a powerful performance to compete with or even surpass the original GNN, enabling us to flexibly apply it independently to some other specific downstream tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution

arXiv

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arXiv 2024年

作者： Jiang, Aiwen Wei, Zhi Peng, Long Liu, Feiqiang Li, Wenbo Wang, Mingwen School of Computer and Information Engineering Jiangxi Normal University Nanchang China School of Digital Industry Jiangxi Normal University Shangrao China Institute of Advanced Technology University of Science and Technology of China Hefei China Noah’s Ark Lab Huawei Technologies Ltd. Shenzhen China

Image super-resolution pursuits reconstructing high-fidelity high-resolution counterpart for low-resolution (LR) image. In recent years, diffusion-based models have garnered significant attention due to their capabilities with rich prior knowledge. The success of diffusion models based on general text prompts has validated the effectiveness of textual control in the field of text2image. However, given the severe degradation commonly presented in low-resolution images, coupled with the randomness characteristics of diffusion models, current models struggle to adequately discern semantic and degradation information within severely degraded images. This often leads to obstacles such as semantic loss, visual artifacts, and visual hallucinations, which pose substantial challenges for practical use. To address these challenges, this paper proposes to leverage degradation-aligned language prompt for accurate, fine-grained, and high-fidelity image restoration. Complementary priors including semantic content descriptions and degradation prompts are explored. Specifically, on one hand, image-restoration prompt alignment decoder is proposed to automatically discern the degradation degree of LR images, thereby generating beneficial degradation priors for image restoration. On the other hand, much richly tailored descriptions from pretrained multimodal large language model elicit high-level semantic priors closely aligned with human perception, ensuring fidelity control for image restoration. Comprehensive comparisons with state-of-the-art methods have been done on several popular synthetic and real-world benchmark datasets. The quantitative and qualitative analysis have demonstrated that the proposed method achieves a new state-of-the-art perceptual quality level, especially in real-world cases based on reference-free metrics. Related source codes and pre-trained parameters will be public in https://***/puppy210/DaLPSR. © 2024, CC BY.

关键词： Restoration

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NEXT:a neural network framework for next POI recommendation

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Frontiers of computer Science 2020年第2期14卷 314-333页

作者： Zhiqian ZHANG Chenliang LI Zhiyong WU Aixin SUN Dengpan YE Xiangyang LUO Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationSchool of Cyber Science and EngineeringWuhan UniversityWuhan 430072China Department of Computer Science The University of Hong KongPokfulam RoadHong Kong 999077China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore State Key Lab of Mathematical Engineering and Advanced Computing Zhengzhou 450001China

The task of next POI recommendations has been studied extensively in recent ***,developing a unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging,because of the heterogeneity nature of these ***,effective mechanisms to smoothly handle cold-start cases are also a difficult *** by the recent success of neural networks in many areas,in this paper,we propose a simple yet effective neural network framework,named NEXT,for next POI *** is a unified framework to learn the hidden intent regarding user's next move,by incorporating different factors in a unified ***,in NEXT,we incorporate meta-data information,e.g.,user friendship and textual descriptions of POIs,and two kinds of temporal contexts(i.e.,time interval and visit time).To leverage sequential relations and geographical influence,we propose to adopt DeepWalk,a network representation learning technique,to encode such *** evaluate the effectiveness of NEXT against other state-of-the-art alternatives and neural networks based *** results on three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI *** experiments show inherent ability of NEXT in handling cold-start.

