Optimal platform investment for product family design

Existing models for developing modular product families based on a common platform are either too engineering oriented or too marketing centric. In this paper, we propose an intermediate modeling ground that bridges this gap by simultaneously considering essential concepts from engineering and marketing to construct an alternative model for platform-based product families. In this model, each variant (in the platform-based product family) contributes a percentage to overall market coverage inside a target market segment. The extent to which a specific variant contributes to market coverage is linked to its degree of distinctiveness. On the other hand the cost of development of all variants (that constitute the product family) is also dependent on the degree of commonality between these variants. The objective of the model is to maximize market coverage subject to an available development budget. Based on a conceptual design of the product family, the proposed model suggests the optimal initial investment in the platform, the commonality level between variants, and the number of variants to be produced in order to maximize market coverage using both analytical and simulation techniques. An application example using an ice scraper product family is included to demonstrate the proposed model.     

Augmenting satellite precipitation estimation with lightning information

We have used lightning information to augment the precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system (PERSIANN-CCS). Co-located lightning data are used to segregate cloud patches, segmented from Geostationary Operational Environmental Satellite (GOES)-12 infrared (IR) data, into either electrified patches (ECPs) or nonelectrified patches (NECPs). A set of features is extracted separately for the ECPs and NECPs. Features for the ECPs include a new feature corresponding to the number of flashes that occur within a 15 minute window around the time of the nominal scan of the satellite IR images of the cloud patches. The cloud patches are classified and clustered using a self-organizing maps (SOM) neural network. Then, brightness temperature and rain rate (T–R) relationships are derived for different clusters. Rain rates are estimated for the cloud patches based on their representative (T–R) relationship. The equitable threat scores (ETS) of the daily and hourly precipitation estimates at a range of rain rate thresholds show that incorporating lightning information can improve categorical precipitation estimation in the winter and fall seasons. In the winter, the ETS improvement is almost 15% for the daily and 12% for the hourly rainfall estimates (at thresholds below 15 mm hour^?1). During the same period, there is also a drop in the false alarm ratio (FAR) and a corresponding increase in the probability of detection (POD) at most threshold levels. During the summer and spring seasons, no categorical significant improvements have been noted, except for the BIAS scores for the hourly rainfall estimates at higher thresholds (above 5 mm hour^?1) in the summer months. A quantitative evaluation in terms of the root mean squared error (RMSE) and correlation coefficient (CORR) shows that the incorporation of lightning data does improve rainfall estimation over all seasons with the most improvement (around 11–13% CORR improvement) occurring during the winter. We speculate that during the winter, more of the ice processes are packed into a thinner stratiform layer with lower cloud tops and freezing levels. Hence, more of the ice contributes to precipitation on the ground. We also expect that information from lightning, related to the ice microphysics processes, provides surrogate information about the rain rate.     

The effects of mutual location-awareness on group coordination

The importance of space and place in collaborative practices has been strengthened with the ubiquitous computing paradigm, which aims at the integration of computation in physical objects and places. New location-based applications allow users to know where other individuals are in the physical world. New collaborative applications engage users in geographically dispersed and mobile activities. However, there is still a lack of information concerning how mutual location-awareness (i.e. knowing partners’ whereabouts) might influence socio-cognitive processes involved in coordination. To address this issue, we conducted field experiments with a mobile and collaborative game, running on Tablet PCs, and compared two interfaces. On the first interface, groups received automatic updates from teammates’ whereabouts, while this automatic MLA tool was not provided by the second interface. In addition, all users could use their Tablet PCs to communicate by annotating the map. We found no differences between the two conditions with regard to the task performance. However, contrary to our expectations, players without automatic MLA had a better representation of their partners’ paths, wrote more messages and provided more elaborate explanations of their strategies. Additionally, automatic location-awareness undermined the coordination process, leading participants to be less articulate about their strategy. The paper discusses these results and the implications of such results.     

Constrained physical design tuning

Existing solutions to the automated physical design problem in database systems attempt to minimize execution costs of input workloads for a given storage constraint. In this work, we argue that this model is not flexible enough to address several real-world situations. To overcome this limitation, we introduce a constraint language that is simple yet powerful enough to express many important scenarios. We build upon a previously proposed transformation-based framework to incorporate constraints into the search space. We then show experimentally that we are able to handle a rich class of constraints and that our proposed technique scales gracefully. Our approach generalizes previous work that assumes simpler optimization models where configuration size is the only fixed constraint. As a consequence, the process of tuning a workload not only becomes more flexible, but also more complex, and getting the best design in the first attempt becomes difficult. We propose a paradigm shift for physical design tuning, in which sessions are highly interactive, allowing DBAs to quickly try different options, identify problems, and obtain physical designs in an agile manner.     

