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A Retina-LGN-V1 Structure-like Spiking Neuron Network for Image Feature Extraction 21

A Retina-LGN-V1 Structure-like Spiking Neuron Network for Im...

引用

Proceedings of the 2021 5th International Conference on Video and Image processing

作者： Sheng Ji Qijun Gu Ye Yuan Peng Zhao Tao Fang Hong Huo Xin Niu Department of Automation_School of Electronic Information and Electrical Engineering_Key Laboratory of System Control and Information Processing Ministry of Education Shanghai Jiao Tong University China College of Computer Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology China

ISBN: (纸本)9781450385893

SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. The additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. The experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes.

关键词： Additive Spiking Timing Dependent Plasticity Learning Primary Visual Pathway Image Classification Spiking Neural Network

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Power and performance analysis of the Graph 500 benchmark on the Single-chip Cloud computer

Power and performance analysis of the Graph 500 benchmark on...

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International Conference on Cloud Computing and Internet of Things (CCIOT)

作者： Zhiquan Lai King Tin Lam Cho-Li Wang Jinshu Su College of Computer National University of Defense Technology Changsha China Department of Computer Science The University of Hong Kong Hong Kong China National Key Laboratory of Parallel and Distributed Processing (PDL) Changsha China

ISBN: (纸本)9781479947638

The concerns of data-intensiveness and energy awareness are actively reshaping the design of high-performance computing (HPC) systems nowadays. The Graph500 is a widely adopted benchmark for evaluating the performance of computing systems for data-intensive workloads. In this paper, we introduce a data-parallel implementation of Graph500 on the Intel Single-chip Cloud computer (SCC). The SCC features a non-coherent many-core architecture and multi-domain on-chip DVFS support for dynamic power management. With our custom-made shared virtual memory programming library, memory sharing among threads is done efficiently via the shared physical memory (SPM) while the library has taken care of the coherence. We conduct an in-depth study on the power and performance characteristics of the Graph500 workloads running on this system with varying system scales and power states. Our experimental results are insightful for the design of energy-efficient many-core systems for data-intensive applications.

关键词： Kernel Benchmark testing computer architecture Programming Power measurement Computational modeling Runtime

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study 9

Evaluating the Performance to Detecting Topic Shift of Large...

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9th International Conference on Intelligent Computing and Signal processing, ICSP 2024

作者： Tian, Pingfang Liu, Deting Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Institute of Scientific and Technical Information of China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Beijing100038 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan430065 China

ISBN: (纸本)9798350376548

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor. © 2024 IEEE.

关键词： adjacent utterances level ChatGPT contextual level single utterances level topic shift detection

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Translation Non-negative Matrix Factorization with Fast Optimization

Translation Non-negative Matrix Factorization with Fast Opti...

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IEEE International Conference on systems, Man, and Cybernetics

作者： Yuanyuan Wang Naiyang Guan Bin Mao Xuhui Huang Zhigang Luo Department of Basic Courses Army Officer Academy Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Science National University of Defense Technology College of Science National University of Defense Technology College of Computer Science National University of Defense Technology

ISBN: (纸本)9781479938414

Non-negative matrix factorization (NMF) reconstructs the original samples in a lower dimensional space and has been widely used in pattern recognition and data mining because it usually yields sparse representation. Since NMF leads to unsatisfactory reconstruction for the datasets that contain translations of large magnitude, it is required to develop translation NMF (TNMF) to first remove the translation and then conduct a decomposition. However, existing multiplicative update rule based algorithm for TNMF is not efficient enough. In this paper, we reformulate TNMF and show that it can be efficiently solved by using the state-of-the-art solvers such as NeNMF. Experimental results on face image datasets confirm both efficiency and effectiveness of the reformulated TNMF.

关键词： Non-negative matrix factorization (NMF) Translation transformation NeNMF

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Prediction of exosomal piRNAs based on deep learning for sequence embedding with attention mechanism

Prediction of exosomal piRNAs based on deep learning for seq...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Yajun Ding, Yulian Li, Aimin Fei, Rong Guo, Xie Wu, Fangxiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9781665468190

PIWI-interacting RNAs (piRNAs) are a type of small non-coding RNAs which bind with the PIWI proteins to exert biological effects in various regulatory mechanisms. A growing amount of evidence reveals that exosomal piRNAs are potential biomarkers for diagnosis and treatment of complex diseases. Effective methods for the prediction of exosomal piRNAs are the foundation of piRNA functional research. In this study, we propose an end-to-end deep network for identifying exosomal piRNAs based on features learned from natural language processing (NLP) models for sequence embedding with attention mechanism. First, a benchmark dataset is constructed by processing piRNA subcellular localization annotated data and sequence data. Moreover, bagging positive unlabeled learning is applied to get the reliable negative set. Finally, we treat a piRNA sequence as a sentence and its k-mer subsequence as a token. Sequence embedding models with self-attention mechanism is designed to extract features from exosome piRNA sequences, which are used for the prediction task. Compared with three competing methods, our model achieves the best performance and reveals the key factors of exosomal piRNA sequences by the attention mechanism. Our model characterizes exosomal piRNAs and could be beneficial for researchers to investigate exosomal piRNAs' functions. © 2022 IEEE.

