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arXiv 2023年

作者： Xu, Hongzuo Wang, Yijie Wei, Juhui Jian, Songlei Li, Yizhou Liu, Ning National Key Laboratory of Parallel and Distributed Computing China College of Computer National University of Defense Technology China College of Science National University of Defense Technology China

Due to the unsupervised nature of anomaly detection, the key to fueling deep models is finding supervisory signals. Different from current reconstruction-guided generative models and transformation-based contrastive models, we devise novel data-driven supervision for tabular data by introducing a characteristic – scale – as data labels. By representing varied sub-vectors of data instances, we define scale as the relationship between the dimensionality of original sub-vectors and that of representations. Scales serve as labels attached to transformed representations, thus offering ample labeled data for neural network training. This paper further proposes a scale learning-based anomaly detection method. Supervised by the learning objective of scale distribution alignment, our approach learns the ranking of representations converted from varied subspaces of each data instance. Through this proxy task, our approach models inherent regularities and patterns within data, which well describes data "normality". Abnormal degrees of testing instances are obtained by measuring whether they fit these learned patterns. Extensive experiments show that our approach leads to significant improvement over state-of-the-art generative/contrastive anomaly detection methods. © 2023, CC BY.

关键词： Anomaly detection

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for Highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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parallel algorithms for force directed scheduling of flattened and hierarchical signal flow graphs

Parallel algorithms for force directed scheduling of flatten...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： P. Prabhakaran P. Banerjee Center for Reliable and High-Performance Computing Coordinated Science Laboratory University of Illinois Urbana IL USA Center for Parallel and Distributed Computing Department of Electrical and Computer Engineering Northwestern University Evanston IL USA

In this paper we present some novel algorithms for scheduling hierarchical signal flow graphs in the domain of high-level synthesis. There are several key contributions of this paper. First, we develop a novel extension of the force directed scheduling problem which naturally handles loops and conditionals by coming up with a scheme of scheduling hierarchical signal flow graphs. Second, we develop three new parallel algorithms for the scheduling problem. Third, our parallel algorithms are portable across a wide range of parallel platforms. We report results on a set of high-level synthesis benchmarks on 8-processor SGI Challenge and a network of 4 SUN SPARCstation5 work stations. Finally, while some parallel algorithms for VLSI CAD reported by earlier researchers have reported a loss of qualities of results, our parallel algorithms produce exactly the same results as the sequential algorithms on which they are based.

关键词： parallel algorithms Flow graphs Processor scheduling Concurrent computing Scheduling algorithm Contracts Cost function Broadcasting

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Cloud workload prediction based on workflow execution time discrepancies

arXiv

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arXiv 2018年

作者： Kecskemeti, Gabor Nemeth, Zsolt Kertesz, Attila Ranjan, Rajiv Department of Computer Science Liverpool John Moores University Laboratory of Parallel and Distributed Systems MTA SZTAKI Software Engineering Department University of Szeged School of Computing Newcastle University

Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (∼20%) better workload predictions for the future of simulated clouds than random workload selection. Copyright © 2018, The Authors. All rights reserved.

关键词： Infrastructure as a service (IaaS)

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TMDA: Task-Specific Multi-source Domain Adaptation via Clustering Embedded Adversarial Training

TMDA: Task-Specific Multi-source Domain Adaptation via Clust...

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IEEE International Conference on Data Mining (ICDM)

作者： Haotian Wang Wenjing Yang Zhipeng Lin Yue Yu State Key Laboratory of High Performance Computing (HPCL) National University of Defense Technology China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China Peng Cheng Laboratory Shenzhen China

