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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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Enhanced Brain Tumor Detection Using Transfer Learning with Fine-Tuned Efficientnet-B3 Blocks and Graph-Based Spatial Attention Capsule Networks 21

Enhanced Brain Tumor Detection Using Transfer Learning with ...

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21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Aggrey, Esther Stacy E.B Zhen, Qin Kodjiku, Seth Larweh Fiasem, Delali Linda Sey, Collins Ejiyi, Chukwuebuka Joseph Ntia, Nsikakabasi Williamson School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Technology Zhejiang Gongshang University Hangzhou China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China

ISBN: (纸本)9798331519254

Brain tumors are critical medical conditions that require timely and accurate diagnosis to improve survival outcomes. This research proposes a cutting-edge framework for brain tumor classification, combining fine-tuned EfficientNet-B3 blocks with graph-based spatial attention capsule networks. By addressing challenges such as limited data availability, overfitting, and the complex nature of tumor structures, the framework employs partial transfer learning, advanced feature extraction, and attention mechanisms to effectively model spatial relationships in MRI images. Evaluated on a dataset of 3,064 contrast-enhanced brain MRI images, the model demonstrated exceptional performance, achieving 99.85% classification accuracy and notable improvements in precision, recall, and F1 scores for glioma, meningioma, and pituitary tumours. The use of advanced data augmentation techniques significantly improved the model's ability to generalize, while its computational efficiency makes it well-suited for clinical diagnostic applications. © 2024 IEEE.

关键词： Dynamic contrast enhanced MRI

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high-order Joint Constituency and Dependency Parsing 30

High-order Joint Constituency and Dependency Parsing

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Joint 30th International conference on Computational Linguistics and 14th International conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Gu, Yanggan Hou, Yang Wang, Zhefeng Duan, Xinyu Li, Zhenghua School of Computer Science and Technology Soochow University China Huawei Inc. China

ISBN: (纸本)9782493814104

This work revisits the topic of jointly parsing constituency and dependency trees, i.e., to produce compatible constituency and dependency trees simultaneously for input sentences, which is attractive considering that the two types of trees are complementary in representing syntax. The original work of Zhou and Zhao (2019) performs joint parsing only at the inference phase. They train two separate parsers under the multi-task learning framework (i.e., one shared encoder and two independent decoders). They design an ad-hoc dynamic programming-based decoding algorithm of O(n5) time complexity for finding optimal compatible tree pairs. Compared to their work, we make progress in three aspects: (1) adopting a much more efficient decoding algorithm of O(n4) time complexity, (2) exploring joint modeling at the training phase, instead of only at the inference phase, (3) proposing high-order scoring components to promote constituent-dependency interaction. We conduct experiments and analysis on seven languages, covering both rich-resource and low-resource scenarios. Results and analysis show that joint modeling leads to a modest overall performance boost over separate modeling, but substantially improves the complete matching ratio of whole trees, thanks to the explicit modeling of tree compatibility. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Dynamic programming

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R-HLS: An IR for Dynamic high-Level Synthesis and Memory Disambiguation based on Regions and State Edges 24

R-HLS: An IR for Dynamic High-Level Synthesis and Memory Dis...

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43rd International conference on computer-Aided Design, ICCAD 2024