关键词： Embeddings

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Static program decomposition among machines in an SIMD/SPMD heterogeneous environment with non-constant mode switching costs

Static program decomposition among machines in an SIMD/SPMD ...

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Heterogeneous Computing Workshop

作者： D.W. Watson J.K. Antonio H.J. Siegel M.J. Atallah Department of Computer Science Utah State University Logan UT USA Parallel Processing Laboratory School of electrical Engineering Purdue University West Lafayette IN USA Computer Sciences Department Purdue University West Lafayette IN USA

The problem of minimizing the execution time of programs within a heterogeneous environment is considered. Different computational characteristics within a parallel algorithm may make switching execution from one machine to another beneficial; however, the cost of switching between machines during the execution of a program must be considered. This cost is not constant, but depends on data transfers needed as a result of the move. Therefore, determining a minimum-cost assignment of machines to program segments is not straightforward. A previously presented block-based mode selection (BBMS) approach is used as a basis to develop a heuristic method for assigning machines to program segments of data-parallel algorithms. Simulation results of parallel program behavior using the heuristic indicate that good assignments are possible without resorting to exhaustive search techniques.< >

关键词： parallel processing parallel machines Switches Cost function computer science Concurrent computing parallel algorithms Laboratories Contracts Testing

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Implementation of visual MPI parallel program performance analysis tool for cluster environments

Implementation of visual MPI parallel program performance an...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Kuan-Ching Li Hsun-Chang Chang Chao-Tung Yang Li-Jen Chang Hsiang-Yao Cheng Liang-Teh Lee Parallel and Distributed Processing Center CSIM Department Providence University Taichung Taiwan HPC Laboratory CSIE Department Tunghai University Taichung Taiwan Department of Computer Science and Engineering Tatung University Taipei Taiwan

ISBN: (纸本)0769522491

In this paper, we present visual tool for performance measurement and analysis of MPI parallel programs in cluster environments. Most of tools available today for cluster systems show solely system performance data (e.g., CPU load, memory usage, network bandwidth, machine-room temperature, server average load, among others), being more suitable for system administrators who maintain such system. The visual tool is designed to show performance data of all computer nodes involved in the execution of MPI parallel program, such as CPU load level and memory usage. Additionally, this tool is able to display comparative performance data charts of multiple executions of the application (instrumented with MPI interface) under development.

关键词： Performance analysis Measurement system performance Bandwidth Temperature Network servers Concurrent computing computer displays Application software Instruments

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An open-source development and simulation platform for smart wheelchairs

An open-source development and simulation platform for smart...

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6th International Conference on Intelligent Robotics and Applications, ICIRA 2013

作者： Liao, Wenlong Chen, Weidong Wei, Zhixuan Zhang, Wenxi Wang, Jingchuan Li, Kang Department of Automation and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai 200240 China Harbin 150001 China Electrical Engineering and Computer Science Queen's University Belfast Belfast BT9 5AH United Kingdom

ISBN: (纸本)9783642408519

Simulator for smart wheelchairs is of great significance for both driver training and algorithm design. A development and simulation platform for wheelchairs is presented in this paper. On this platform, simulations of kinematics, dynamics and sensors are integrated, and a virtual prototype of wheelchair and virtual world are modled also. The sensors include encoders, a laser range finder and a set of Kinect. This platform is implemented in Gazebo and ROS, and is completely open source and freely available. Experiments verifies that this platform can be used as a valid tool for driver training and algorithm design. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Virtual reality

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Constraint-Relaxation Approach for Nonnegative Matrix Factorization: A Case Study

Constraint-Relaxation Approach for Nonnegative Matrix Factor...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jue Wang Naiyang Guan Xuhui Huang Zhigang Luo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Hunan P.R. China Department of Computer Science and Techonoly College of Computer National University of Defense Technology Hunan P.R. China

ISBN: (纸本)9781479986989

Nonnegative matrix factorization (NMF) is a powerful technique for dimensionality reduction. Conventional NMF algorithms usually keep the matrices W and H nonnegative while iterating. However, to get the NMF of a matrix, it's unnecessary to force the temporary solutions in iterations nonnegative. In this paper, we propose a two-staged approach for NMF. At the relaxation stage, the nonnegative constraint of temporary solutions is relaxed and a real valued matrix factorization is generated. At the constraint stage, the real valued matrix factorization is transformed to a nonnegative matrix factorization by an invertible linear transformation. Based on this approach, we study on exact nonnegative matrix factorization when rank=2. We proved that, given two real valued matrices of rank=2, there exists an invertible linear transformation which can transform the real valued matrices to nonnegative matrices with their product stable. We propose an algorithm to find out the transformation. When rank is higher than 2, this kind of transformation may not exist. In the experiments, it's showed that this approach can reach a nonnegative matrix factorization with lower reconstruction error than conventional methods, and the technique for rank=2 exact NMF works well.

关键词： Algorithm design and analysis Matrix decomposition Additives computers Transforms Matrix converters Clustering algorithms

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