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关键词： 模块溯源   模块能力分解   模块化实施方案  

摘要： 模块化教学以素质为核心、以能力为本位,是应用型人才最有效的培养模式之一。"数据结构与算法"是计算机科学与技术专业最重要的专业基础模块之一。通过探讨"数据结构与算法"模块的模块溯源、模块在专业培养中作用、模块能力分解、模块化实施方案等方面,并从理论教学内容重组、实践教学内容重组和自主学习三个方面深入研究了"数据结构与算法"的模块化实施方案,最后分析了本专业"数据结构与算法"的模块化实施效果和后续改进措施。

关键词： 数据结构   跨校选课   数据分析  

摘要： 该文从《数据结构》课程的性质和内容作了介绍,强调了课程的重要性。以《数据结构》课程为例对于跨校选课实施混合式教学改革,通过实施改革,以数据分析为基础和总结方法,为下一步进行自主校内SPOC混合翻转提供有效的数据支持以及方法借鉴。

关键词： 递归数据结构   算法   数据结构   教学方法  

摘要： 本文从递归数据结构的定义出发,结合单链表递归示意图和结点定义对递归数据结构的递归性加以描述。简述递归数据结构的算法设计要点,以输出单链表结点值为例分析递归数据结构的递归设计过程,并给出实现的程序代码和主函数。

关键词： Name lookup   trie   implementation granularity   named data networking  

摘要： Name lookup is an essential function but a performance bottleneck in both today's and future network architectures. Trie is an excellent candidate data structure and has been widely used for looking up and updating names. However, the granularity of trie-at bit, byte (character), or component level-can dramatically affect the network performance in terms of memory usage and packet-processing speed, which has not yet been studied adequately. To fill this gap, we first show that the choice of trie's granularity for name lookups and updates (i.e., insertions and removals) is not a trivial problem due to the complex performance tradeoffs involved. We also introduce a new tool, called NameGen, which uses a Markov-based name learning model and generates pseudo-real datasets with different tunable name characteristics. We compare different trie granularities based on a collection of datasets and performance metrics, highlight the strengths and weaknesses of each granularity, and draw a conclusion on the choice of granularity. Surprisingly, our experimental evaluation finds that there are only two key rules to choose the proper trie's granularity for any kind of dataset: 1) bit-level trie is the choice when the memory requirement is a real concern and 2) character- and component-level tries are preferred for faster lookups and updates when dealing with names composed of short and long components, respectively.

关键词： 数据结构   高等学校   教材   算法分析  

ISBN: (纸本)9787301155578

摘要： 本书分列几个专题，集中探讨了以自然本真淳朴为美的传统审美观，对现代社会技术时代以工巧为上的审美风尚也有一定批评。文笔优美，在行云流水的叙述当中传达了美学的思辨，合乎丛书之旨。作者希望借此书较前作《曲院风荷》有所推进。

关键词： Specular reflections   Image restoration   Endoscopic images   Graph structures  

摘要： Minimally invasive surgical and diagnostic systems are commonly used in clinical practices. However, the accuracy and robustness of these systems depend heavily on computer based processes such as tracking, detecting or segmenting clinically meaningful regions of interest, which are significantly affected by the inherent specular reflections that appear on the organs' surfaces. Restoration of the acquired data for clinical purposes still presents challenges because of the high texture and color variations across the image. In this work, we propose a novel fully-automated solution for endoscopic image restoration, which we call ReTouchimg. Our approach is designed as a two-step scheme. The first is a detection step that is based on the synergy of a set of color variations and gradient information conditions. For the second step, we introduce an inpainting process which is based on graph data structures for recovering the missing information. We exhaustively evaluate our approach on real endoscopic datasets and compare it against some works from the body of literature. We also demonstrate that our solution deals with complex cases such as strong illumination variation and large affected areas through a careful quantitative evaluation of a range of numerical results. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Mixture model   Mixed feature   Cluster analysis   Comorbidity   Generalised Bernoulli distribution  

摘要： In the present era of "Big Data", data collection involving massive amount of features with a mix of variable types is commonplace. Mixture model-based techniques for statistical cluster analysis of mixed numerical and categorical feature data have their limitations, due to the difficulty in specifying appropriate component-densities when common multivariate distributions become invalid. This problem is particularly apparent in applications where the outcome feature variables are in a categorical form. An example of such an application is the analysis of binary morbidity data in national health survey, where the aims are to quantify heterogeneous comorbidity patterns of health conditions and identify (risk)-features of individuals that explain the heterogeneity. In this paper, we propose an unsupervised mixture regression model of multivariate generalised Bernoulli distributions for cluster analysis on the basis of categorical outcome features and mixed risk features. The proposed method is illustrated using simulated data and two real data sets concerning comorbidity patterns among 20,788 Australians who participated in the 2007-2008 National Health Survey (NHS) and among 470 patients who were recruited in a randomised controlled trial of a health intervention about in-patient detoxification from alcohol, heroin or cocaine in Boston. The method is also readily applicable to cluster more general mixed-feature data via the framework of consensus clustering. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Python   Framework   PRMS   PAWN   Parameter sensitivity  

摘要： A persistent problem in numerical hydrologic modeling, is tracking provenance or how particular data came to be. With multiple modules available for individual flux parameterizations and over 100 parameters, the Precipitation-Runoff Modeling System (PRMS) is a perfect example of why it is such a challenge to track the history of input and output of complex models. We present a lightweight, object-oriented Python framework with programmatic tools for management and visualization using PRMS as an example platform. Within this framework, a modeler can write intuitive code for a myriad of basic or advanced applications. The framework also includes methods that, for example, apply systematic or stochastic parameter modifications while simultaneously saving metadata on which parameters were varied and with what improvement in performance. We include a case study that uses built in Monte Carlo parameter resampling for global sensitivity analysis of eight PRMS parameters related to estimation of shortwave solar radiation.

关键词： 数据结构   算法分析  

ISBN: (纸本)9787115509000

摘要： 本书主要内容包括：为什么要了解数据结构与算法，大O表示法及其代码优化利用，栈、队列等的合理使用等。

关键词： LiDAR   web-visualization   efficient data structures   multi-resolution   out-of-core  

摘要： In recent years, the evolution and improvement of LiDAR (Light Detection and Ranging) hardware has increased the quality and quantity of the gathered data, making the storage, processing and management thereof particularly challenging. In this work we present a novel, multi-resolution, out-of-core technique, used for web-based visualization and implemented through a non-redundant, data point organization method, which we call Hierarchically Layered Tiles (HLT), and a tree-like structure called Tile Grid Partitioning Tree (TGPT). The design of these elements is mainly focused on attaining very low levels of memory consumption, disk storage usage and network traffic on both, client and server-side, while delivering high-performance interactive visualization of massive LiDAR point clouds (up to 28 billion points) on multiplatform environments (mobile devices or desktop computers). HLT and TGPT were incorporated and tested in ViLMA (Visualization for LiDAR data using a Multi-resolution Approach), our own web-based visualization software specially designed to work with massive LiDAR point clouds.