Beyond classical domain-specific adversarial training, a recently proposed task-specific framework has achieved a great success in single source domain adaptation by utilizing task-specific decision boundaries. However, compared to single-source-single-target setting, multi-source domain adaptation (MDA) shows more powerful capability to handle with most real-life cases. To align target domain with diverse multi-source domains using task-specific decision boundaries, we provide a deep insight of task-specific framework on MDA for the first time. Accordingly, we propose a novel task-specific multi-source domain adaptation method (TMDA) with a clustering embedded adversarial training process. Specifically, the proposed TMDA detects and refines less discriminative target representations through a max-min optimization over two adversarial task-specific classifiers. Moreover, our analysis implies that scattered multi-source representations disturb the adversarial training under the task-specific framework. To tight up the dispersed source representations, we embeds a relationship-based domain clustering into TMDA. Empirical results demonstrate that our TMDA outperforms state-of-the-art methods on toy dataset, sentiment analysis and digit classification.

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parallel algorithms for simultaneous scheduling, binding and floorplanning in high-level synthesis

Parallel algorithms for simultaneous scheduling, binding and...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： P. Prabhakaran P. Banerjee Center for Reliable and High-Performance Computing Coordinated Science Laboratory University of Illinois Urbana-Champaign IL USA Center for Parallel and Distributed Computing Department of Electrical and Computer Engineering Northwestern University Evanston IL USA

With small device features in submicron technologies, interconnection delays play a dominant part in cycle time. Hence, it is important to consider the impact of physical design during high level synthesis. In comparison to a traditional approach which separates high-level synthesis from physical design, an algorithm which is able to make these stages interact very closely, would result in solutions with lower latency and area. However, such an approach could result in increased runtimes. parallel processing is an attractive way of reducing the runtimes. In this paper, two parallel algorithms for simultaneous scheduling, binding and floorplanning algorithm are presented. A detailed hardware model is considered, taking into account multiplexor and register areas and delays. Experimental results are reported on an IBM SP-2 multicomputer, with close to linear speedups for a set of benchmark circuits.

关键词： parallel algorithms High level synthesis Runtime Integrated circuit interconnections Delay effects Algorithm design and analysis parallel processing Scheduling algorithm Hardware Registers

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Automatic protocol based intervention plan analysis in healthcare

Automatic protocol based intervention plan analysis in healt...

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Proceedings of the International Convention MIPRO

作者： Miklos Kozlovszky Levente Kovács Khulan Batbayar Zoltán Garaguly MTA SZTAKI/Laboratory of Parallel and Distributed Computing Budapest Hungary Biotech Knowledge Center/Obuda University Budapest Hungary Physiological Controls Group/Obuda University Budapest Hungary

Evidence and protocol based medicine decreases the complexity and in the same time also standardizes the healing process. Intervention descriptions moderately open for the public, and they differ more or less at every medical service provider. Normally patients are not much familiar about the steps of the intervention process. There is a certain need expressed by patients to view the whole healing process through intervention plans, thus they can prepare themselves in advance to the coming medical interventions. Intervention plan tracking is a game changer for practitioners too, so they can follow the clinical pathway of the patients, and can receive objective feedbacks from various sources about the impact of the services. Resource planning (with time, cost and other important parameters) and resource pre-allocation became feasible tasks in the healthcare sector. The evolution of consensus protocols developed by medical professionals and practitioners requires accurate measurement of the difference between plans and real world scenarios. To support these comparisons we have developed the Intervention Process Analyzer and Explorer software solution. This software solution enables practitioners and healthcare managers to review in an objective way the effectiveness of interventions targeted at health care professionals and aimed at improving the process of care and patient outcomes.

关键词： Protocols Heuristic algorithms Software Electronic medical records Monitoring Hospitals

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Data management for large-scale scientific computations in high performance distributed systems

Data management for large-scale scientific computations in h...

