Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

ELEVATED TEMPERATURE FRACTURE OF PARTICULATE-REINFORCED ALUMINUM PART I: FRACTURE TOUGHNESS

Abstract—The plane-strain initiation and growth fracture toughnesses of powder-metallurgy-processed, SiC particulate-reinforced 2009 plate were measured at temperatures from 25°C to 316°C. Initiation toughness from electrical potential monitoring ( K _JICi) is 18 MPa°m at 25°C, and is nearly constant to 220°C before decreasing sharply to 6 MPa°m at 316°C. Growth toughness, given by the tearing modulus ( T _R), is less than 3 from 25°C to 125°C, and increases dramatically above 200°C. The magnitude and temperature dependence of initiation toughness depend on detection of the critical fracture event. Standard measures of toughness K _IC and K _JIC exceed K _JICi and increase to a plateau with increasing temperature. The fracture mode for the composite is microvoid nucleation, growth and coalescence at all temperatures. Void nucleation is associated with SiC; such particles both crack and create stress and plastic strain concentrations that rupture the interface or adjacent matrix, particularly at corners. Matrix plasticity and cavitation increase with increasing temperature. Void growth is regular at all temperatures, but limited by adjacent SiC particles. Both K _JICi and T _R are governed by the temperature-dependent crack-tip plastic stress and strain fields, and the intrinsic damage resistance of the composite microstructure.     

N-Methyl-D-aspartate attenuates opioid receptor-mediated G protein activation and this process involves protein kinase C

The effects of N-methyl-D-aspartate (NMDA) on opioid receptor-mediated G protein activation were explored in neuroblastoma X glioma hybrid (NG108-15) cells. Treatment of the cells with NMDA resulted in a remarkable attenuation of [35S]guanosine-5'-O-(3-thio)triphosphate binding stimulated by [D-Pen2,D-Pen5]-enkephalin (DPDPE), a delta-opioid receptor agonist. The effects of NMDA were dose and time dependent with an IC50 value of 5 nM and could be blocked by NMDA receptor antagonists. After NMDA treatment, the DPDPE dose-response curve shifted to the right (EC50 value increased approximately 7-fold, from 6 to 40 nM), and the maximal response induced by DPDPE was reduced by approximately 60%. The effects of NMDA were reversible, and the DPDPE response could recover within 60 min. The functional responses of delta-, mu-, and kappa-opioid receptors in primarily cultured neurons also were attenuated significantly by NMDA treatment. The inhibitory effects of NMDA on opioid receptor-mediated G protein activation could be blocked by coadministration of the protein kinase C (PKC) inhibitors or by elimination of the extracellular Ca2+. Correspondingly, NMDA treatment of NG108 cells significantly elevated cellular PKC activity and stimulated Gialpha2 phosphorylation. Transient transfection into NG108-15 cells of the wild-type Gialpha2 and a mutated Gialpha2 (Ser144Ala) resulted in a 2-fold increase in DPDPE-stimulated G protein activation. The DPDPE responses were greatly inhibited by NMDA treatment in the wild-type Gialpha2-transfected cells but much less affected in the mutant Gialpha2-transfected cells. In summary, NMDA attenuates opioid receptor/G protein coupling, and this process requires activation of PKC.     

A variational method for contour tracking via covariance matching

This paper presents a novel formulation for contour tracking.We model the second-order statistics of image regions and perform covariance matching under the variational level set framework.Specifically,covariance matrix is adopted as a visual object representation for partial differential equation(PDE) based contour tracking.Log-Euclidean calculus is used as a covariance distance metric instead of Euclidean distance which is unsuitable for measuring the similarities between covariance matrices,because the matrices typically lie on a non-Euclidean manifold.A novel image energy functional is formulated by minimizing the distance metric between the candidate object region and a given template,and maximizing the one between the background region and the template.The corresponding gradient flow is then derived according to a variational approach,enabling partial differential equations(PDEs) based contour tracking.Experiments on several challenging sequences prove the validity of the proposed method.     

