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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Narasinga Rao Miniskar Pruek Vanna-Iampikul Aaron Young Sung Kyu Lim Frank Liu Jieun Yoo Corrinne Mills Nhan Tran Farah Fahim Jeffrey S Vetter Computer Science and Mathematic Division Oak Ridge National Laboratory Oak Ridge USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Physics University of Illinois Chicago Chicago USA Scientific Computing Division Fermi National Accelerator Laboratory Chicago USA

Low latency inference has many applications in edge machine learning. In this paper, we present a run-time configurable convolutional neural network (CNN) inference ASIC design for low-latency edge machine learning. By implementing a 5-stage pipelined CNN inference model in a 3D ASIC technology, we demonstrate that the model distributed on two dies utilizing face-to-face (F2F) 3D integration achieves superior performance. Our experimental results show that the design based on 3D integration achieves 43% better energy-delay product when compared to the traditional 2D technology.

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Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning

arXiv

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arXiv 2023年

作者： Barwey, Shivam Shankar, Varun Viswanathan, Venkatasubramanian Maulik, Romit Argonne Leadership Computing Facility Argonne National Laboratory United States Department of Mechanical Engineering Carnegie Mellon University United States Mathematics & Computer Science Division Argonne National Laboratory United States

The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes. This is accomplished here with the development of a novel graph neural network (GNN) autoencoding architecture with demonstrations on complex fluid flow applications. To address the first goal of interpretability, the GNN autoencoder achieves reduction in the number nodes in the encoding stage through an adaptive graph reduction procedure. This reduction procedure essentially amounts to flowfield-conditioned node sampling and sensor identification, and produces interpretable latent graph representations tailored to the flowfield reconstruction task in the form of so-called masked fields. These masked fields allow the user to (a) visualize where in physical space a given latent graph is active, and (b) interpret the time-evolution of the latent graph connectivity in accordance with the time-evolution of unsteady flow features (e.g. recirculation zones, shear layers) in the domain. To address the goal of unstructured mesh compatibility, the autoencoding architecture utilizes a series of multi-scale message passing (MMP) layers, each of which models information exchange among node neighborhoods at various lengthscales. The MMP layer, which augments standard single-scale message passing with learnable coarsening operations, allows the decoder to more efficiently reconstruct the flowfield from the identified regions in the masked fields. Analysis of latent graphs produced by the autoencoder for various model settings are conducted using unstructured snapshot data sourced from large-eddy simulations in a backward-facing step (BFS) flow configuration with an OpenFOAM-based flow solver at high Reynolds numbers. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation

TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Se...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Junbao Zhou Jilin Mei Pengze Wu Liang Chen Fangzhou Zhao Xijun Zhao Yu Hu Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory (CIC)

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In autonomous driving, 3D LiDAR plays a crucial role in understanding the vehicle’s surroundings. However, the newly emerged, unannotated objects presents few-shot learning problem for semantic segmentation. This paper addresses the limitations of current few-shot semantic segmentation by exploiting the temporal continuity of LiDAR data. Employing a tracking model to generate pseudo-ground-truths from a sequence of LiDAR frames, our method significantly augments the dataset, enhancing the model’s ability to learn on novel classes. However, this approach introduces a data imbalance biased to novel data that presents a new challenge of catastrophic forgetting. To mitigate this, we incorporate LoRA, a technique that reduces the number of trainable parameters, thereby preserving the model’s performance on base classes while improving its adaptability to novel classes. This work represents a significant step forward in few-shot 3D LiDAR semantic segmentation for autonomous driving. Our code is available at https://***/BowmanChow/Track-no-forgetting.

关键词： Adaptation models Laser radar Three-dimensional displays Codes Semantic segmentation Few shot learning Autonomous vehicles Intelligent robots

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Dielectric Rod Antenna Array with Planar Folded Slot Antenna Excitation

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 664-673页

作者： Saffold, Gabriel L. Weller, Thomas M. Antenna Systems Division Sensors and Electromagnetic Applications Laboratory Georgia Tech Research Institute AtlantaGA30080 United States School of Electrical Engineering and Computer Science Oregon State University CorvallisOR97331 United States

A dielectric rod antenna array fed by slot antenna radiators without the use of metallic waveguide is presented here. Communication systems, especially those related to 5G, are moving up into mm-wave bands where metal losses can become significant and many traditional fabrication and manufacturing techniques become more difficult. Dielectric rod antennas (DRA) are entirely made of dielectric and may be injection-molded or 3D-printed as solid rods, layered rods, or tubes. While the normal approach to feeding DRAs involves some version of metallic waveguide, we show here that the DRA may be integrated with a planar radiator feed and used effectively in an array configuration. The array demonstrated is a 2 × 2 array designed for operation at 15 GHz. Each DRA is 6 λ long. The array is fabricated on a 100 mm × 100 mm substrate with 52 mm separation between elements. The array radiation efficiency is 80% for an array gain of 19 dBi. © 2020 IEEE.