作者： Metz, David Reissmann, Nico Själander, Magnus Norway

ISBN: (纸本)9798400710773

Dynamically scheduled hardware enables high-level synthesis (HLS) for applications with irregular control flow and latencies, which perform poorly with conventional statically scheduled approaches. Since dynamically scheduled hardware is inherently data flow based, it is beneficial to have an intermediate representation (IR) that captures the global data flow to enable easier transformations. State-of-the-art dynamic HLS utilize control flow based IRs, which model data flow only at the basic block level, requiring the rediscovery of inter-block parallelism. The Regionalized Value State Dependence Graph (RVSDG) is an IR that models (1) control flow as part of the global data flow utilizing regions and (2) memory dependencies using state edges. We propose R-HLS, a new RVSDG dialect targeted for dynamic high-level synthesis. R-HLS explicitly models control flow decisions, routing, and memory, which are only abstractly represented in the RVSDG. Expressing the control flow as part of the data flow reduces the need for complex optimizations to extract performance and enables easy conversion to parallel circuits. Furthermore, we present a distributed memory disambiguation optimization that leverages memory state edges to decouple address generation from data accesses, resulting in resource efficient out-of-program-order execution of memory operations. Our results show that R-HLS effectively exposes parallelism, resulting in fewer executed cycles and a 10% speedup on average, compared to the state-of-the-art in dynamic HLS with optimized memory disambiguation. These results are achieved with a significant reduction in resource utilization, such as a 79% reduction in lookup-tables and 22% reduction in flip-flops, on average. © 2024 Copyright is held by the owner/author(s).

关键词： Table lookup

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TOWARDS ENERGY EFFICIENT SPIKING NEURAL NETWORKS: AN UNSTRUCTURED PRUNING FRAMEWORK 12

TOWARDS ENERGY EFFICIENT SPIKING NEURAL NETWORKS: AN UNSTRUC...

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12th International conference on Learning Representations, ICLR 2024

作者： Shi, Xinyu Ding, Jianhao Hao, Zecheng Yu, Zhaofei Institute for Artificial Intelligence Peking University China School of Computer Science Peking University China

Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to Artificial Neural Networks (ANNs) when deployed on neuromorphic chips. While recent studies have demonstrated the impressive performance of deep SNNs on challenging tasks, their energy efficiency advantage has been diminished. Existing methods targeting energy consumption reduction do not fully exploit sparsity, whereas powerful pruning methods can achieve high sparsity but are not directly targeted at energy efficiency, limiting their effectiveness in energy saving. Furthermore, none of these works fully exploit the sparsity of neurons or the potential for unstructured neuron pruning in SNNs. In this paper, we propose a novel pruning framework that combines unstructured weight pruning with unstructured neuron pruning to maximize the utilization of the sparsity of neuromorphic computing, thereby enhancing energy efficiency. To the best of our knowledge, this is the first application of unstructured neuron pruning to deep SNNs. Experimental results demonstrate that our method achieves impressive energy efficiency gains. The sparse network pruned by our method with only 0.63% remaining connections can achieve a remarkable 91 times increase in energy efficiency compared to the original dense network, requiring only 8.5M SOPs for inference, with merely 2.19% accuracy loss on the CIFAR-10 dataset. Our work suggests that deep and dense SNNs exhibit high redundancy in energy consumption, highlighting the potential for targeted SNN sparsification to save energy. Codes are available at https://***/xyshi2000/Unstructured-Pruning. © 2024 12th International conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Neural networks

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A fuzzing method to reduce the number of generated invalid testcases 2

A fuzzing method to reduce the number of generated invalid t...

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2022 2nd conference on high performance Computing and Communication Engineering, HPCCE 2022

作者： Kong, Chaochao Zhang, Fan School of Mathematics & Computer Science Wuhan Polytechnic University Wuhan430023 China

ISBN: (纸本)9781510663374

Fuzzing is a technique to detect vulnerabilities in target programs by generating testcases in random mutation phase. However, for path constraints such as magic numbers in the program, the fuzzing random mutation phase can easily generate a large number of invalid test cases that do not result in new code coverage and trigger new crashes, resulting in low code coverage for Fuzzing. In this paper, firstly, the random mutation process of Fuzzing is investigated and it is found that 1) dictionaries help to improve the code coverage of Fuzzing and 2) the random mutation stage generates a large number of invalid testcases due to randomness. Then, we propose a novel method called Dict-MAB (Dictionary and multi-armed bandit). Dict-MAB improves the code coverage of fuzzing by reducing the number of invalid test cases generated during random mutation. Specifically, the extracted proprietary dictionary can assist fuzzing to pass complex path constraints such as magic numbers, while modeling the random mutation phase as a multi-armed bandit machine model can reduce the randomness of this phase and reduce the number of invalid testcases *** experiments show that the number of valid testcases generated by Dict-MAB is improved by 27.49% on average. In particular, the code coverage on lcms and libjpeg improved by 13.71 % on average. © 2023 SPIE.

