Remote sensing image segmentation is a specialized form of semantic segmentation that presents unique challenges not typically found in general semantic segmentation tasks. The key issues addressed in this study are t...
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Quantum computer simulators are an indispensable tool for prototyping quantum algorithms and verifying the functioning of existing quantum computer hardware. The current largest quantum computers feature more than one...
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ISBN:
(数字)9798331524937
ISBN:
(纸本)9798331524944
Quantum computer simulators are an indispensable tool for prototyping quantum algorithms and verifying the functioning of existing quantum computer hardware. The current largest quantum computers feature more than one thousand qubits, challenging their classical simulators. Statevector quantum simulators are challenged by the exponential increase of representable quantum states with respect to the number of qubits, making more than fifty qubits practically unfeasible. A more appealing approach for simulating quantum computers is adopting the tensor network approach, whose memory requirements fundamentally depend on the level of entanglement in the quantum circuit, and allows simulating the current largest quantum computers. This work investigates and evaluates the CUDA-Q tensor network simulators on an Nvidia Grace Hopper system, particularly the Matrix Product State (MPS) formulation. We compare the performance of the CUDA-Q state vector implementation and validate the correctness of MPS simulations. Our results highlight that tensor network-based methods provide a significant opportunity to simulate large-qubit circuits, albeit approximately. We also show that current GPUaccelerated computation cannot fully utilize GPU efficiently in the case of MPS simulations.
In recent years, Transformers have achieved significant success in image fusion. These methods utilize self-attention mechanism across different spatial or channel dimensions and have demonstrated impressive performan...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
In recent years, Transformers have achieved significant success in image fusion. These methods utilize self-attention mechanism across different spatial or channel dimensions and have demonstrated impressive performance. However, existing methods only optimize along a single dimension and struggle to simultaneously capture the complex dependencies between spatial and channel dimensions. To address this problem, we propose a novel multi-dimensional adaptive interaction transformer network, named as MAITFuse, to enhance the multilevel information expression and detail retention capabilities of images. We design a Multi-Dimensional Feature Extraction (MDFE) module to extract features across spatial and channel dimensions in parallel, and introduce a novel weighted cross-attention fusion method to integrate multi-dimensional information effectively. Experimental results show that, compared to existing fusion methods, our proposed method achieves superior fusion performance across various datasets.
Driven in part of the rapid growth of consortium blockchain applications, blockchain interoperability becomes extremely essential to exchange transactional data among decentralized applications. To ensure the data int...
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ISBN:
(纸本)9783030967727;9783030967710
Driven in part of the rapid growth of consortium blockchain applications, blockchain interoperability becomes extremely essential to exchange transactional data among decentralized applications. To ensure the data integrity of transactions, the state-of-the-art studies of the blockchain interoperability apply data locks, which however severely decrease system efficiency. To boost interoperability performance, this paper proposes a novel approach based on multi-version concurrency control to parallelize interoperable transactions, which aims high transaction processing throughput while ensuring data integrity. The experimental evaluation with the Smallbank benchmark shows that the proposed method achieves up to 4x performance increase (in terms of processed transactions per second, TPS) compared with the existing methods, and moreover, it decreases the average latency with 58%.
The article explores character recognition using convolutional neural networks (CNNs) optimized with the CUDA platform to enhance computational efficiency. It outlines the CNN architecture, methods for leveraging GPU-...
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ISBN:
(数字)9798331531836
ISBN:
(纸本)9798331531843
The article explores character recognition using convolutional neural networks (CNNs) optimized with the CUDA platform to enhance computational efficiency. It outlines the CNN architecture, methods for leveraging GPU-based parallel data processing, and presents experimental results derived from the MNIST dataset. The study highlights that implementing CUDA drastically reduces processing time while maintaining a high level of predictive accuracy. The findings emphasize the potential of GPU acceleration in handling intensive computational tasks, making it a promising approach for real-time applications in image recognition and machine learning.
High inference times of machine learning-based axon tracing algorithms pose a significant challenge to the practical analysis and interpretation of large-scale brain imagery. This paper explores a distributed data pip...
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ISBN:
(数字)9781665497862
ISBN:
(纸本)9781665497862
High inference times of machine learning-based axon tracing algorithms pose a significant challenge to the practical analysis and interpretation of large-scale brain imagery. This paper explores a distributed data pipeline that employs a SLURM-based job array to run multiple machine learning algorithm predictions simultaneously. image volumes were split into N (1-16) equal chunks that are each handled by a unique compute node and stitched back together into a single 3D prediction. Preliminary results comparing the inference speed of 1 versus 16 node job arrays demonstrated a 90.95% decrease in compute time for 32 GB input volume and 88.41% for 4 GB input volume. The general pipeline may serve as a baseline for future improved implementations on larger input volumes which can be tuned to various application domains.
image semantic segmentation is an important research direction in imageprocessing, computer vision and deep learning. Semantic segmentation is to classify the image pixel by pixel, so that the original image is divid...
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Brain tumours are among the most life-threatening diseases, and automatic segmentation of brain tumours from medical images is crucial for clinicians to identify and quantify tumour regions with high precision. While ...
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ISBN:
(数字)9798350352894
ISBN:
(纸本)9798350352900
Brain tumours are among the most life-threatening diseases, and automatic segmentation of brain tumours from medical images is crucial for clinicians to identify and quantify tumour regions with high precision. While traditional segmentation models have laid the groundwork, diffusion models have since been developed to better manage complex medical data. However, diffusion models often face challenges related to insufficient parallel computing power and inefficient GPU utilization. To address these issues, we propose the DF-SegDiff model, which includes diffusion segmentation, parallel data processing, a distributed training model, a dynamic balancing parameter and model fusion. This approach significantly reduces training time while achieving an average Dice score of 0.87, with several samples reaching Dice values close to 0.94. By combining BRATS2020 with the Medical Segmentation Decathlon dataset, we also integrated a comprehensive dataset containing 800 training samples and 53 test samples. Evaluation of the model using Dice, IoU, and other relevant metrics demonstrates that our method outperforms current state-of-the-art techniques.
In the age of big data, the volume of RDF data has been exploding due to the growing demands for open data, including Linked Open Data (LOD), semantic data processing, and knowledge graphs. Large-scale RDF data may co...
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During the past 10 years, there has been a surging interest in developing distributed graph processing systems. This tutorial provides a comprehensive review of existing distributed graph processing systems. We firstl...
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ISBN:
(纸本)9789811604782;9789811604799
During the past 10 years, there has been a surging interest in developing distributed graph processing systems. This tutorial provides a comprehensive review of existing distributed graph processing systems. We firstly review the programming models for distributed graph processing and then summarize the common optimization techniques for improving graph execution performance, including graph partitioning methods, communication mechanisms, parallelprocessing models, hardware-specific optimizations, and incremental graph processing. We also present an emerging hot topic, distributed Graph Neural Networks (GNN) frameworks, and review recent progress on this topic.
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