适应用户需求变化的前摄推荐模型

为了减少前摄推荐对用户当前活动可能产生的干扰,提出一种适应用户接受度变化的前摄推荐模型,感知用户对系统主动的信息推送是否有潜在需求以及需求的波动性,以提高用户体验。使用接受度向量来评估用户对信息推送的需求,应用决策树算法对用户历史上下文进行规则推理,形成用户对上下文推荐的接受度向量,并根据上下文变化和用户反馈来调整推荐接受度向量。试验结果表明,模型能够响应用户对推荐的动态变化,有助于改善用户体验。     

Management of organic matter in the tropics: translating theory into practice

Inputs of organic materials play a central role in the productivity of many tropical farming systems by providing nutrients through decomposition and substrate for synthesis of soil organic matter (SOM). The organic inputs in many tropical farming systems such as crop residues, manures, and natural fallows are currently of low quality and insufficient quantity to maintain soil fertility hence there is need to find alternative or supplementary sources of nutrients. Knowledge gained over the past decade on the role of organic resource quality in influencing soil nutrient availability patterns (Synchrony Principle) and SOM maintenance (SOM Principle) provides a strong scientific basis on which to develop management tools. This scientific information must be linked with farmer knowledge and circumstances to provide a realistic approach to soil fertility and SOM management in the tropics. A decision tree has been developed for testing hypotheses about the resource quality parameters that affect nitrogen release patterns and rates. The decision tree is linked to an Organic Resource Database (ORD) with detailed information on the resource quality of agroforestry trees and leguminous cover crops providing a systematic means of selecting organic resources for soil fertility management. The decision tree has also been translated into a practical field guide for use with farmers in evaluating organic materials. The longer-term effects of organic inputs on SOM might also be addressed through the decision tree and database. It is generally believed that materials good for short-term soil fertility will not build or maintain SOM; if true then it is difficult to imagine practical means of maintaining SOM in the African context where short-term fertility issues will take precedence over longer-term maintenance of SOM. This revised version was published online in June 2006 with corrections to the Cover Date.     

Use of the 15N natural abundance technique to quantify biological nitrogen fixation by woody perennials

Biological nitrogen fixation (BNF) associated with trees and shrubs plays a major role in the functioning of many ecosystems, from natural woodlands to plantations and agroforestry systems, but it is surprisingly difficult to quantify the amounts of N₂ fixed. Some of the problems involved in measuring N₂ fixation by woody perennials include: (a) diversity in occurrence, and large plant-to-plant variation in growth and nodulation status of N₂-fixing species, especially in natural ecosystems; (b) long-term, perennial nature of growth and the seasonal or year-to-year changes in patterns of N assimilation; and (c) logistical limitations of working with mature trees which are generally impossible to harvest in their entirety. The methodology which holds most promise to quantify the contributions of N₂ fixation to trees is the so-called `¹⁵N natural abundance' technique which exploits naturally occurring differences in ¹⁵N composition between plant-available N sources in the soil and that of atmospheric N₂. In this review we discuss probable explanations for the origin of the small differences in ¹⁵N abundance found in different N pools in both natural and man-made ecosystems and utilise previously published information and unpublished data to examine the potential advantages and limitations inherent in the application of the technique to study N₂ fixation by woody perennials. Calculation of the proportion of the plant N derived from atmospheric N₂ (%Ndfa) using the natural abundance procedure requires that both the ¹⁵N natural abundance of the N derived from BNF and that derived from the soil by the target N₂-fixing species be determined. It is then assumed that the ¹⁵N abundance of the N₂-fixing species reflects the relative contributions of the N derived from these two sources. The ¹⁵N abundance of the N derived from BNF (B) can vary with micro-symbiont, plant species/provenance and growth stage, all of which create considerable difficulties for its precise evaluation. If the%Ndfa is large and the ¹⁵N abundance of the N acquired from other sources is not several ¹⁵N units higher or lower than B, then this can be a major source of error. Further difficulties can arise in determining the ¹⁵N abundance of the N derived from soil (and plant litter, etc.) by the target plant as it is usually impossible to predict which, if any, non-N₂-fixing reference species will obtain N from the same N sources in the same proportions with the same temporal and spatial patterns as the N₂-fixing perennial. The compromise solution is to evaluate the ¹⁵N abundance of a diverse range of neighbouring non-N₂-fixing plants and to compare these values with that of the N₂-fixing species and the estimate of B. Only then can it be determined whether the contribution of BNF to the target species can be quantified with any degree of confidence. This review of the literature suggests that while the natural abundance technique appears to provide quantitative measures of BNF in tree plantation and agroforestry systems, particular difficulties may arise which can often limit its application in natural ecosystems.     

