In the era of Big Data, Information can be generated, extracted, and utilized in diverse ways. In business, information about business capabilities can be a crucial aspect in understanding the strengths and competenci...
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Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existing CLIP-based methods in training-fre...
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The brain is probably the most complex organ in the human body. To understand processes such as learning or healing after brain lesions, we need suitable tools for brain simulations. The Model of Structural Plasticity...
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ISBN:
(纸本)9783031125973;9783031125966
The brain is probably the most complex organ in the human body. To understand processes such as learning or healing after brain lesions, we need suitable tools for brain simulations. The Model of Structural Plasticity offers a solution to that problem. It provides a way to model the brain bottom-up by specifying the behavior of the neurons and using structural plasticity to form the synapses. However, its original formulation involves a pairwise evaluation of attraction kernels, which drastically limits scalability. While this complexity has recently been decreased to O(***(2)n) after reformulating the task as a variant of an nbody problem and solving it using an adapted version of the Barnes-Hut approximation, we propose an even faster approximation based on the fast multipole method (FMM). The fast multipole method was initially introduced to solve pairwise interactions in linear time. Our adaptation achieves this time complexity, and it is also faster in practice than the previous approximation.
Automatic code summarization refers to generating concise natural language descriptions for code snippets. It is vital for improving the efficiency of program understanding among software developers and maintainers. D...
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Automatic code summarization refers to generating concise natural language descriptions for code snippets. It is vital for improving the efficiency of program understanding among software developers and maintainers. Despite the impressive strides made by deep learning-based methods, limitations still exist in their ability to understand and model semantic information due to the unique nature of programming languages. We propose two methods to boost code summarization models: context-based abbreviation expansion and unigram language model-based subword segmentation. We use heuristics to expand abbreviations within identifiers, reducing semantic ambiguity and improving the language alignment of code summarization models. Furthermore, we leverage subword segmentation to tokenize code into finer subword sequences, providing more semantic information during training and inference, thereby enhancing program understanding. These methods are model-agnostic and can be readily integrated into existing automatic code summarization approaches. Experiments conducted on two widely used Java code summarization datasets demonstrated the effectiveness of our approach. Specifically, by fusing original and modified code representations into the Transformer model, our Semantic Enhanced Transformer for Code Summarizsation (SETCS) serves as a robust semantic-level baseline. By simply modifying the datasets, our methods achieved performance improvements of up to 7.3%, 10.0%, 6.7%, and 3.2% for representative code summarization models in terms of BLEU-4, METEOR, ROUGE-L and SIDE, respectively.
Current vulnerability detection methods based on deep learning and program slicing techniques are widely used, but the program representations and slicing strategies they employ are not well-suited for this purpose. T...
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Graph Convolutional Networks (GCNs) have been widely applied in fields such as social network analysis and recommendation systems. Recently, deep GCNs have emerged, enabling the exploration of deeper hidden informatio...
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ISBN:
(数字)9783982674100
ISBN:
(纸本)9798331534646
Graph Convolutional Networks (GCNs) have been widely applied in fields such as social network analysis and recommendation systems. Recently, deep GCNs have emerged, enabling the exploration of deeper hidden information. Compared to traditional shallow GCNs, deep GCNs feature significantly more layers, leading to considerable computational and data movement challenges. Processing-In-Memory (PIM) offers a promising solution for efficiently handling GCNs by enabling near-data computation, thus reducing data transfer between processing units and memory. However, previous work mainly focused on shallow GCNs and has shown limited performance with deep GCNs. In this paper, we present Dancer, an innovative PIM-based GCN accelerator. Dancer optimizes data movement during the inference process, significantly improving efficiency and reducing energy consumption. Specifically, we introduce a novel compressed graph storage architecture and a dynamic quantization technique to minimize data transfers at each layer of the GCN. Additionally, through a detailed analysis of weight dynamics changes, we propose a sparsity propagation strategy to further alleviate the computational and data transfer burden between layers. Experimental results demonstrate that, compared to current state-of-the-art methods, Dancer achieves 3.7× speedup, 7.6× energy efficiency, and reduces of 9.6× DRAM access on average.
The reconstruction of high-fidelity flow fields from low-fidelity data has attracted considerable attention in fluid dynamics but poses many challenges to existing deep learning methods due to the spatiotemporal compl...
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The reconstruction of high-fidelity flow fields from low-fidelity data has attracted considerable attention in fluid dynamics but poses many challenges to existing deep learning methods due to the spatiotemporal complexity of flows and the lack of standardized benchmark datasets. In this study, we generate a low- and high-fidelity dataset containing 25 600 snapshots of four representative flow dynamics simulations using eight different numerical-precision and grid-resolution configurations. Using this dataset, we develop a physics-guided transformer-based generative adversarial network (PgTransGAN) for concurrently handling numerical-precision and grid-resolution enhancement. PgTransGAN leverages a dual-discriminator-based generative adversarial network for capturing continuous spatial and temporal dynamics of flows and applies a soft-constraint approach to enforce physical consistency in the reconstructed data using gradient information. An efficient transformer model is also developed to obtain the long-term temporal dependencies and further alleviate storage constraints. We compare the performance of PgTransGAN against standard linear interpolation and solutions based solely on convolutional neural networks or generative adversarial networks, and demonstrate that our method achieves better reconstruction quality at the data, image, and physics levels with an upscaling factor of 4 or even 8 in each grid dimension.
With the rapid advancement of wireless networks, edge computing has emerged as a promising paradigm for providing computing services to nearby latency-sensitive applications. Toward this trend, resource trading market...
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Compilation optimization bugs are prevalent and can significantly affect the correctness of software products, posing serious challenges to software development. Identifying and localizing these bugs are critical task...
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Compilation optimization bugs are prevalent and can significantly affect the correctness of software products, posing serious challenges to software development. Identifying and localizing these bugs are critical tasks for compiler developers. However, the intricate nature and extensive scale of modern compilers make it difficult to pinpointing the root causes of such bugs. Previous research has introduced innovative techniques that generate witness test programs–tests that pass–by mutating bug-triggering test cases, highlighting the importance of this problem and demonstrating the effectiveness of such approaches. Nevertheless, existing techniques based on witness test programs generation suffer from inherent limitations. Specifically, they do not guarantee the successful creation of witness test programs via mutation and are often time-consuming, typically requiring extensive iterations to produce a valid witness test program. In this study, we present Odfl, a simple yet effective approach for automatically isolating compiler optimization faults by introducing the concept of differentiated compilation configurations. The core insight behind Odfl is that modifying compilation settings such as disabling fine-grained compilation flags in GCC or reducing the number of fine-grained compilation passes in LLVM, can suppress the manifestation of compiler bugs triggered by the same test program. Through adjusting these settings, Odfl creates differentiated compilation configuration that produce multiple compiler executions with distinct pass/-fail outcomes. We utilize these differentiated configurations to collect both passing and failing compiler coverage, and then apply Spectrum-Based Fault Localization (SBFL) techniques to rank compiler source files based on their suspiciousness. Our evaluation of 60 GCC and 50 LLVM compiler bugs demonstrates that Odfl substantially outperforms state-of-the-art compiler fault localization techniques in terms of both effectiveness and ef
The emergence of the Industrial Internet of Things (IIoT) can transform and improve industrial domain processes. This is achieved by IIoT’s ability to collect and process vast amounts of data using technology such as...
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