关键词： Random processes

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Integration and Application of Blockchain Technology and SPD Model in the Management of Medical Consumables 10

Integration and Application of Blockchain Technology and SPD...

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10th International conference on Industrial Engineering and Applications, ICIEA 2023

作者： Gao, Yalan Li, Minrui Yu, Yuanhong Luo, Mengyue Gong, Yiling Business School Sichuan University China Law School Sichuan University China School of Computer Science Sichuan University China

ISBN: (纸本)9781643684086

With the accelerating development of the healthcare sector in China, the drawbacks of the traditional management mode for medical consumables are emerging due to the great variety of models and frequent changes of suppliers and the management dilemma of high cost and low efficiency has hindered the modernization of hospital management is frustrated for the high cost and low efficiency. This paper firstly analyzes the problems facing traditional management mode for medical consumables, recognizes the importance of the SPD supply chain model applied to medical consumables, further discusses the approaches of applying SPD management mode for medical consumables based on blockchain technology, analyzes the risks of 'blockchain + SPD' model and puts forward suggestions accordingly. It turns out that the SPD management mode for medical consumables based on blockchain technology, supported by the supply chain management theory and blockchain information technology, can help effectively reduce the loss of cost caused by information asymmetry, opacity and untraceable, improve the operation and management efficiency of hospitals, and enhance the quality of medical peripheral products and service capability of hospitals. This study provides a reference for the innovation of theoretical framework of supply chain management of medical consumables and the efficient development of blockchain technology in the management of medical consumables. © 2023 The authors and IOS Press.

关键词： Blockchain

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ShrinkHPO: Towards Explainable Parallel Hyperparameter Optimization 40

ShrinkHPO: Towards Explainable Parallel Hyperparameter Optim...

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40th IEEE International conference on Data Engineering, ICDE 2024

作者： Mu, Tianyu Wang, Hongzhi Tang, Haoyun Shao, Xinyue School of Computer Science Harbin Institute of Technology Harbin China National University of Singapore Singapore

ISBN: (纸本)9798350317152

In this era of exploding data volumes, more and more complex data analysis tasks are now accomplished by machine learning (ML) or deep learning (DL). Despite the powerful and flexible task processing capability, it also brings challenges such as how to reasonably design the optimal hyperparameters configuration and the huge consumption of time for a single validation. Existing Hyperrarameter Optimization (HPO) methods are gradually facing performance bottlenecks. However, in an era where progressively data-centric big data analytics methods are prevalent, such efficiency issues need to be urgently addressed in the design of intelligent DBMS. In this paper, we propose ShrinkHPO, an efficient and explainable-designed HPO approach with a major focus on (a) efficient hyperparameter configuration search strategy, (b) asynchronous executing intervention, and (c) XAI (eXplainable AI) design. ShrinkHPO employs a hyperparameter weight estimation strategy named Shrink-search together with an asynchronous execution design to improve HPO efficiency. To the best of our knowledge, ShrinkHPO is the first HPO method that introduces explainable analysis and verification to ensure the judgment of 'high significance' hyperparameters. We also conduct a series of experiments on data analysis tasks in the database domain (classification, regression, time series classification, CASH [1], etc.), collecting HPO results on the usual ML or DL models in each task. Compared to SOTA asynchronous and sequential HPO baselines, ShrinkHPO achieves top performance on both accuracy (RMSE for regression tasks) and time cost, accelerating from 20% to a maximum of 2.2 x. © 2024 IEEE.

关键词： Data Analytics

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Exploring Heterogeneous Computing Environments: A Preliminary Analysis of Python and SYCL performance 11th

Exploring Heterogeneous Computing Environments: A Preliminar...

