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arXiv 2023年

作者： Li, Boyan Leng, Luziwei Shen, Shuaijie Zhang, Kaixuan Zhang, Jianguo Liao, Jianxing Cheng, Ran The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Advanced Computing and Storage Lab Huawei Technologies Co. Ltd. Shenzhen518055 China

Advancements in adapting deep convolution architectures for Spiking Neural Networks (SNNs) have significantly enhanced image classification performance and reduced computational burdens. However, the inability of Multiplication-Free Inference (MFI) to align with attention and transformer mechanisms, which are critical to superior performance on high-resolution vision tasks, imposing limitations on these gains. To address this, our research explores a new pathway, drawing inspiration from the progress made in Multi-Layer Perceptrons (MLPs). We propose an innovative spiking MLP architecture that uses batch normalization to retain MFI compatibility and introducing a spiking patch encoding layer to enhance local feature extraction capabilities. As a result, we establish an efficient multi-stage spiking MLP network that blends effectively global receptive fields with local feature extraction for comprehensive spike-based computation. Without relying on pre-training or sophisticated SNN training techniques, our network secures a top-1 accuracy of 66.39% on the ImageNet-1K dataset, surpassing the directly trained spiking ResNet-34 by 2.67%. Furthermore, we curtail computational costs, model parameters, and simulation steps. An expanded version of our network compares with the performance of the spiking VGG-16 network with a 71.64% top-1 accuracy, all while operating with a model capacity 2.1 times smaller. Our findings highlight the potential of our deep SNN architecture in effectively integrating global and local learning abilities. Interestingly, the trained receptive field in our network mirrors the activity patterns of cortical cells. Source codes are publicly accessible at https://***/EMI-Group/mixer-snn. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Object Localization in the Presence of Noise

Object Localization in the Presence of Noise

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IEEE Conference on Aerospace

作者： Mridul Gupta Mary L. Comer Edward J. Delp Jonathan Chan Mitchell Krouss Paul Martens Moses W. Chan Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Advanced Technology Center Lockheed Martin Corporation Sunnyvale California USA

Detection and localization of small objects is crucial in many applications, such as surveillance, microscopy, and astronomy. In many space-based imaging applications, the spatial resolution of the imaging system is not enough to localize a small object (point source). Low signal-to-noise ratio (SNR) increases the difficulty such as when the objects are dim. We can model the noise in several ways and consider two cases in this paper. Noise is modeled as additive spatially invariant Gaussian distributed noise and spatially varying Poisson distributed noise. We assume that we have a coarse estimate for the spatial location of an object in an image and that a 2D symmetric Gaussian function approximates the point spread function of the imaging system and, thus, the gray-level distribution of an object. In this paper, we describe a machine learning method that minimizes a cost function derived from the maximum likelihood estimation of the observed image to determine an object's sub-pixel spatial location and amplitude. We call the proposed method Sub-Pixel Location Estimator for Small Objects (SPLEO). We compare the variance of SPLEO (both spatial location and object amplitude estimates) with the Cramer-Rao lower bound (CRLB).

关键词： Location awareness Training Maximum likelihood estimation Surveillance Microscopy Machine learning Cost function

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ExpoEv: Enhancing Social Recommendation Service with Social Exposure and Feature Evolution

ExpoEv: Enhancing Social Recommendation Service with Social ...

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IEEE International Conference on Web Services (ICWS)

作者： Li Ma Zuowu Zheng Xiuqi Huang Zhaoxiang Zhang Xiaofeng Gao Jianxiong Guo Guihai Chen Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China

Social networks are widely recognized as highly effective information sources for social recommendation services. However, previous social recommendation methods assumed that a user’s preference factor and social trust factor shared a common latent feature space. Additionally, few studies have explored the incorporation of social information into the item domain for recommendations. To address these gaps, we propose ExpoEv, a deep collaborative filtering recommendation model that integrates social exposure based on feature evolution for social recommendation services. Specifically, we propose a social exposure module for both user and item domains that considers the number of items that a user’s social friends interact with. Furthermore, we introduce a feature evolution component that enables the incorporation of social exposure information with social trust and attribute factors in the context of social recommendation services. Experiments demonstrate the effectiveness of our model in the quality of recommendation service.

