Fault handling in large water networks with online dictionary learning

Fault detection and isolation in water distribution networks is an active topic due to the nonlinearities of flow propagation and recent increases in data availability due to sensor deployment. Here, we propose an efficient two-step data driven alternative: first, we perform sensor placement taking the network topology into account; second, we use incoming sensor data to build a network model through online dictionary learning. Online learning is fast and allows tackling large networks as it processes small batches of signals at a time. This brings the benefit of continuous integration of new data into the existing network model, either in the beginning for training or in production when new data samples are gathered. The proposed algorithms show good performance in our simulations on both small and large-scale networks.     

一种基于小字典不对等语料的跨语言词嵌入方法

双语词嵌入通常采用从源语言空间到目标语言空间映射,通过源语言映射嵌入到目标语言空间的最小距离线性变换实现跨语言词嵌入。然而大型的平行语料难以获得,词嵌入的准确率难以提高。针对语料数量不对等、双语语料稀缺情况下的跨语言词嵌入问题,该文提出一种基于小字典不对等语料的跨语言词嵌入方法,首先对单语词向量进行归一化,对小字典词对正交最优线性变换求得梯度下降初始值,然后通过对大型源语言(英语)语料进行聚类,借助小字典找到与每一聚类簇相对应的源语言词,取聚类得到的每一簇词向量均值和源语言与目标语言对应的词向量均值,建立新的双语词向量对应关系,将新建立的双语词向量扩展到小字典中,使得小字典得以泛化和扩展。最后,利用泛化扩展后的字典对跨语言词嵌入映射模型进行梯度下降求得最优值。在英语—意大利语、德语和芬兰语上进行了实验验证,实验结果证明该文方法可以在跨语言词嵌入中减少梯度下降迭代次数,减少训练时间,同时在跨语言词嵌入上表现出较好的正确率。     

Object-oriented DBMS requirements

The requirements on an object-oriented DBMS for management of information in a large, complex enterprise are presented. These requirements aid in the achievement of an environment characterized by data sharing, open architectures, application and data portability, and assurance of data integrity. They were defined from the point of view of a user of the DBMS; therefore they describe the expected functionality of the DBMS and do not specify the method of implementation to achieve this functionality. They encompass requirements on the data model, query and data manipulation languages, the system architecure, interfaces to the system, change management, and transaction management.     

Amphetamine-like discrimination stimulus effects of ephedrine and its stereoisomers in pigeons.

This study assessed the discriminative stimulus effects of (±)-ephedrine and its stereoisomers in pigeons discriminating 1.0 mg/kg of amphetamine from saline. Amphetamine, (±)-, (-)-, and (+)-ephedrine, and cocaine occasioned greater than 80% drug-key responding with the following rank order of potency: amphetamine > cocaine > (-)-ephedrine ≥ (±)-ephedrine ≥ (+)-ephedrine. Neither the α-adrenergic antagonist, phentolamine, nor the β-adrenergic antagonist, propranolol, antagonized the effects of amphetamine or (±)-ephedrine. In contrast, the dopamine receptor antagonist, haloperidol, antagonized the discriminative stimulus effects of amphetamine and (±)-ephedrine as well as those of (-)- and (+)-ephedrine. These results indicate that, like cocaine, (±)-ephedrine and its stereoisomers share discriminative stimulus effects with amphetamine. Moreover, these effects appear to be the result of increased activity in dopaminergic systems. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

High‐error approximate dictionary search using estimate hash comparisons

A method for finding all matches in a pre‐processed dictionary for a query string q and with at most k differences is presented. A very fast constant‐time estimate using hashes is presented. A tree structure is used to minimize the number of estimates made. Practical tests are performed, showing that the estimate can filter out 99% of the full comparisons for 40% error rates and dictionaries of up to four million words. The tree is found to be efficient up to a 50% error rate. Copyright © 2006 John Wiley & Sons, Ltd.     

灵活运用数据库设计理论

简介了关系模型、键、完整性规则、E-R模型、函数依赖、模式分解和关系模式的范式等数据库技术基础和规范化设计理论，从键和完整性规则的实现、数据冗余的特殊情况和字典设计等方面论述了在实际设计工作中如何灵活运用关系数据库设计理论。     

Unseen users, unknown systems: Computer design for a scholar's dictionary

The Dictionary of Old English computing systems have provided access since the 1970s to a database of approximately three million running words. These systems, designed for a variety of machines and written in a variety of languages, have until recently been planned with computing center billing algorithms in mind. With personal workstations emphasis has shifted to building more elegant user interfaces and to providing the entire DOE database to editors around the world. While the shift from sequential files to random access files and the provision of extensive development tools have changed some of the design process, error checking and protection of the database against accidental intrusion have remained as central issues.Richard L. Venezky is Unidel Professor of Educational Studies and professor of Computer and Information Sciences at the University of Delaware. He was formerly professor and chair of Computer Sciences at the University of Wisconsin. His research interests include writing systems, literacy, knowledge representation, and computer-assisted instruction. Among his recent publications are The Subtle Danger: Reflections on the Literacy Abilities of America's Young Adults (Princeton, NJ: ETS, 1987), and Steps Towards a Modern History of American Reading Instruction (Review of Research in Education, 1986, vol. 13, 129–70).     

无理数字典码的测试数据压缩方案

提出了一种无理数字典码的测试数据压缩方案,能有效地压缩芯片测试数据。方案利用无理数序列建立字典,编码时只需进行相容性检查,无需填充无关位,简化了操作步骤;同时,选择局部压缩率最大的一组数据作为最终编码结果,保证压缩效果可以达到局部最优。对ISCAS 89标准电路Mintest集的实验结果显示,该方案的压缩效果优于传统压缩方法,如Golomb码、FDR码、混合定变长码等。     

基于稀疏表征的话者识别

近年来,随着信号的稀疏性理论越来越受到人们的关注,稀疏表征分类器也作为一种新型的分类算法被应用到话者识别系统中。该模型的基本思想是:只要超完备字典足够大,任意待测样本都能够用超完备字典进行线性表示。基于信号的稀疏性理论,未知话者的向量系数,即稀疏解可以通过L1范数最小化获取。超完备字典则可视为语音特征向量在高斯混合模型-通用背景模型(GMM-UBM)上进行MAP自适应而得到的大型数据库。采用稀疏表征模型作为话者辨认的分类方法,基于TIMIT语料库的实验结果表明,所采用的话者辨认方法,能够大大提高说话人识别系统的性能。     

稀疏卷积非负矩阵分解的语音增强算法

非平稳噪声和低信噪比条件下提高增强语音质量一直以来都是语音增强研究的难题。近年来,卷积非负矩阵分解在语音增强算法中成功应用,本文进一步考虑语音信号在时频域的稀疏性,提出了稀疏卷积非负矩阵分解(Sparse Convolutive Nonnegative Matrix Factorization, SCNMF)的语音增强算法。该算法包括训练和增强两个阶段。训练阶段通过SCNMF算法分别对纯净语音和噪声的频谱进行训练,得到纯净语音和噪声字典,并将其作为增强阶段的先验信息。增强阶段首先通过SCNMF算法对带噪语音的频谱进行分解,然后利用纯净语音和噪声联合字典对语音编码矩阵进行估计,重构增强语音。本文通过实验仿真分析了稀疏因子对增强语音质量的影响。实验结果表明,在非平稳噪声和低信噪比条件下,本文算法增强效果均优于多带谱减、非负矩阵分解、卷积非负矩阵分解等传统的算法。