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11th conference on Cloud Computing, Big Data and Emerging Topics, JCC-BD and ET 2023

作者： Faqir-Rhazoui, Youssef García, Carlos Department of Computer Architecture and Automatics Universidad Complutense de Madrid Madrid Spain

ISBN: (纸本)9783031409417

Python’s popularity has grown rapidly over the past decade due to its high productivity factor. While Python is popular in scientific computing, its interpreted nature and limitations on parallelization result in slower performance compared to compiled languages. Despite this, Python is still a valuable tool for high-performance computing (HPC) due to its ease of programming and optimization capabilities. This paper discusses the performance gap between Python and compiled languages like SYCL when working with heterogeneous computing environments and the challenges in developing portable and high-performance native extensions. It highlights the development of the dpctl Python library, which provides a portable and architecture-agnostic API for writing extensions and supports asynchronous SYCL kernel execution. We developed four benchmarks across CPU, integrated GPU, and discrete Intel Arc GPU, finding that SYCL outperformed Python in all cases, but optimizations could be made to improve Python performance. This research also highlights issues encountered when porting Python code, limitations in using Numpy, and current Intel Arc performance issues. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Python

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Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal Classification

Multimodal Dynamics: Dynamical Fusion for Trustworthy Multim...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Han, Zongbo Yang, Fan Huang, Junzhou Zhang, Changqing Yao, Jianhua Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Tencent AI Lab Bellevue WA 98004 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Integration of heterogeneous and high-dimensional data (e.g., multiomics) is becoming increasingly important. Existing multimodal classification algorithms mainly focus on improving performance by exploiting the complementarity from different modalities. However, conventional approaches are basically weak in providing trustworthy multimodal fusion, especially for safety-critical applications (e.g., medical diagnosis). For this issue, we propose a novel trustworthy multimodal classification algorithm termed Multimodal Dynamics, which dynamically evaluates both the feature-level and modality-level informativeness for different samples and thus trustworthily integrates multiple modalities. Specifically, a sparse gating is introduced to capture the information variation of each within-modality feature and the true class probability is employed to assess the classification confidence of each modality. Then a transparent fusion algorithm based on the dynamical informativeness estimation strategy is induced. To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification. Extensive experiments are conducted on multimodal medical classification datasets. In these experiments, superior performance and trustworthiness of our algorithm are clearly validated compared to the state-of-the-art methods.

关键词： computer vision Heuristic algorithms Estimation Classification algorithms Pattern recognition Medical diagnosis Medical diagnostic imaging

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STUDY ON THE EFFECT OF SR²⁺-TI⁴⁺ CO-DOPED YIG FERRITE MICROWAVE MAGNETIC PROPERTIES AND DIELECTRIC PROPERTY MODULATION ON MICROWAVE FERRITE CIRCULATOR performance 19

STUDY ON THE EFFECT OF SR<SUP>2+</SUP>-TI<SUP>4+</SUP> CO-DO...

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19th IEEE International computer conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Yin Qisheng Liu Yingli Wu Jian Li Jie Chen Jianfeng Lu Shifan Univ Elect Sci & Technol China State Key Lab Elect Thin Films & Integrated Devic Chengdu 610054 Peoples R China

ISBN: (纸本)9781665493895

This work focuses on microwave properties of Y3-xSrxFe5xTixO12 (when x = 0.0 - 0.5) prepared using traditional ceramic technology. Experimental results showed that proper Sr2+-Ti4+ ion substitution increased the dielectric constant (er) while decreasing the ferromagnetic resonance linewidth (Delta H) of YIG ferrites. Y2.7Sr0.3Fe4.7Ti0.3O12 sintered at 1450. for 6 h had the optimal microwave dielectric and magnetic properties: epsilon(r) = 16.8, tan delta(epsilon)= 5.3x10(-4), saturation magnetization (4 pi MS) = 1850 G, Delta H = 56 Oe. The YIG ferrite in the previous study's a Y-junction microstrip circulator was replaced with this material to achieve double the working bandwidth. The extended bandwidth of the circulator shows that the improvement of the properties of ferrite materials is an important factor in improving the performance of microwave devices used.

关键词： Circulators Microwave high-frequency components Microwave dielectric properties Ferromagnetic resonance linewidth Garnet YIG ferrites

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