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Spacecraft Time-Series Online Anomaly Detection Using Deep Learning

Spacecraft Time-Series Online Anomaly Detection Using Deep L...

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IEEE Conference on Aerospace

作者： Sriram Baireddy Sundip R. Desai Richard H. Foster Moses W. Chan Mary L. Comer Edward J. Delp Video and Image Processing Lab (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA Advanced Technology Center Lockheed Martin Corporation Sunnyvale California USA

Anomaly detection in spacecraft telemetry channels is of great importance, especially considering the extremeness of the spacecraft operating environment. These anomalies often function as precursors for system failure. Currently, domain experts manually monitor telemetry channels, which is time-consuming and limited in scope. An automated approach to anomaly detection would be ideal, considering that each satellite system has thousands of channels to monitor. Deep learning models have been shown to be effective at capturing the normal behavior of the channels and flagging any abnormalities. However, each channel needs a unique model trained on it, and high performing models have been shown to require an increased training time. We instead propose training deep learning models in an online manner to quickly understand the behavior of a given channel and identify anomalies in real-time. This greatly reduces the amount of training time required to obtain a model for each channel. We present the results of our approach to show that we can achieve performance comparable to state-of-the-art spacecraft anomaly detection methods with minimal training time.

关键词： Training Space vehicles Deep learning Computational modeling Predictive models Real-time systems Behavioral sciences

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Diffsound: Discrete Diffusion Model for Text-to-sound Generation

arXiv

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arXiv 2022年

作者： Yang, Dongchao Yu, Jianwei Wang, Helin Wang, Wen Weng, Chao Zou, Yuexian Yu, Dong The Advanced Data and Signal Processing laboratory School of Electronic and Computer Engineering Peking University China Tencent AI Lab United States

Generating sound effects that people want is an important topic. However, there are limited studies in this area for sound generation. In this study, we investigate generating sound conditioned on a text prompt and propose a novel text-to-sound generation framework that consists of a text encoder, a Vector Quantized Variational Autoencoder (VQ-VAE), a token-decoder, and a vocoder. The framework first uses the token-decoder to transfer the text features extracted from the text encoder to a mel-spectrogram with the help of VQ-VAE, and then the vocoder is used to transform the generated mel-spectrogram into a waveform. We found that the token-decoder significantly influences the generation performance. Thus, we focus on designing a good token-decoder in this study. We begin with the traditional autoregressive (AR) token-decoder, which has shown state-of-the-art performance in previous sound generation works. However, the AR token-decoder always predicts the mel-spectrogram tokens one by one in order, which may introduce the unidirectional bias and accumulation of errors problems. Moreover, with the AR token-decoder, the sound generation time increases linearly with the sound duration. To overcome the shortcomings introduced by AR token-decoders, we propose a non-autoregressive token-decoder based on the discrete diffusion model, named Diffsound. Specifically, the Diffsound model predicts all of the mel-spectrogram tokens in one step and then refines the predicted tokens in the next step, so the best-predicted results can be obtained by iteration. Our experiments show that our proposed Diffsound model not only produces better text-to-sound generation results when compared with the AR token-decoder but also has a faster generation speed, i.e., MOS: 3.56 v.s 2.786, and the generation speed is five times faster than the AR decoder. Furthermore, to automatically assess the quality of generated samples, we define three different objective evaluation metrics i.e., Fréchet Inc

关键词： Signal encoding

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Environment-aware dynamic graph learning for out-of-distribution generalization 23

Environment-aware dynamic graph learning for out-of-distribu...