城市水土保持区划及功能性评价研究——以宜昌市为例

目前全国地级市水土保持区划工作滞后,宜昌市作为湖北省域副中心城市,城市水土流失严重。为突出地方特色、因地制宜地指导地方水土保持区划工作的落地实施,以宜昌市为例,在国家、省级水土保持区划成果的基础上,紧密结合宜昌市地方特点,逐步进行细化,形成宜昌市水土保持四级区划,并对区划成果进行基本功能分析评价。 宜昌市水土保持区划使用四级分区体系,前三级区划沿用国家水土保持区划中一级、二级、三级分区范围;四级区被分为6个水土保持片区:沿江平原丘陵农田防护区、香溪河山地生态维护区、三峡库区山地丘陵保土区、沮漳河山地丘陵保土区、清江山地水源涵养保土区以及中心城区人居环境维护区。全市共涉及5个水土保持基本功能,分别为农田防护、生态维护、土壤保持、水源涵养和人居环境维护。地级市水土保持区划分区既要在国家、省级水土保持区划基础上进行,同时还需突出地方特点,因地制宜。宜昌市水土流失治理应坚持预防为主,保护水源,并加强水土保持重点监管。     

湖库疏浚技术及淤积物资源化利用技术进展

文章介绍了我国部分河湖水库的淤积现状,评述了当前国内外对河湖疏浚设备和淤泥资源化利用的几种主要途径,包括物理利用、生物处理以及化学处理方法等,同时对新型的干馏法进行了介绍,指出了未来淤泥资源化利用的主要发展方向.     

众筹项目推荐:面向隐式反馈的深度学习协同过滤

目前,在推荐系统研究中,用户的隐式反馈,以及极度稀疏的数据,已成为影响协同过滤推荐效果的主要问题.针对这一现象,本文提出了深度学习协同过滤算法,先利用卷积神经网络,对用户-项目矩阵的隐层特征进行学习,再结合协同过滤,对用户-项目的交互信息进行建模,并将两种特征融合预测推荐列表.以众筹平台的数据为实验对象,比较模型中各参数对推荐效果的影响,并设计与基线方法的对比实验.实验结果表明:均匀采集负反馈,并在一定卷积层数的网络中,数据稀疏度越高,效果越好;对比基线方法,本文提出的算法在公开数据集(Yahoo!Movie)上取得了最好的推荐结果.本文提出的算法有助于提高众筹平台的融资成功率,同时也丰富了推荐系统的研究体系.     

Bead-based polymerase chain reaction on a microchip

We present a bead-based approach to microfluidic polymerase chain reaction (PCR), enabling fluorescent detection and sample conditioning in a single microchamber. Bead-based PCR, while not extensively investigated in microchip format, has been used in a variety of bioanalytical applications in recent years. We leverage the ability of bead-based PCR to accumulate fluorescent labels following DNA amplification to explore a novel DNA detection scheme on a microchip. The microchip uses an integrated microheater and temperature sensor for rapid control of thermal cycling temperatures, while the sample is held in a microchamber fabricated from (poly)dimethylsiloxane and coated with Parylene. The effects of key bead-based PCR parameters, including annealing temperature and concentration of microbeads in the reaction mixture, are studied to achieve optimized device sensitivity and detection time. The device is capable of detecting a synthetically prepared section of the Bordetella pertussis genome in as few as 10 temperature cycles with times as short as 15?min. We then demonstrate the use of the procedure in an integrated device; capturing, amplifying, detecting, and purifying template DNA in a single microfluidic chamber. These results show that this method is an effective method of DNA detection which is easily integrated in a microfluidic device to perform additional steps such as sample pre-conditioning.     

基于光通信的数据单向导入系统的设计与实现

随着信息网络的日趋成熟,网络信息安全尤其是内网信息安全,越来越受到人们的关注,成为一时研究的热点。其中如何防止在移动存储设备与内网计算机信息交流过程中内网信息流失的问题,显得尤为迫切。为此,本文提出一种基于光通信的数据单向导入系统,为用户提供一种安全地将信息或者数据单向导入到内网的解决方案。利用光的单向性,从传输通道上卡断了反向的可能,有效地保护内网的机密信息不会流失到外部,同时做到良好的人机交互,用户可以任意选择存储设备中的文件发送。