关键词： Radiators

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DAANet: Dual Attention Aggregating Network for Salient Object Detection

DAANet: Dual Attention Aggregating Network for Salient Objec...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Li, Yijie Wang, Hewei Li, Zhenqi Wang, Shaofan Dev, Soumyabrata Zuo, Guoyu Beijing University of Technology Beijing-Dublin International College Beijing100124 China Beijing University of Technology Faculty of Information Technology Beijing100124 China University College Dublin School of Computer Science Dublin Ireland Beijing University of Technology Faculty of Information Technology China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China

ISBN: (纸本)9798350325706

When the Convolutional neural network (CNN) has been introduced for computer vision, many researches use CNN-based model to perform salient object detection (SOD). In recent years, The feature pyramid network (FPN) based structure is more popular for salient object detection tasks. In this paper, to improve the overall performance of salient object detection tasks, we propose a dual attention aggregating network (DAANet), which is an FPN-based deep convolutional neural network with a dual attention aggregation module (DAAM) and dilated refinement block (DRB). The DRB module uses convolutions with different dilation rates to expand the receptive field. The DAAM considers the salient map prediction from the low-level output as pseudo-attention which can efficiently aggregate multi-scale information. The convolution block attention module (CBAM) in DAAM can refine the aggregation of pseudo-attention which enables better performance. We evaluate DAANet on six benchmark datasets that prove the effectiveness of DAANet and its components. Our implementation can be found at: https://***/Att100/DAANet. © 2023 IEEE.

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Current opinions on large cellular models

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Quantitative Biology 2024年第4期12卷 433-443页

作者： Minsheng Hao Lei Wei Fan Yang Jianhua Yao Christina V.Theodoris Bo Wang Xin Li Ge Yang Xuegong Zhang MOE Key Laboratory of Bioinformatics and Bioinformatics Division BNRISTDepartment of AutomationTsinghua UniversityBeijingChina AI Lab TencentShenzhenChina Gladstone Institutes and University of California San FranciscoCaliforniaUSA Department of Computer Science University of TorontoTorontoOntarioCanada State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Life Sciences and School of Medicine Center for Synthetic and Systems BiologyTsinghua UniversityBeijingChina

1|*** language models(LLMs)have made breakthroughs in natural language processing(NLP)and understanding,and have brought revolutions in many other fields[1-4].Inspired by those successes,several large cellular models(LCMs)adopting similar structures of LLMs have been developed for single-cell transcriptomics,including(but not limited to)scBERT[5],Geneformer[6],scGPT[7],scFoundation[8],and GeneCompass[9].The practices of these models have shown LCMs’power and potential in various biological tasks and illustrated the possibilities of revolutionizing future biological studies by LCMs.

关键词： large cellular models large language models scBERT Geneformer scGPT scFoundation GeneCompass single-cell transcriptomics

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Holographic-Pattern Based Multi-User Beam Training in RHS-Aided Hybrid Near-Field and Far-Field Communications

arXiv

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arXiv 2024年

作者： Zhang, Shupei Di, Boya Kaushik, Aryan Eldar, Yonina C. State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China The Department of Computing & Mathematics Manchester Metropolitan University United Kingdom The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Reconfigurable holographic surfaces (RHSs) have been suggested as an energy-efficient solution for extremely large-scale arrays. By controlling the amplitude of RHS elements, high-gain directional holographic patterns can be achieved. However, the complexity of acquiring real-time channel state information (CSI) for beamforming is exceedingly high, particularly in large-scale RHS-assisted communications, where users may distribute in the near-field region of RHS. This paper proposes a one-shot multi-user beam training scheme in large-scale RHS-assisted systems applicable to both near and far fields. The proposed beam training scheme comprises two phases: angle search and distance search, both conducted simultaneously for all users. For the angle search, an RHS angular codebook is designed based on holographic principles so that each codeword covers multiple angles in both near-field and far-field regions, enabling simultaneous angular search for all users. For the distance search, we construct the distance-adaptive codewords covering all candidate angles of users in a real-time way by leveraging the additivity of holographic patterns, which is different from the traditional phase array case. Simulation results demonstrate that the proposed scheme achieves higher system throughput compared to traditional beam training schemes. The beam training accuracy approaches the upper bound of exhaustive search at a significantly reduced overhead. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Uncertainty Quantification and Quality Control for Heatmap-based Landmark Detection Models