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

作者： Haonan Yuan Qingyun Sun Xingcheng Fu Ziwei Zhang Cheng Ji Hao Peng Jianxin Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University and School of Computer Science and Engineering Beihang University Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Department of Computer Science and Technology Tsinghua University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EADGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

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FastGR: Global Routing on CPU-GPU with Heterogeneous Task Graph Scheduler

FastGR: Global Routing on CPU-GPU with Heterogeneous Task Gr...

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作者： Liu, Siting Pu, Yuan Liao, Peiyu Wu, Hongzhong Zhang, Rui Chen, Zhitang Lv, Wenlong Lin, Yibo Yu, Bei The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Hong Kong Peking University School of Integrated Circuits Beijing100871 China HiSilicon Shenzhen518129 China Huawei Noah's Ark Lab Shatin Hong Kong Huawei Technologies Company Shenzhen China Beijing Advanced Innovation Center for Integrated Circuits Beijing100871 China

Running time is a key metric across the standard physical design flow stages. However, with the rapid growth in design sizes, routing runtime has become the runtime bottleneck in the physical design flow. As a result, speeding routing becomes a critical and pressing task for IC design automation. Aside from the running time, we need to evaluate the quality of the global routing solution since a poor global routing engine degrades the solution performance after the entire routing stage. This work takes both of them into consideration. We propose a global routing framework with GPU-accelerated routing algorithms and a heterogeneous task graph scheduler, called FastGR, to accelerate the procedure of the modern global router and improve its effectiveness. Its runtime-oriented version FastGRL achieves 2.489× speedup compared with the state-of-the-art global router. Furthermore, the GPU-accelerated L-shape pattern routing algorithm used in FastGRL can contribute to 9.324× speedup over the sequential algorithm on CPU. Its quality-oriented version FastGRH offers a 27.855% improvement of the number of shorts over the runtime-oriented version and still gets 1.970× faster than the most advanced global router. © 1982-2012 IEEE.

关键词： Graphics processing unit

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Learned Bloom Filter for Multi-key Membership Testing 28th

Learned Bloom Filter for Multi-key Membership Testing

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28th International Conference on Database Systems for advanced Applications, DASFAA 2023

作者： Li, Yunchuan Wang, Ziwei Yang, Ruixin Zhao, Yan Zhou, Rui Zheng, Kai University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China Aalborg University Aalborg Denmark Cloud Database Innovation Lab of Cloud BU Huawei Technologies Co. Ltd. Chengdu China

ISBN: (纸本)9783031306365

Multi-key membership testing refers to checking whether a queried element exists in a given set of multi-key elements, which is a fundamental operation for computing systems and networking applications such as web search, mail systems, distributed databases, firewalls, and network routing. Most existing studies for membership testing are built on Bloom filter, a space-efficient and high-security probabilistic data structure. However, traditional Bloom filter always performs poorly in multi-key scenarios. Recently, a new variant of Bloom filter that has combined machine learning methods and Bloom filter, also known as Learned Bloom Filter (LBF), has drawn increasing attention for its significant improvements in reducing space occupation and False Positive Rate (FPR). More importantly, due to the introduction of the learned model, LBF can well address some problems of Bloom filter in multi-key scenarios. Because of this, we propose a Multi-key LBF (MLBF) data structure, which contains a value-interaction-based multi-key classifier and a multi-key Bloom filter. To reduce FPR, we further propose an Interval-based MLBF, which divides keys into specific intervals according to the data distribution. Extensive experiments based on two real datasets confirm the superiority of the proposed data structures in terms of FPR and query efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data structures

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Spiking Neural Networks for LPI Radar Waveform Recognition with Neuromorphic Computing

Spiking Neural Networks for LPI Radar Waveform Recognition w...