关键词： POI neural networks POI recommendation

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A Compact Dead-Zone-Free Multi-Channel Stimulation and Recording System for Precision Peripheral Nerve Modulation 46

A Compact Dead-Zone-Free Multi-Channel Stimulation and Recor...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Xing, Yanjie Chen, Weihuang Yu, Danlei Gao, Xiang Bao, Jieting Xu, Kedi Zhejiang China Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal Key Laboratory of Biomedical Engineering of Education Ministry Department of Biomedical Engineering Zhejiang University Hangzhou China Institute of Advanced Digital Technology and Instrument Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory for Network Multimedia Technologies Zhejiang University Hangzhou310027 China Binjiang Institude of Zhejiang University Hangzhou310027 China Nanhu Brain-computer interface institute Hangzhou311100 China Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China

ISBN: (纸本)9798350371499

Peripheral nerve modulation has a certain potential in motor recovery, but the majority of systems suffer from the drawback of dead time caused by switching channels to achieve multi-channel stimulation. As a response to this challenge, we have developed a peripheral nerve modulation device with 64-channel recording and 16-channel stimulation without dead time. Through modulation of the rat sciatic nerve, we identified the crucial role of initial phase and amplitude in the outcomes of dual-channel combined stimulation. This design not only enhanced neural activation capabilities with constant power supply but also achieves more precise neural activation and reduces unintentional activation. © 2024 IEEE.

关键词： Recording instruments

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A mathematical model for efficient extraction of key locations from point-cloud data in track area

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Industrial Artificial Intelligence 2023年第1期1卷 1-14页

作者： Chen, Shuyue Wu, Jiaolv Lu, Jian Wang, Xizhao College of Mathematics and Statistics Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institue of Information Technology Shenzhen China Shenzhen No. 3 Vocational School of Technology Shenzhen China College of Engineering Huaqiao University Quanzhou China Shenzhen Key Lab. of Advanced Machine Learning and Applications Shenzhen University Shenzhen China Guangdong Key Lab. of Intelligent Information Process Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

During the construction of a metro system, it is inevitable that deviations will occur between the excavated tunnel and the original designed scheme. As such, it is necessary to adjust the designed scheme to accommodate these discrepancies. Specifically, the adjustment of the designed scheme involves a rigorous process of repeatedly selecting and verifying the feasibility of the proposed modifications using point-cloud data obtained from the tunnel. However, this process can be considerably time-consuming due to the large-scale and potentially redundant nature of the point-cloud data. This paper proposes a mathematical model for point-cloud data acquired in measuring a mined tunnel, which may deviate from the originally designed one. The modeling, which mainly includes determining its normal plane, and building the equation of tunnel point-cloud data, is to quickly extract several key locations in the tunnel surface for modifying the original design in order to achieve a minimum error between the modified design and the mined tunnel. In comparison with the conventional processing of extracting several key locations directly from point-cloud data, our model shows a significant promotion of extraction efficiency under an acceptable error bound. The model is tested in a real tunnel point-cloud data and the testing results confirm the increase of fitting accuracy and the decrease of computational load.

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Model-Free Predictive Combined Control for Three-Phase Grid-Connected Voltage Source Converters

Model-Free Predictive Combined Control for Three-Phase Grid-...

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Power Electronic and Drive Systems and Technologies Conference (PEDSTC)

作者： Alireza Jabbarnejad Sadegh Vaez-Zadeh Advanced Motion Systems Research Lab School of electrical and computer engineering Collage of engineering University of Tehran Tehran Iran Advanced Motion Systems Research Lab and The Center of Excellence on Applied Electromagnetic Systems School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

This paper proposes a model-free predictive combined control for three-phase grid-connected voltage source converters. It controls current components in the grid virtual flux reference frame according to the combined control method, along with the current prediction based on the predictive control method. The method has an inherent rapid dynamic response and high power quality with low sampling frequency. It avoids using any system parameters in the control structure, leading to a proper performance even with no information of system parameters. The method is simple and has a low computation burden, and lacks any pulse-width modulation (PWM) sub-system, additional sensor, and lookup table. The method is evaluated through simulation studies, where the results represent the effectiveness of the proposed method and its superiority over conventional method under parameter mismatch conditions.

关键词： Transient response Computational modeling Power quality Predictive models Pulse width modulation Observers Robustness

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