Resource-efficient routing and scheduling of time-constrained streaming communication on networks-on-chip

Network-on-chip-based multiprocessor systems-on-chip are considered as future embedded systems platforms. One of the steps in mapping an application onto such a parallel platform involves scheduling the communication on the network-on-chip. This paper presents different scheduling strategies that minimize resource usage by exploiting all scheduling freedom offered by networks-on-chip. It also introduces a technique to take the dynamism in applications into account when scheduling the communication of an application on the network-on-chip while minimizing the resource usage. Our experiments show that resource-utilization is improved when compared to existing techniques.     

Misfit versus latency

In this paper we present a framework that combines some ideas of the behavioral modeling approach and the prediction error modeling approach. It is shown that the proposed model selection procedure can be rephrased as an optimization problem that only depends on the model parameters. Experiments illustrate the potential of the so-called misfit versus latency framework.     

Mining frequent conjunctive queries in relational databases through dependency discovery

We present an approach for mining frequent conjunctive in arbitrary relational databases. Our pattern class is the simple, but appealing subclass of simple conjunctive queries. Our algorithm, called Conqueror $^+$ , is capable of detecting previously unknown functional and inclusion dependencies that hold on the database relations as well as on joins of relations. These newly detected dependencies are then used to prune redundant queries. We propose an efficient database-oriented implementation of our algorithm using SQL and provide several promising experimental results.     

Estimating spine shape in lateral sleep positions using silhouette-derived body shape models

This study aims at evaluating spinal alignment during sleep by combining personalized human models with mattress indentation measurements. A generic surface model has been developed that can be personalized based on anthropometric parameters derived from silhouette extraction. Shape assessment of the personalized surface models, performed by comparison with 3-D surface scans of the trunk, showed a mean unsigned distance of 9.77 mm between modeled and scanned surface meshes. The surface model is combined with an inner skeleton model, allowing the model to simulate distinct sleep postures. An automatic fitting algorithm sets the appropriate degrees of freedom to position the model on the measured indentation according to the adopted sleep posture. Validation on lateral sleep positions showed good intraclass correlations (0.73–0.88) between estimated and measured angular spinal deformations, indicating that silhouette-derived body shape models provide a valuable tool for the unobtrusive assessment of spinal alignment during sleep.Relevance to industry: A common drawback of the available techniques to assess spinal deformation on bedding systems is that they interfere with the actual sleep process. The current study presents a novel method based on silhouette-derived body shape models in order to estimate spine shape during sleep unobtrusively.     

Experiences of Autonomy and Control Among Chinese Learners: Vitalizing or Immobilizing?

Various cross-cultural researchers state that autonomy is not valued in Eastern cultures and, hence, is unlikely to predict optimal study functioning and well-being. In contrast, self-determination theory (SDT; R. M. Ryan & E. L. Deci, 2000) maintains that autonomous or volitional study motivation is universally important and should predict better learning and higher well-being, even among Chinese students. Two studies were conducted to shed light on this controversial issue. Findings from both studies indicated that autonomous study motivation positively predicts adaptive learning attitudes, academic success, and personal well-being, whereas controlled motivation was associated with higher drop-out rates, maladaptive learning attitudes, and ill-being. In addition, Study 2 revealed that parental autonomy support versus psychological control is related to more adaptive learning strategies and higher well-being and that these effects were mediated by students' relative autonomy for studying. The importance of defining autonomy as an intraindividual, phenomenological experience versus an interpersonal, culturally bounded value is discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

催化氧化净化黄磷尾气中的磷和硫

利用黄磷尾气生产甲酸、乙酸以及甲醇等高附加值产品，净化预处理成为制药其应用的瓶颈问题。研究了用普通活性炭和自制催化剂KU2催化氧化净化黄磷尾气的方法；讨论了固定床系统中常压下、温度20~140℃，催化剂上磷和硫化氢的吸附特征。KU2和活性炭都能通过催化氧化过程有效脱除黄磷尾气中的P4，PH3和H2S杂质，随着反应温度和氧含量增加可显著提高净化效果，在最优条件下，经催化氧化净化后的总磷含量＜5mg/m3，硫化氢＜5mg/m3，失效后的催化剂易于再生，催化剂经多次再生后催化性能恢复良好；提出了活性炭固定床稳定     

MICAST — The effect of magnetically controlled fluid flow on microstructure evolution in cast technical Al-alloys

The MICAST research program focuses on a systematic analysis of the effect of convection on the microstructure evolution in cast Al-alloys. The experiments within MICAST are carried out under well defined thermally and magnetically controlled, convective boundary conditions and analyzed using advanced diagnostics and theoretical modeling, involving phase field simulation, micro-modeling and global simulation of heat and mass transport. The MICAST team uses as a model material the Al-Si base alloys. This paper gives a brief overview on recent experimental results of the MICAST team on the effect of rotating magnetic fields on microstructure in AlSi alloys.     

Artificial argument assistants for defeasible argumentation

The present paper discusses experimental argument assistance tools. In contrast with automated reasoning tools, the objective is not to replace reasoning, but to guide the user's production of arguments. Two systems are presented, and based on . The focus is on defeasible argumentation with an eye on the law. Argument assistants for defeasible argumentation naturally correspond to a view of the application of law as dialectical theory construction. The experiments provide insights into the design of argument assistants, and show the pros and cons of different ways of representing argumentative data. The development of the argumentation theories underlying the systems has culminated in the logical system that formalizes the interpretation of prima facie justified assumptions. introduces an innovative use of conditionals expressing support and attack. This allows the expression of warrants for support and attack, making it a transparent and flexible system of defeasible argumentation.     

The Linear Dynamic Complementarity Problem is a special case of the Extended Linear Complementarity Problem

In this paper we consider some extensions of the Linear Complementarity Problem, which is one of the fundamental problems in mathematical programming. More specifically we consider the Linear Dynamic Complementarity Problem (LDCP), the Generalized Linear Complementarity Problem (GLCP) and the Extended Linear Complementarity Problem (ELCP). In this note we show that the LDCP is a special case of the ELCP and the GLCP.     

Benchmarking Least Squares Support Vector Machine Classifiers

In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.