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IEEE Transactions on Medical Imaging 2025年

作者： Feng, Yong Yang, Jinzhu Tang, Lingzhi Sun, Song Wang, Yonghuai Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Northeastern University School of Computer Science and Engineering Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China The First Hospital of China Medical University Department of Cardiovascular Ultrasound Shenyang China

Uncertainty quantification is a vital aspect of explainable artificial intelligence that fosters clinician trust in medical applications and facilitates timely interventions, leading to safer and more reliable outcomes. Although deep learning models have reached clinically acceptable accuracy in anatomical landmark detection, their predictions remain susceptible to contextual noise due to the small size of the target structures, making uncertainty quantification more challenging than in classification and segmentation tasks. This paper presents an end-to-end uncertainty quantification method tailored for heatmapbased anatomical landmark detection models, designed to improve both interpretability and controllability in clinical applications. Leveraging Dempster-Shafer Theory and Subjective Logic Theory, we implement probability assignment and uncertainty quantification through a single forward pass to ensure computational efficiency. We introduce an evidence map that captures the strength of landmark evidence, alongside an uncertainty map that calibrates predicted probabilities within the Subjective Logic framework. The interaction between these two components, facilitated by a cross-attention mechanism, further improves landmark detection accuracy and enhances the effectiveness of uncertainty quantification. Experimental results demonstrate that the proposed method maintains detection accuracy, even in noisy environments, while outperforming state-of-the-art methods in terms of uncertainty quantification and quality control. Furthermore, the model effectively identifies out-of-distribution data solely through calibrated probabilities when encountering inconsistencies in multi-center data and novel data, underscoring its potential for clinical applications. © 1982-2012 IEEE.

关键词： Model predictive control

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BECNN: Bias Field Estimation CNN Trained with a Dual Route Implicit Supervised Learning Strategy

BECNN: Bias Field Estimation CNN Trained with a Dual Route I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuaizheng Chen Chaolu Feng Wei Li Jinzhu Yang Dazhe Zhao School of Computer Science And Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Bias fields adversely affect various automatic analysis technologies. Therefore, bias field correction is essential. However, deep learning based methods encounter challenges in obtaining ground truth. Although existing methods attempt to address this problem by using training-free techniques or constructing datasets with approximations of the ground truth, the lack of task-oriented guidance, training instability, and inappropriate use of approximations still impact performance. Additionally, different approaches for bias field correction, i.e., estimating bias fields versus directly restoring clean images, exhibit different performances. However, there is no consensus on the best way, resulting in limited performance in some cases. To address these problems, we propose a bias field generation method to construct the dataset and provide task-oriented information. We then propose the concept of Equivalent of Residual Mapping (ERM) to analyze the advantages of estimating bias fields. According to ERM, we propose the Bias field Estimation Convolutional Neural Network (BECNN). Finally, we propose the Dual Route Implicit Supervised Learning (DRISL) strategy to balance the guidance derived from approximations of the ground truth with over-dependence on them. The proposed method is compared qualitatively and quantitatively with the correlated methods. Experiment results demonstrate that the proposed method performs effectively both on bias field estimation and correction.

关键词： Training Measurement Deep learning Image resolution Computational modeling Supervised learning Estimation Image restoration Computational efficiency Convolutional neural networks

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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition systems

A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Kulwa, Frank Samuel, Oluwarotimi Williams Asogbon, Mojisola Grace Oyemakinde, Tolulope Tofunmi Obe, Olumide Olayinka Li, Guanglin Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China University of Derby School of Computing and Engineering DerbyDE22 3AW United Kingdom Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9781665493840

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario ( © 2023 IEEE.

关键词： Electrodes

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