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International Symposium on High Performance distributed computing

作者： A. Choudhary M. Kandemir H. Nagesh J. No X. Shen V. Taylor S. More R. Thakur Center for Parallel and Distributed Computing Department of Electrical and Computer Engineering Northwestern University Evanston IL USA Mathematics and Computer Science Division Argonne National Laboratory Argonne IL USA

With the increasing number of scientific applications manipulating huge amounts of data, effective data management is an increasingly important problem. Unfortunately, so far the solutions to this data management problem either require deep understanding of specific storage architectures and file layouts (as in high-performance file systems) or produce unsatisfactory I/O performance in exchange for ease-of-use and portability (as in relational DBMSs). In this paper we present a new environment which is built around an active meta-data management system (MDMS). The key components of our three-tiered architecture are user application, the MDMS, and a hierarchical storage system (HSS). Our environment overcomes the performance problems of pure database-oriented solutions, while maintaining their advantages in terms of ease-of-use and portability. The high levels of performance are achieved by the MDMS, with the aid of user-specified directives. Our environment supports a simple, easy-to-use yet powerful user interface, leaving the task of choosing appropriate I/O techniques to the MDMS. We discuss the importance of an active MDMS and show how the three components, namely application, the MDMS, and the HSS, fit together. We also report performance numbers from our initial implementation and illustrate that significant improvements are made possible without undue programming effort.

关键词： Large-scale systems distributed computing High performance computing Data visualization Engineering management Concurrent computing Relational databases Art Data analysis Image analysis

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An energy-efficient scheduling algorithm for sporadic real-time tasks in multiprocessor systems

An energy-efficient scheduling algorithm for sporadic real-t...

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13th IEEE International Workshop on FTDCS 2011, the 8th International Conference on ATC 2011, the 8th International Conference on UIC 2011 and the 13th IEEE International Conference on HPCC 2011

作者： Zhang, Dong-Song Chen, Fang-Yuan Li, Hong-Hua Jin, Shi-Yao Guo, De-Ke National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China School of Computing Science Simon Fraser University 8888 University Drive Burnaby BC V5A 1S6 Canada National Laboratory for Information System Engineering School of Information Systems and Management National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9780769545387

As the energy consumption of embedded multiprocessor systems becomes increasingly prominent, the real-time energy-efficient scheduling in multiprocessor systems becomes an urgent problem to reduce the system energy consumption while meeting real-time constraints. For a multiprocessor with independent DVFS and DPM at each processor, this paper proposes an energy-efficient real-time scheduling algorithm named LRE-DVFS-EACH, based on LRE-TL which is an optimal real-time scheduling algorithm for sporadic tasks. LRE-DVFS-EACH utilizes the concept of TL plane and the idea of fluid scheduling to dynamically scale the voltage and frequency of processors at the initial time of each TL plane as well as the release time of a sporadic task in each TL plane. Consequently, LRE-DVFS-EACH can obtain a reasonable tradeoff between the real-time constraints and the energy saving. LRE-DVFS-EACH is also adaptive to the change of workload caused by the dynamic release of sporadic tasks, which can obtain more energy savings. The experimental results show that compared with existing algorithms, LRE-DVFS-EACH can not only guarantee the optimal feasibility of sporadic tasks, but also achieve more energy savings in all cases, especially in the case of high workloads. © 2011 IEEE.

关键词： Real time systems

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Direct method-green's theory: From PDE to BIE in the geometric transformation

Direct method-green's theory: From PDE to BIE in the geometr...

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Li-Na Yang Tao-Shen Li Yuan Yan Tang Jia Xu Jian-Jia Pan Hui-Wu Luo Xian-Wei Zheng Electronics and Information Guangxi University Nanning China Department of Computer and Information Science University of Macau Macau Guangxi Colleges and Universities Key Laboratory of Parallel and Distributed Computing Nanning China

ISBN: (纸本)9781509029181

In this research, we apply the Green's theory for converting the partial differential equation to the boundary integral equation for geometric transformation. Green's theory is designed specifically for integral equation. It is efficient in detecting the singularity point to the geometric transformation that has been verified. Experimental results show that the Green's theory has good performance.

关键词： Mathematical model Integral equations Pattern recognition Image restoration Wavelet analysis Partial differential equations Boundary conditions

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