Systematic Extraction of Structure–Activity Relationship Information from Biological Screening Data

A data mining approach is introduced that automatically extracts SAR information from high‐throughput screening data sets and that helps to select active compounds for chemical exploration and hit‐to‐lead projects. SAR pathways are systematically identified consisting of sequences of similar active compounds with gradual increases in potency. Fully enumerated SAR pathway sets are subjected to pathway scoring, filtering, and mining, and pathways with the most significant SAR information content are prioritized. High‐scoring SAR pathways often reveal activity cliffs contained in screening data. Subsets of SAR pathways are analyzed in SAR trees that make it possible to identify microenvironments of significant SAR discontinuity from which hits are preferentially selected. SAR trees of alternative pathways leading to activity cliffs identify key compounds and help to develop chemically intuitive SAR hypotheses.     

Assessment of resistance spot welding quality based on ultrasonic testing and tree-based techniques

Classification and regression tree (CART) and random forest techniques were proposed as pattern recognition tools for classification of ultrasonic oscillograms of resistance spot welding (RSW) joints. The results showed that CART models produced an acceptable error rate with high interpretability. These features may be used to understand and control the decision processes, instruct other human operators, compare margins of safety or modify them depending on the criticality of the industrial process. Compared with CART trees, random forests reduced the error rate at the cost of decreasing decision interpretability. The use of the agreement of the forest was proposed as a measure to reduce the workload of human operators, who would only have to focus on the analysis of ultrasonic oscillograms that are difficult to interpret.     

Weak associativity and restricted rotation

A restricted rotation induced by a weak associative law is introduced. The corresponding equivalence relation is identical to the Glivenko congruence on Tamari lattices, i.e. lattices of binary trees endowed by the well-known rotation operation.     

A GRASP/VND heuristic for the phylogeny problem using a new neighborhood structure

A phylogeny is a tree that relates taxonomic units, based on their similarity over a set of characters. The phylogeny problem consists in finding a phylogeny with the minimum number of evolutionary steps. We propose a new neighborhood structure for the phylogeny problem. A greedy randomized adaptive search procedure heuristic based on this neighborhood structure and using variable neighborhood descent for local search is described. Computational results on randomly generated and benchmark instances are reported, showing that the new heuristic is quite robust and outperforms the other algorithms in the literature in terms of solution quality and time‐to‐target value.     

Robust inference of trees

This paper is concerned with the reliable inference of optimal tree-approximations to the dependency structure of an unknown distribution generating data. The traditional approach to the problem measures the dependency strength between random variables by the index called mutual information. In this paper reliability is achieved by Walley's imprecise Dirichlet model, which generalizes Bayesian learning with Dirichlet priors. Adopting the imprecise Dirichlet model results in posterior interval expectation for mutual information, and in a set of plausible trees consistent with the data. Reliable inference about the actual tree is achieved by focusing on the substructure common to all the plausible trees. We develop an exact algorithm that infers the substructure in time O(m ⁴), m being the number of random variables. The new algorithm is applied to a set of data sampled from a known distribution. The method is shown to reliably infer edges of the actual tree even when the data are very scarce, unlike the traditional approach. Finally, we provide lower and upper credibility limits for mutual information under the imprecise Dirichlet model. These enable the previous developments to be extended to a full inferential method for trees.     

Rational Unification in 28 Characters

We present a case study where Synchronising Graphs, a system of parallel graph transformation, are used to solve the syntactic unification problem for first order rational terms (with possibly infinite unifier). The solution we offer is efficient, that is quasi-linear, and simple: a program of 28 characters.