This paper describes the core of an spontaneous service provision framework offering general functionality based on objects in Internet of Things. The framework supports sensor, wireless network and end system based o...
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Surgical video workflow analysis has made intensive development in computer-assisted surgery by combining deep learning models, aiming to enhance surgical scene analysis and decision-making. However, previous research...
Surgical video workflow analysis has made intensive development in computer-assisted surgery by combining deep learning models, aiming to enhance surgical scene analysis and decision-making. However, previous research has primarily focused on coarse-grained analysis of surgical videos, e.g., phase recognition, instrument recognition, and triplet recognition that only considers relationships within surgical triplets. In order to provide a more comprehensive fine-grained analysis of surgical videos, this work focuses on accurately identifying triplets from surgical videos. Specifically, we propose a vision-language deep learning framework that incorporates intra- and inter- triplet modeling, termed I2TM, to explore the relationships among triplets and leverage the model understanding of the entire surgical process, thereby enhancing the accuracy and robustness of recognition. Besides, we also develop a new surgical triplet semantic enhancer (TSE) to establish semantic relationships, both intra- and inter-triplets, across visual and textual modalities. Extensive experimental results on surgical video benchmark datasets demonstrate that our approach can capture finer semantics, achieve effective surgical video understanding and analysis, with potential for widespread medical applications.
This paper proposes a six-layer conceptual architecture for spontaneous service provision. In the architecture, techniques of context-aware, semantic reasoning and service composition are smoothly synthesized. A dynam...
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As Grid computing is becoming a reality, there is a need for managing and monitoring the available resources worldwide, as well as the need for conveying these resources to the everyday user. This paper describes a re...
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We discuss issues related to domain decomposition and multilevel preconditioning techniques which are often employed for solving large sparse linear systems in parallel computations. We implement a parallel preconditi...
Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification proble...
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Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion problems, edge editing problems, edge deletion problems, and edge completion problems. For each type of problem, we outline typical examples together with recent results, analyze the main techniques, and provide some suggestions for future research in this field.
Graphics processing units (GPUs) have rapidly emerged as a very significant player in highperformancecomputing. Single instruction multiple thread (SIMT) pipelines are typically used in GPUs to exploit parallelism a...
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This paper presents a general methodology for implementing on clusters the runtime support for a two-level dependence-driven thread model, initially targeted to shared-memory multiprocessors. The general ideal is to e...
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Implementing Multi Sequence Alignment (MSA) problem using the method of progressive alignment is not feasible on common computing systems;it takes several hours or even days for aligning thousands of sequences if we u...
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