Simple negotiation schemes for agents with simple preferences: sufficiency,necessity and maximality

We investigate the properties of an abstract negotiation framework where agents autonomously negotiate over allocations of indivisible resources. In this framework, reaching an allocation that is optimal may require very complex multilateral deals. Therefore, we are interested in identifying classes of valuation functions such that any negotiation conducted by means of deals involving only a single resource at a time is bound to converge to an optimal allocation whenever all agents model their preferences using these functions. In the case of negotiation with monetary side payments amongst self-interested but myopic agents, the class of modular valuation functions turns out to be such a class. That is, modularity is a sufficient condition for convergence in this framework. We also show that modularity is not a necessary condition. Indeed, there can be no condition on individual valuation functions that would be both necessary and sufficient in this sense. Evaluating conditions formulated with respect to the whole profile of valuation functions used by the agents in the system would be possible in theory, but turns out to be computationally intractable in practice. Our main result shows that the class of modular functions is maximal in the sense that no strictly larger class of valuation functions would still guarantee an optimal outcome of negotiation, even when we permit more general bilateral deals. We also establish similar results in the context of negotiation without side payments.     

A model-driven traceability framework for software product lines

Software product line (SPL) engineering is a recent approach to software development where a set of software products are derived for a well defined target application domain, from a common set of core assets using analogous means of production (for instance, through Model Driven Engineering). Therefore, such family of products are built from reuse, instead of developed individually from scratch. SPL promise to lower the costs of development, increase the quality of software, give clients more flexibility and reduce time to market. These benefits come with a set of new problems and turn some older problems possibly more complex. One of these problems is traceability management. In the European AMPLE project we are creating a common traceability framework across the various activities of the SPL development. We identified four orthogonal traceability dimensions in SPL development, one of which is an extension of what is often considered as “traceability of variability”. This constitutes one of the two contributions of this paper. The second contribution is the specification of a metamodel for a repository of traceability links in the context of SPL and the implementation of a respective traceability framework. This framework enables fundamental traceability management operations, such as trace import and export, modification, query and visualization. The power of our framework is highlighted with an example scenario.     

Properties of motor units after immobilization of cat peroneus longus muscle

Changes in contractile properties of cat peroneus longus motor units were studied 2, 5, and 8 wk after selective immobilization of this muscle, which was achieved by fixing the distal tendon of the peroneus longus to the fibula either at the muscle minimal physiological length ("short" length) or at the length for a 90 degree ankle joint ("neutral" length). In each muscle, 75-90% of the units [slow (S), fast resistant to fatigue (FR), fast intermediate (FI), and fast fatigable (FF)] were studied. Immobilization elicited a permanent decrease in tetanic force developed by single motor units, which was larger for resistant-to-fatigue units (S, FR). In most instances this decrease was not related to the immobilization length. In all units, twitch contraction and half-relaxation times underwent a transient increase, the extent and time course of which were influenced by immobilization length. The relationship between the frequency of motor units activation and the ratio of unfused to maximal tetanic force was studied. For fast units, there was a transient shift of the relation toward low frequencies after 2 and 5 wk of immobilization at neutral and short length, respectively.     

基于树型结构网格矢量量化的点云渲染算法

针对3D模型海量点云数据存在的空间冗余问题,提出一种基于TSLVQ(tree structure lattice vector quantization)的静态点云有损渲染算法。算法旨在利用层级嵌套网格的集合,解决渲染低效的问题。首先对整个点云进行包围盒封装,多层量化,把数个较小尺度的截断包围盒嵌入到一个较高尺度的截断包围盒单元中,每一步量化过程采用8叉树方法将包围盒分割为八个最佳尺寸的空或非空小包围盒;最后在最高深度的层级里,用包围盒来代替整个小包围盒中全部的点。同时,算法可自行设定8叉树的深度,从而任意控制编码的复杂度和精度,满足渲染的实时性要求。实验结果表明,与现有的网格有损压缩算法相比,提出的算法能在保证模型重建精度的基础上具有较好的空间分解优势,实现实时渲染效果。     

Point set morphological filtering and semantic spatial configuration modeling: Application to microscopic image and bio-structure analysis

High-level spatial relation and configuration modeling issues are gaining momentum in the image analysis and pattern recognition fields. In particular, it is deemed important whenever one needs to mine high-content images or large scale image databases in a more expressive way than a purely statistically one. Continuing previous efforts to incorporate structural analysis by developing specific efficient morphological tools performing on mesh representations like Delaunay triangulations, we propose to formalize spatial relation modeling techniques dedicated to unorganized point sets. We provide an original mesh lattice framework which is more convenient for structural representations of large image data by means of interest point sets and their morphological analysis. The set of designed numerical operators is based on a specific dilation operator that makes it possible to handle concepts like “between” or “left of” over sparse representations of image data such as graphs. Based on this new theoretical framework for reasoning about images, we are able to process high-level queries over large histopathological images, knowing that digitized histopathology is a new challenge in the field of bio-imaging due to the high-content nature and large size of these images.