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IEEE National Conference on Radar

作者： Alex Henderson Steven Harbour Chris Yakopcic Tarek Taha David Brown Justin Tieman Garrett Hall Dayton Engineering Advanced Projects Lab Southwest Research Institute Beavercreek OH Department of Electrical and Computer Engineering University of Dayton Dayton OH Advanced Electronic Warfare Department Southwest Research Institute Warner Robins GA RF Sensors and Systems Department Southwest Research Institute San Antonio TX

The ultra-wideband, frequency agile and high duty cycle features of Low Probability of Intercept (LPI) radar pose serious concerns for passive radar detection systems that must efficiently identify LPI radar modulation schemes with very low latency and high accuracy. For these tasks, conventional deep learning techniques have often been employed, though this comes at the cost of increased model complexity and high energy consumption when implemented in tactically relevant scenarios with significant channel deterioration and size, weight, and power (SWaP) constrained platforms. In this paper, we propose a neuromorphic approach based on energy-efficient and robust Spiking Neural Networks (SNNs). To demonstrate the effectiveness of our proposed approach, we perform a challenging LPI radar waveform recognition task with a Spiking Convolutional Neural Network (SCNN). Simulation results show that the SCNN achieves a 13-waveform recognition accuracy of 99.1% at OdB SNR with 1.51µJ of total energy per inference. It is found that our system outperforms non-spiking models with a reduction in model size and three orders of magnitude lower energy consumption. To the best of our knowledge, this work presents the first spike-based implementation of an LPI radar waveform recognition system. Moreover, this paper highlights the potential for extremely low SWaP neuromorphic hardware deployment to enable intelligent electronic support systems that demand high throughput efficiency and ultra-low power.

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Efficient COVID-19 detection using data mining algorithms: a comparison of basic and hybrid approaches

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Soft Computing 2025年第3期29卷 1437-1451页

作者： Saidi, Mohammad Gheibi, Mohammad Ghazikhani, Adel Lotfata, Aynaz Chahkandi, Benyamin Familsamavati, Sajad Behzadian, Kourosh Department of Computer Science Imam Reza International University Mashhad Iran MS of Software Engineering Information and Communication Technology Unit Mashhad Municipality Fire and Safety Services Organization Mashhad Iran Big Data Lab Imam Reza International University Mashhad Iran Institute for Nanomaterials Advanced Technologies and Innovation Technical University of Liberec Liberec46117 Czech Republic Tallinn10615 Estonia Department of Pathology Microbiology and Immunology School of Veterinary Medicine University of California Davis95616 United States Department of Urban and Regional Planning San Jose State University San Jose95192 United States Faculty of Civil and Environmental Engineering Gdansk University of Technology Narutowicza Street 11/12 80-233 Gdansk Poland Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Monterrey Mexico School of Computing and Engineering University of West London London United Kingdom

Accurate and efficient diagnosis of COVID-19 remains a significant challenge due to the limitations of current detection methods, such as blood tests and chest scans, which can be time-consuming and error-prone. This study aims to compare the performance of basic and hybrid data mining algorithms in diagnosing COVID-19, using blood test results and clinical information to identify the most effective approach. A dataset of 200 records from suspected and infected COVID-19 patients, with 23 characteristics and one diagnostic class, was analysed. Nine data mining algorithms were tested: four basic algorithms (Naive Bayes, Support Vector Machine, Decision Tree, K-Nearest Neighbor) and five hybrid algorithms (Random Forest, AdaBoost, Majority Voting, XGBoost, Bagging). The study also integrated Response Surface Methodology (RSM) and Adaptive-Network-based Fuzzy Inference System (ANFIS) to enhance model performance. The Bagging algorithm demonstrated superior performance with an accuracy of 88%, sensitivity of 74%, and F-criterion of 78%. The integration of RSM and ANFIS further showed that a smart model could be developed for efficient pandemic crisis management, achieving up to 100% accuracy when considering key factors like AST, Albumin, and CRP. The findings suggest that Bagging and hybrid data mining algorithms can significantly improve COVID-19 detection, reducing time and errors in identifying exposed individuals. The study highlights the potential of combining machine learning techniques with RSM-ANFIS models for effective pandemic management and decision-making in medical settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Support vector machines

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