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关键词： Anomaly detection   Support vector data description   Deep learning   Autoencoder  

摘要： A B S T R A C T Support vector data description (SVDD) is a classical anomaly detection algorithm. How to develop a deep version of SVDD is one valuable problem in the anomaly detection field. Aiming at this problem, an improved SVDD model called deep structure preservation SVDD (DSPSVDD) is proposed by integrating the deep feature extraction with the data structure preservation. Firstly, the typical SVDD methods are revisited in view of model depth profiles and the limitations of the present deep SVDD model are analyzed. Then in order to extract the deep data features more effectively, an enhanced comprehensive optimization objective is designed for the deep SVDD model by considering both the hypersphere volume minimization and the network reconstruction error minimization simultaneously. The experimental results on the MNIST, Fashion-MNIST, and MVTec AD image benchmark datasets show that the proposed DSPSVDD method achieves the better anomaly detection performance compared with the traditional deep SVDD method. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Non-volatile memory   Concurrent data structures   Refinement   Linearizability  

摘要： Non-volatile memory (NVM), aka persistent memory, is a new memory paradigm that preserves its contents even after power loss. The expected ubiquity of NVM has stimulated interest in the design of persistent concurrent data structures, together with associated notions of correctness. In this paper, we present a formal proof technique for durable linearizability, which is a correctness criterion that extends linearizability to handle crashes and recovery in the context *** proofs are based on refinement of Input/Output automata (IOA) representations of concurrent data structures. To this end, we develop a generic procedure for transforming any standard sequential data structure into a durable specification and prove that this transformation is both sound and complete. Since the durable specification only exhibits durably linearizable behaviours, it serves as the abstract specification in our refinement proof. We exemplify our technique on a recently proposed persistentmemory queue that builds on Michael and Scott's lock-free queue. To support the proofs, we describe an automated translation procedure from code to IOA and a thread-local proof technique for verifying correctness of invariants.

关键词： mesh   data structure   GPU   parallel  

摘要： We propose a new static high-performance mesh data structure for triangle surface meshes on the GPU. Our data structure is carefully designed for parallel execution while capturing mesh locality and confining data access, as much as possible, within the GPU's fast "shared memory." We achieve this by subdividing the mesh into patches and representing these patches compactly using a matrix-based representation. Our patching technique is decorated with ribbons, thin mesh strips around patches that eliminate the need to communicate between different computation thread blocks, resulting in consistent high throughput. We call our data structure RXMesh: Ribbon-matriX Mesh. We hide the complexity of our data structure behind a flexible but powerful programming model that helps deliver high performance by inducing load balance even in highly irregular input meshes. We show the efficacy of our programming model on common geometry processing applications-mesh smoothing and filtering, geodesic distance, and vertex normal computation. For evaluation, we benchmark our data structure against well-optimized GPU and (single and multi-core) CPU data structures and show significant speedups.

关键词： 数据结构  

ISBN: (纸本)9787111687429

摘要： 本书针对大学程序设计竞赛和课程教学，基于数据结构的知识体系结构和循序渐进的原则组织内容，包括基本编程能力训练、线性数据结构的编程、树的编程、图的编程。在每一章中，先介绍了相关的数据结构知识后，然后给出相应的范例；在每章的结尾给出相关题库。

关键词： 探究式学习   幂级数   应用研究  

摘要： 本文分析了探究式学习的意义与内涵,介绍了探究式学习在数学教学中的基本步骤,同时提出了探究式学习在数学教学中创设情境和拓展应用时,可以将高等数学与专业的应用进行结合,以此提高学生学习的积极性和参与度,从而提高探究式学习的教学效果.本文以幂级数为例,采用探究式学习的教学方法进行教学.

关键词： Space Adjustment   optimisation   structure Maintenance   classification  

摘要： One of the most important assumptions in machine learning tasks is the fact that training data points and test data points are extracted from the same distribution. However, this paper assumes the situation in which this fact does no longer hold. Therefore, a task named space adjustment, through which the distribution of the data points in the training-data space and the distribution of the data points in the test-data space become identical, is inevitable. Hereby, authors propose a linear mapping for the space adjustment task in the paper. It considers four approaches for preserving localities among data samples during the space adjustment. Each approach is defined based on a different locality concept. Considering all locality concepts in an objective function, authors transform the space adjustment into an optimisation problem. The paper proposes to optimise the corresponding objective function by an iterative approach. Empirical study shows that the proposed method outperforms the baseline methods. To do experiments, authors employ a large number of real-world datasets.

关键词： 线上线下混合实验教学   分层   个性化学习   Educoder  

摘要： 与一般知识型理论课程不同,“数据结构与算法”要求学生在掌握数据结构基础知识的同时,还需具备算法设计、分析与评价的能力。常见的线上教学平台,如中国大学M00C、雨课堂、课堂派等,测试以选择题、填空题等知识答题为主,无法实现程序的自动测评,不能满足本课程的能力培养需求。文章作者选用Educoder平台,围绕课程的能力目标设计了“课前自测一课中同步练一课后能力提升”的分层次实训资源,进行线上线下混合式实验教学,在提升课程挑战度、创新性的同时,也提高了学生算法设计能力、学习主动性,并能满足学生个性化学习需求。